Full text of "Information storage and neural control; tenth annual scientific meeting of the Houston Neurological Society jointly sponsored by the Dept. of Neurology, Baylor University College of Medicine, Texas Medical Center, Houston, Texas"

See other formats

Marine Biological Laboratory Library

Woods Hole, /Massachusetts

Presented by t.ie i-lBL Associates-1971 Gift

INFORMATION STORAGE AND NEURAL CONTROL

^-^

^^ ^^-1

INFORMATION STORAGE

and

NEURAL CONTROL

Tenth Annual Scientific
Meeting of the Houston
Neurological Society

Jointly Sponsored by the
Department of Neurology
Baylor University
College of Medicine
Texas Medical Center
Houston, Texas

Compiled and Edited by
WILLIAM S. FIELDS, M.D.

Professor and Chairman

Department of Neurology

Baylor University College of Medicine

and

WALTER ABBOTT, Ph.D.

Assistant Professor of Epidemiology
Director, Biomathematics Research Laboratory
Baylor University College of Medicine

CHARLES C THOMAS . PUBLISHER

Springfield • Illinois • U.S.A.

Published and Distributed Throughout the World by

CHARLES C THOMAS • PUBLISHER

Bannerstone House
301-327 East Lawrence Avenue, Springfield, Illinois, U.S.A.

This book is protected by copyright. No
part of it may be reproduced in any manner
without written permission from the publisher.

With THOMAS BOOKS careful attention is given to all details of
manufacturing and design. It is the Publisher's desire to present books
that are satisfactory as to their physical qualities and artistic possibilities
and appropriate for their particular use. THOMAS BOOKS will be true
to those laws of quality that assure a good name and good will.

Printed in the United States of America

CONTRIBUTORS

Gregory Bateson, M.A.: Ethnologist, Veterans Administration
Hospital, Palo Alto, California; Professoi (Visiting) Depart-
ment of Anthropology, Stanford University, Stanford, California.

Mary A. B. Brazier, D.Sc.: National Institutes of Health Career
Professor at the Brain Research Institute, University of Cali-
fornia, Los Angeles, California.

Neil R. Burch, M.D.: Associate Professor of Psychiatry, Baylor
University College of Medicine, Houston, Texas.

Harold E. Childers: Assistant Professor of Biophysics, Baylor Uni-
versity College of Medicine, Houston, Texas.

James E. Darnell, Jr., M.D.: Department of Biology, Division of
Microbiology, Massachusetts Institute of Technology, Cam-
bridge, Massachusetts.

Harrison Echols, Ph.D.: Assistant Professor, Department of Bio-
chemistry, College of Agriculture, The University of Wisconsin,
Madison, Wisconsin.

Ralph W. Gerard, M.D., Ph.D.: Director of Laboratories, Mental
Health Research Institute, The University of Michigan, Ann
Arbor, Michigan

Robert T. Gregory, Ph.D.: Associate Professor of Mathematics;
Senior Research Mathematician, Computation Center, The
University of Texas, Austin, Texas.

E. Roy John, Ph.D.: Professor and Director, Center for Brain
Research, The University of Rochester, College of Arts and
Science, Rochester, New York.

Saul Kit, Ph.D.: Biochemist, Department of Biochemistry; Head,
Section of Nucleoprotein Metabolism, The University of Texas
M. D. Anderson Hospital and Tumor Institute, Houston, Texas.

Robert K. Lindsay, Ph.D.: Assistant Professor of Psychology;
Research Scientist, Computation Center, The University of
Texas, Austin, Texas.

vi Contributors

Warren S. McCulloch, M.D.: Head, Neurophysiology Group,
Division of Sponsored Research, Research Laboratory of Elec-
tronics, Massachusetts Institute of Technology, Cambridge,
Massachusetts.

James G. Miller, M.D.: Director, Mental Health Research In-
stitute, The University of Michigan, Ann Arbor, Michigan.

Frank Morrell, M.D.: Professor of Neurology, Stanford University
School of Medicine, Palo Alto, California.

Bernard C. Patten, Ph.D.: Associate Professor of Marine Science,
Virginia Institute of Marine Science, College of William and
Mary, Gloucester Point, Virginia.

Bernard Saltzberg: Senior Scientist, The Bissett-Berman Cor-
poration, Santa Monica, California.

FOREWORD

HIS volume entitled Information Storage and Neural Control is
compiled from the proceedings of the Tenth Annual Scientific
Meeting" of the Houston Neurological Society. This meeting, like
its predecessors, was concerned with the exploration of a specific
area of current biomedical investigation. For some of those persons
who may have occasion to read the contributions presented here
by scientists in various disciplines, there may be little that is im-
inediately applicable in clinical medicine. Many of the concepts
and techniques which are described relate at this time only to
fundamental research, but there is no doubt that in the future a
better appreciation of these facts will be exceedingly important
to clinicians.

Progress in the biological sciences has been impeded to a con-
siderable extent by our inability to obtain objective quantitative
data in many critical areas of research. Biostatisticians and
geneticists were among the first to recognize this serious defect
and to inake attempts to fill in the gaps. The concept of vary-
ing information content was introduced when I — the informa-
tion value of a group of observations — was defined as the re-
ciprocal of the variance of the data. At first glance, this concept
appears to be in direct conflict with the modern idea of high
information content for a low probability datum. This need
not, necessarily, be the case, since a narrow range of variation
implies inclusion of low probability observations from the extremes
of the normal curve with the correspondingly high information
content of these low probability observations. The next iinportant
forward step resulted from the application in the biological sciences
of physical and chemical laws derived from the exact sciences.
This period began with the publication of A. J. Lotka's Elements
of Physical Biology in 1925, in which the theoretical concepts of
modern mathematics, physics, and physical chemistry were applied
rigorously to models of biological systems. Many of the laws and

viii Foreword

theories now widely accepted in various special areas of research
can be traced to this basic work. For example, the studies of
Gause and Witt on competitive action in biological systems con-
stitute experimental verification of several of the models described
by Lotka.

The modern era of physical biology, or biological physics,
cannot be dated precisely, but great impetus was given to this
field of investigation by the publication of Shannon's work. The
Mathematical Theory of Communication, in 1948. The full impact of
this monumental contribution is only now beginning to be realized.

The symposium from which these papers were compiled was
organized for the specific purpose of presenting to both basic
scientists and clinicians a spectrum of applications of information
theory in biology. The audience, as well as the contributors,
represented a diversity of disciplines including mathematics,
physics, chemistry, virology, ecology, physiology, and several
fields of clinical medicine, such as neurology, psychiatry, and
internal medicine. It is hoped that this volume will serve as a
source of reference for clinicians and basic scientists alike.

We wish to acknowledge the continued support of Dr. Hampton
C. Robinson, whose financial aid has made possible the presen-
tations of these symposia. Assistance in underwriting publication
costs of the proceedings has been given to us by the M. B. and
Fannie Finkelstein Foundation.

We also wish to express our appreciation to Dr. Wayne H.
Holtzman, Director of The Hogg Foundation for Mental Health,
Austin, Texas, for his helpful suggestions in the formulation of
the program.

We are grateful for the wonderful cooperation given us by the
contributors to this volume and for the editorial assistance of
Thelma Armstrong and Joan Chambers.

W. S. F.
W. A.

CONTENTS

Page

Contributors
Foreword

vii

Part I — Introduction
Moderator William S. Fields, M.D.
Chapter

I. What Is Information Theory? — Bernard Saltzberg ... 5

Discussion of Chapter 1 17

II. Binary Representation of Information — Robert T. Gregory . 27

III. Information Processing Theory — Robert K. Lindsay . . 34

Part II — Information in Biological Systems
Moderator: Heather D. Mayor, Ph.D.

IV. Genetic Control of Protein Synthesis — Harrison Echols . . 59
Discussion of Chapter IV 72

V. Coding by Purine and Pyrimidine Moieties in Animals,

Plants, and Bacteria — Saul Kit 76

Discussion of Chapter V 120

VI. Virus Action and Replication — James E. Darnell, Jr. . .123

Discussion oi Ch3.\)ie.r VI 139

VII. The Information Concept in Ecology: Some Aspects of In-
formation-Gathering Behavior in Plankton — Bernard G.

Patten 140

Discussion of Chapter VII 171

VIII. Exchange of Information About Patterns of Human

Behavior — ^Gregory Bateson „ 173

Discussion oi Chdipi^r Will 184

X Contents

Chapter Page

Part III — Neurophysiological Aspects of
Information Storage and Transfer
Moderator: Hebbel E. HofF, M.D., Ph.D.

IX. Information Storage in Nerve Cells — Frank Morrell . . . 189
X. How Can Models From Information Theory Be Used in

Neurophysiology? — Mary A. B. Brazier 230

Discussion of Chapter X 241

XI. Neural Mechanisms of Decision Making — E.Roy John . . 243

Discussion of Chapter XI 278

XII. Anastomotic Nets Combating Noise — Warren S. McCulloch . 283

Discussion of Chapter XII 296

Part IV — The Human Nervous System
Moderator: Wayne H. Holtzman, Ph.D.

XIII. The Individual as an Information Processing System —

James G. Miller 301

XIV. Information Processing in the Time Domain — Neil R. Burch

and Harold E. Childers 329

Discussion of Chapter XIV 349

Part V — Summary and General Discussion
Moderator: Ralph W. Gerard, M.D., Ph.D.

XV. Summary— Ralph W. Gerard 353

General Discussion 367

Appendix A
Introduction— Michael H. Arbib 377

A Logical Calculus of the Ideas Immanent in Nervous

Activity— Warren S. McCulloch and Walter H. Pitts . . 379

Index 401

INFORMATION STORAGE AND NEURAL CONTROL

PART I — INTRODUCTION

Moderator: William S. Fields, M.D.

CHAPTER
I

WHAT IS INFORMATION THEORY?

Bernard Saltzberg

^^ INTRODUCTION

XHE purpose of this paper is to describe the principles under-
lying" the quantitative aspects of the storage and communication
of information so that a better understanding may be gained of
the nature of efficient information storage, with its attendant
implications in coding and control processes, including neural
control. Insofar as possible, the discussion will avoid abstract
mathematical arguments and will be directed to those with little or
no previous acquaintance with probability or information theory.

INFORMATION MEASURE

Although information theory is an essentially mathematical
subject, a basic understanding of the underlying principles can
be acquired without resorting to complex mathematical argu-
ments. In simple qualitative terms, information, as defined by
Shannon, * is merely a measure of how much uncertainty has

*The formal development of information theory originated in the work of Claude
E. Shannon of Bell Telephone Laboratories who published his fundamental paper,
"The Mathematical Theory of Communication," in 1948. In this paper he set up
a mathematical scheme in which the concepts of the production and transmission
of information could be defined quantitatively. Historically however, Shannon's
work stems from certain early basic observations in theoretical physics concerning
entropy. Boltzman (1894) observed that entropy is related to "missing information,"
inasmuch as it is related to the number of alternatives which remain possible to a
physical system after all the macroscopically observable information concerning it
has been recorded. Leo Szilard (1925) extended this idea to a general discussion of
information in physics, and von Neumann (1932) treated information in quantuin
mechanics and particle physics. Information theory, as developed by Shannon, con-

6 Information Storage and Neural Control

been removed by the receipt of a message. For example, if you
are told that the baby Dr. Jones delivered today is a boy, then
you have been given one bit of information* (by definition one
bit is the amount of information necessary to resolve two equally
likely alternatives). If the uncertainty is greater, the amount of
information necessary to remove it is greater. Therefore, a message
which identifies one of 32 equally likely alternatives contains more
information (five bits as we shall explain later) than a message
which resolves 16 equally likely alternatives (four bits).

Some elementary examples to convey the basic notions of
information measure may be helpful in developing some qualitative
insights. Consider a simple game in which you are asked to guess
a number with possible values from one to eight. With no a priori
knowledge of which of these numbers is the correct choice, the
probability of guessing the correct number is 1/8. In the language
of information theory, this situation might be described as follows:
A system is in one of eight equally probable states, and the state
of the system is completely unknown to the receiver. It is appro-
priate to ask how much information is conveyed to the receiver
by completely resolving the uncertainty of the receiver's knowledge.
Let us designate the eight equally probable states of the system
by the numbers from one to eight. Assume that you are to ask
only binary questions, i.e., questions which admit only of a yes
or no answer, in an attempt to determine the state of this system.
It is a simple matter to discover that the minimum number of
such questions certain to establish the state of the system is three.
In this simple illustration we have introduced the basic concepts
from which the quantitative definition of information can be
formulated: namely, the number of equally probable states of

nects more directly with certain ideas generated about thirty years ago by H. Nyquist
and R. V. L. Hartley, both of Bell Telephone Laboratories. Professor Norbert Weiner's
work in the study of Cybernetics, which deals mainly with the use of information to
effect certain control actions, has been a major impetus in applying information
theory to biological and central nervous system phenomena.

*Information theory does not deal with the importance of the information in a
message. For example, the information in the message, "the baby is a boy," is one bit
independent of whether you are the father. This comment is made in order to em-
phasize the fact that information theory docs not deal with the subjective value of
information, which falls more properly into the domain of semantics, but rather with
objective measures of information.

JVhat is Information Theory? 7

a system (in this case eight), the number of ahernatives resolved
by each question (two, because of the binary nature of the ques-
tion), and the minimum number of questions necessary to de-
termine tlie state of the system (three in this case). It is easily
seen that tiie relationship between these numbers is 2'^ = 8. In
the vernacular of information theory, we say that three bits of
information are necessary to determine the state of such a sys-
tem; i.e., three appropriately chosen questions, each of which
resolves two alternatives, usually designated as 1 or 0, correspond-
ing" to yes or no, are all that is necessary to reduce indeterminacy
to certainty. The problem of choosing the appropriate questions
is analogous to that of choosing a good code. For example, asking
the question: "Is the number 3?" would correspond to very
inefficient coding of information. Phrasing or coding questions in
this way would require that you be allowed to ask eight questions
in order to be certain to determine the state of the system. In this
illustration a correct way of coding or phrasing the c^uestions
would be as follows: Question 1: "Is the number greater than 4?"
If yes, then ask Question 2: "Is the number greater than 6?"
If no, then ask Question 3: "Is the number 5?"' If no, then the
system must be in state 6.

As previously mentioned, the probability of having guessed the
correct state before receiving these three bits of information was
1/8 in the example used. After the first bit of information is re-
ceived the probability of guessing" correctly is increased from
1/8 to 1/4, after the second bit from 1/4 to 1/2, and after the
third bit from 1 ;'2 to 1 . Thus, each successive bit received has
reduced our uncertainty as to the state of the system until all the
uncertainty is removed. In this example, the receipt of any more
information is unnecessary or redundant. However, as we shall
discuss later, the redundancy may be useful in correcting errors
due to noise in the communication channel.

In order to use a more general illustration which is not restricted
to a system with equally probable states, let us consider the game
of Twenty Questions. In most situations the probabilities of some
states, i.e., the possible set of objects to be identified, are higher
than the probabilities of others. A good information theorist with
some a priori knowledge of the probabilities of these states would

8 Information Storage and Neural Control

ask questions in accordance with his a priori knowledge of these
probability states. Information theory as well as intuition tells us
that a good strategy would be to inquire about the most likely
probability states first. This point might be more clearly illustrated
by considering the information storage problem, which is equiva-
lent in principle.

INFORMATION STORAGE

Mathematically, there is no important difference between the
application of information theory to communications systems
through which information flows continuously and to static sys-
tems used for storing information. The problem of storing infor-
mation is essentially one of making a representation. The repre-
sentation can take any form as long as the original or something
equivalent to it can be reconstructed at will. It is clear for example,
that even though information exists as sound, there is no need
to store it acoustically. There is no objection to the use of a re-
versible code since information is invariant under such a trans-
formation and, therefore, can be stored equally well electrically
or magnetically; as for example on a recording tape. We simply
have to insure that every possible event to be recorded can be
represented in the store. This implies that an empty store must
merely be capable of being put into different states and that the
precise nature of these states is quite immaterial to the question
of how much information can be stored. Thus, the capacity of
an empty information store depends only on the total number
of distinguishable states of which it admits. Hence, the larger the
number of states, the larger the capacity.

If a storage unit such as a knob with click positions has n possible
states, then two such units provide altogether n'' states. From this
it is clear that duplication of the basic units is a powerful way to
increase storage capacity. Since physically, it is generally easier
to make two ^/-state devices than one single device with n'' states,
practical storage systems will generally be found to consist of a
multiplicity of smaller units. Thus, 1000 two-state devices can
provide a total of 2'""'' possible states.

The exponential dependence of the number of states on the
number of units immediately suggests a logarithmic measure of

What is Information Theory? 9

information capacity and, in fact, the information capacity of a
storage system is defined by the equation,

C = log n,
where n is tlie number of distinguishable states. This makes the
capacity of a compound storage system equal to the capacity of a
basic storage unit multiplied by the number of units in the system.
If the logarithm is taken to the base 2, then C is the equivalent
number of binary storage units (bits); and if the logarithm is
taken to base 10, then the information capacity is given in units
called Hartleys. For example, the capacity of a knob with 32
click positions is equal to that of five two-position switches (five
bits). A ten-position knob, on the other hand, has a capacity of
one Hartley, and two ten-position knobs capable of being placed
in 100 different states have an information capacity of two Hart-
leys. Since storage elements which are binary in nature (two
positions) are much less susceptible to error and are easier to
mechanize, it is more common to deal with binary units (bits) of
information than with decimal units of information (Hartleys).

So far we have discussed information storage and, correspond-
ingly, information capacity. There is an important distinction,
however, between information capacity and information content.
The information content of a message may be defined as the !
minimum capacity required for storage. To illustrate this impor-
tant point, consider a two-state message such as a reply to some
question which admits only yes or no. If someone in this audi-
torium is asked, "Are you a doctor?", then a reply admits of two
possible message states and it will certainly be possible to store
the reply in one binary storage unit. Intuition tells us that the
message contains one bit of information, for, by itself, it cannot
be stored any more efficiently. However, our previous discussion
has demonstrated that a bit of information should substantially
reduce uncertainty. In view of the fact that most of the people
in this auditorium are doctors, I could simply guess "yes" for
each person questioned and be correct most of the time. Thus,
one would expect the average information per question to be
less than one bit, as indeed it is.

Using a numerical example from Woodward, suppose that 128
people in this auditorium are questioned and the 128 binary

1 0 Information Storage and Neural Control

messages have to be stored in a system of binary storage units.
(It will be assumed that we are interested in preserving the exact
order of the replies and not simply in counting the number of
yeses and noes). Proceeding in the most obvious manner and using
one storage unit for each message, we should set down a sequence
such as this:

YYYYYYYYNYYYYYYYYYYYYYYYNYYY . . .

The question, "Are you a doctor?", expects the answer yes from
this medical group, and of the 128 messages there will be only
one or two no states. Therefore, it would be more economical to
store the positions of the noes in the sequence and convert the
numbers 9, 25, corresponding to the noes in the above sequence,
into the binary form as 0001001 and 0011001. Seven digits are
allowed because there are 2 messages altogether. Thus, the se-
quence could be coded into the sequence

00010010011001 . . .

This makes use of binary storage units just as in the original
sequence, but a much smaller number of them. It is understood,
as part of the code, that decoding proceeds in blocks of seven.
This avoids violating the binary form of marking off groups of
digits. The preceding code, which is only one of many that could
be devised, shows that a set of two-state messages can sometimes
be stored in such a way that each message occupies, on the average,
less than one bit of storage capacity.

From such considerations, the following definition of information
content is suggested:

I = -2 ?^.Tog/>i

whei'e p. = probability of the ith. state

n = total number of states.
If all n states are equally probable, then it follows that pi = \/ n
for all values of /. Thus, substituting pi = 1/n into the expression
for /, we note that

/ = log 71.

From this it follows that if all n states are equally probable, the
information content is exactly equal to the information capacity

What is Information Theory? 11

of a store with n states. In other words, if ah the states are equally
probable, then it is not possible to store the information any more
efficiently than one bit of information per message, on the average.
If there are some preferred states, i.e., if the pi are not equally
probable, then it can be shown that the average information per
message can range from zero to one bit. Zero information cor-
responds to the condition where a single state has unity probability
and all the other states have probability zero. As stated before, the
other extreme is attained when all the states are equally probable.
In other words, on the average, one must receive more information
to resolve fully the states of a completely random system (all
states equally probable) than to resolve the states of a less random
system (all states not equally probable).

COMMUNICATION OF INFORMATION

Let us now consider information theory as it pertains to the
communication of information. For this purpose, we define infor-
mation received as the diff'erence between the state of knowledge
of the recipient before and after the communication. In more
precise terms, information received is given by:

where

/ = log

I = information received
Pa = probability of the event at the receiver after

the message is received
Pb = probability of the event at the receiver before
the message is received.

In receiving a message regarding the sex of a baby, for example,
this expression implies that if the receiver does not know the
baby's sex, then

1
Vb = 2'

and if you (the receiver) receive a signal that "the baby is a boy,"
then

Pa = I (provided the message is not noisy)

1 2 Information Storage and Neural Control

and, therefore,

/ = log jy^ = log 2 = 1 bit.

If the message were a noisy one, then you might not be quite
certain that you received the signal for "boy" correctly. You may
nevertheless be willing to give four to one odds that it is a boy,
based on the noisy signal you received. In this case

Va = .8

and, thus

I = log Yjx = log 1.6 = .68 bits,

1/ Z

which demonstrates the quantitative reduction in information due
to noise.

In the case of no noise it is clear tlie pa is always unity and

/ = -log pb.

The important problems in tlie communication of information are,
however, concerned with the effects of noise. The maximum
amount of information that can be sent through a communication
channel in the presence of noise is a topic of particular usefulness
whicli we shall examine briefly.
Getting back to the expression

it is interesting to note the implications of this definition. If, for
example, a communication system is so noisy that the message
has not reduced the receiver's uncertainty as to the event (i.e.,
p^ = p^)^ then / = log 1=0 and no information has been received.
Thus, it is seen that a communication does not necessarily convey
any information. The communication must reduce the recipient's
uncertainty as to the events in question in order to convey infor-
mation. The mathematical definition I = log Pa/Pb is, therefore,
consistent with intuitive requirements for a measure of information.
One of the important problems in communication theory has
to do with the maximum rate at which information can be sent
over a communication channel which is disturbed by random
noise. This problem has fundamental implications for information

What is Information Theory? 13

transfer rates in biological systems as well as for the neurophysio-
logical aspects of information transfer, which are to be treated in
later papers at this symposium. In order to discuss this problem,
it is necessary to define a few terms, namely:

B = the bandwidth of the communication channel (this
defines the range of frequencies which can pass through
a system)

S = received effective signal power
N = received effective noise power.

In any communication system, the message from which the
recipient derives information is a combination of signal plus noise.
It can be shown (not without some mathematical difficulty,
however) that the maximum rate at which information can be
sent through a channel — which is 1) signal power limited by .S',
and 2) disturbed by random noise of power N — is given by

R = B\og(l + S/N).
In other words, the maximum information that can be sent in
a time T is RT or

I = BT log (1 + S/N).
The important implication of these formulae in the design
of communication systems resides in the fact that S/N, the signal-
to-noise ratio, is a function of B, the bandwidth of the channel.
Therefore, if one determines the dependence of signal-to-noise
ratio on bandwidth, it is possible to achieve a tradeoff between
S/N and B, which optimizes the information handling capacity
of the system.

EQUIVOCATION

This leads us to the more involved concepts of equivocation
and channel capacity and to Shannon's basic theorems on error
correction. The previously mentioned maximum rate at which
information can be sent through a channel, usually referred to
as the channel capacity C, is intimately related to these ideas and,
therefore, requires some elaboration and clarification.

As Shannon has stated, it may seem surprising that we should
define a definite capacity C for a noisy channel, since we can never

14 Information Storage and Neural Control

send certain {i.e., probability equal one) information over such
a channel. It is clear, however, that by sending the information
in a redundant form, the probability of errors can be reduced.
For example, by repeating the message many times and by a
statistical study of the different versions of the message, the prob-
ability of errors can be made very small. One would expect,
however, that to make this probability of errors approach zero,
the redundancy of the encoding must increase indefinitely and
the rate of transmission must therefore approach zero. This is by
no means true. If it were, there would not be a well-defined
capacity, but only a capacity for a given frequency of errors or a
given equivocation, the capacity going down as the error require-
ments are made more stringent. Actually, the capacity C defined
earlier has a very definite significance. It is possible to send infor-
mation at the rate C through the channel, with as small a fre-
quency of errors or equivocation as desired, by proper encoding.
This statement is not true for any rate greater than C. If an attempt
is made to transmit at a higher rate than C, then there will neces-
sarily be an equivocation equal to or greater than the excess.

To clarify the concept of equivocation, let us suppose there are
two possible symbols, 0 and 1, and that we are transmitting at a
rate of 1,000 symbols per second with probabilities Po = Pi = 1/2.
Thus, our source is producing information at the rate of 1,000
bits per second (Shannon refers to this as the entropy of the
source). During transmission, noise introduces errors so that, on
the average, one symbol in 100 is received incorrectly (a 0 as 1,
or 1 as 0). What is the rate of transmission of information? Cer-
tainly less than 1,000 bits per second since about one per cent
of the received symbols are incorrect. Our first impulse might be
to say the rate is 990 bits per second, merely subtracting the
expected number of errors. This is not satisfactory since it fails
to take into account the recipient's lack of knowledge of where
the errors occur. We may carry this to an extreme case and
suppose the noise so great that the received signals are entirely
independent of the transmitted signals. The probability of receiving
1 is one-half whatever was transmitted, and the same is true for
zero. Since about one-half of the received symbols are correct due
to chance alone, we could give the system credit for transmitting

What is Iiijormatwn Theory? 15

500 bits per second while actually no information was being
transmitted at all. Equally good transmission would be obtained
by dispensing with the channel entirely and flipping a coin at
the receiving end.

The proper correction to apply to the amount of information
transmitted is the uncertainty of what was actually sent after we
have received a signal. This reduction in received information
is the conditional entropy of the message and is called the equivo-
cation. It measures the average ambiguity of the received signal
or, in other words, the average uncertainty in the message when
the signal is known. For definiteness, let us calculate the equivo-
cation of the first example. In this example, noise caused an error
in about one out of each 1 00 symbols, so that if a zero was received,
the a posteriori probability that a zero was transmitted is .99 and
that a 1 was transmitted is .01. The equivocation, or the uncer-
tainty associated with each symbol, is exactly the entropy associated
with these concHtional probabilities. Thus,

Equivocation per symbol = —[.99 log .99 + .01 log .01]

= .081 bits.

Since the source is producing information at a rate of 1,000
bits per second, the equivocation rate is 1,000 X .081 =81 bits
per second. Therefore, we may say that the system is transmitting
at a rate of 1,000 — 81 = 919 bits per second. Again, in the
extreme case where a 0 is equally likely to be received as a 0 or 1
and a 1 as a 1 or 0, the a posteriori probabilities are ,1/2 and 1/2,

Equivocation = — U^ log ~y -{- 7, log -^

= 1 bit per symbol,

or 1,000 bits per second. The rate of transmission is then zero as
it should be.

These examples have demonstrated that noise causes a reduction
in received information and have shown precisely how this loss in
information is measured. Before leaving this subject, I would like
to quote a theorem (due to Shannon) which emphasizes why this
quantitative measure, called the equivocation, is so important.

Shannon has shown that (in a noisy communication system)
if a correction channel is added which has a capacity equal to

1 6 Information Storage and Neural Control

the equivocation of the system, then it is possilole to encode the cor-
rection data so as to send it over this channel and correct all but
an arbitrarily small fraction of the errors. This is not possible
if the channel capacity is less than the equivocation.

Roughly then, the equivocation may be considered as the
amount of additional information that must be supplied per
second at the receiving point to correct the received message.

CONCLUDING REMARKS

This paper has dealt primarily with some of the basic aspects
of the statistical theory of information. Very few comments have
been made regarding semantic information, not because this sub-
ject is unimportant, but rather because there is at present no
sound quantitative theory for treating semantic information. In
concluding, however, I would like to remark that statistical infor-
mation theory has relevance to semantics insofar as it tells us what
confidence we can place in the accuracy of the information we re-
ceive as opposed to the information sent. The significance or value
of the information to the recipient does not fall within the domain
of the quantitative measures provided by information theory.

With reference to the theme of this symposium, one might say
that information theory provides insight for analyzing and im-
proving storage and communication processes, but does not unravel
the bewildering complexities associated with significance, meaning,
or value judgments. From my personal experience with the prob-
lems of physiological signal analysis, this fact lies at the core of
the difliculties which the life sciences face in applying information
theory to their problems. Finding significant factors in a maze
of statistical information is an immensely challenging problem in
medicine as well as in many other fields. The problems require
both an intelligent application of information theory and a
thorough knowledge of the phenomena being studied so that good
questions can be asked in the right way to enhance the probability
of getting a useful answer.

REFERENCES

1. Bell, D. A.: Information Theory and Its Engineering Applications. New
York, Sir Isaac Pitman and Sons, Ltd., 1956.

What is Information Theory? 17

2. Cherry, Colin: On Human Communication — A Review, A Survey, and A

Criticism. Massachusetts Institute of Technology, Technology Press,
1957.

3. Feinstein, Amiel: Foundations of Irformation Theory. New York,

McGraw-Hill, 1958.

4. Gabor, D.: Lectures on Communication Theory. Massachusetts Institute

of Technology Research Laboratory of Electronics, Technical Re-
port No. 238, 1952.

5. Goldman, Stanford: Information Theory. Englewood Cliffs, New Jersey,

Prentice-Hall, Inc., 1952.

6. Khinchin, A. I.: Mathematical Foundations of Information Theory. New

York, Dover Puljlications, Inc., 1957.

7. Schwartz, Mischa: Information Transmission Modulation and Noise. New

York, McGraw-Hill, 1959.

8. Shannon, Claude E., and Weaver, Warren: The Mathematical Theory

of Communications. Urbana, The University of Illinois Press, 1949.

9. Stumpers, F. L. H. M.: Interpretation and Communication Theory. Lab-

oratoria N. V. Philips Gloeilampenfabreiken, Eindhoven, Holland,
1959.
10. Woodward, P. M.: Probability and Information Theory, With Applica-
tions to Radar. New York, Pergamon Pixss, 1953.

DISCUSSION OF CHAPTER I

Heather D. Mayor (Houston, Texas): In case one wishes to
draw analogies from physics rather than from thermodynamics,
can you clarify something? Could we equate your conditional
entropy with, say, the Heisenberg uncertainty principle and your
noise ratio with the perturbations introduced in measuring the
system?

Bernard Saltzberg (Santa Monica, California) : Yes, in terms of
inforination measure, uncertainty, or conditional entropy, and the
noise which gives rise to the uncertainty (/.^., equivocation) are
aspects of essentially ec^uivalent ideas.

Mayor: And the Bohr generalized complementary principle in
biological systems — would that fit, too, with your intrinsic con-
cepts? For example, if we can find the exact position of a micro-
organism, it is difficult at the same time to establish with certainty
another parameter, such as its size. In a biological system, would
this approach fit with your generalized entropy concept?

1 8 Information Storage and Neural Control

Saltzberg: The principles involved in applying generalized
entropy concepts or information theory to biological systems and
quantum mechanical systems are not altered in essential ways.
There are differences in nomenclature which sometimes conceal
these basic similarities.

Mayor: But could you treat them the same way?

Saltzberg: Yes. In fact, all of these applications have an amaz-
ingly close parallel to the generalized treatment of entropy in
thermodynamics.

Robert R. Ivers (Fargo, North Dakota): Would you define
a little better the term noise that you used during your discussion?
Is this interference with signals, or is it the interposing of randoin
signals in the systein, or is it just general inaccuracy of the system?

Saltzberg: You have asked a very basic question. In order to
avoid confusion, I should like to refer to noise as a subclass of a
larger class of signals called undesired signals. Undesired signals
may be placed in three categories: namely, (a) noise, (b) inter-
ference, and (c) distortion. Noise signals may be defined as signals
which are not coherent with any signals to which meaning is
assigned. Interference may be defined as an undesired signal
which is a desired signal in some other system or is coherent with
desired signals of some other systein. Examples are cross-talk and
common channel interferences in broadcast programs. Distortion
introduces undesired signals due to effects such as non-linearities
or non-flat amplitude vs. frequency transmission characteristics.
In my discussion I have been referring to the first of these undesired
signals, namely, random noise.

Mayor: In actually performing measurements on your system,
you no doubt introduce additional perturbations which could be
considered as "noise." Would you consider this a valid parallel?

Saltzberg: This sort of parallel seems reasonable to me. If you
add an element of indeterminacy to the state of the system, you may
consider this as due to noise. There are some deep questions as
to what constitutes noise in systems, and these cannot be treated
in cjualitative terms or in brief comments.

Arthur Shapiro (New York, New York) : Along the same line,
how would you treat what happens if you read onto a transmission
line a page from a table of random numbers and then another

What is Information Theory? 19

page from a table of random numbers? Are you transmitting
information, and how would you determine how much?

Saltzberg: Yes. You are always transmitting information when-
ever you convey a message, unless the noise is so great that the
equivocation of the system is equal to the information content of
the source. Your question apparently refers to tlie importance of
the information. A table of random numbers may be useless to
the receiver, but, nevertheless, statistical information has been
communicated .

Shapiro: Then it is not really true, as you started out by saying,
that the meaning of what you transmit has nothing to do with how
much information is transmitted. The meaning apparently has a
great deal to do with how much information is transmitted.

Saltzberg: Apparently I have caused some confusion. Seman-
tic meaning or the importance of a message is subjective and is
not part of statistical information theory. The previously used
example applies here. A message announcing the birth of a boy
conveys one bit of information to an unknowing" receiver inde-
pendent of whether the receiver is the father or not. Thus, whether a
number is taken from a table of random numbers or a table of
trigonometric functions has no bearing on the information received,
providing the receiver has no a priori knowledge of these numbers.

Walter Abbott (Houston, Texas): The point has been made
that the information content of any datum is proportional to its
surprise value. Does this get involved in your semantic implications?

Saltzberg: Surprise, as used in this context, does not have any
semantic implications. If a datum or a message identifies one of a
thousand possible states, then it has surprise value in the sense that
you would have been extremely surprised to have guessed the state
without receipt of the information provided by the message. If a
system had only two possible states, then you would not be so sur-
prised to guess the correct state.

Mary A. B. Brazier (Los Angeles, California) : I believe that
by surprise value Dr. Abbott means an event of low probability.

Myron F. Weiner (Dallas, Texas) : How much must be known
of the probabilities, or of the number of probabilities of different
messages, or of the number of possible different messages to be
conveyed before one can get some idea of what a message is,

20 Information Storage and Neural Control

providing one has previously had no information about the
system?

Saltzberg: If you knew nothing" about the probability states of
the messages, then, of course, you would have very little engi-
neering data upon which to base an optimum design for a receiving
system. This question may pertain to the a priori probabilities
which are useful in choosing an appropriate code. This is analogous
to the Twenty Question game mentioned previously. If the ques-
tioner has some a priori knowledge of the probabilities, he can ask
questions in a specific order, depending on the probabilities, and,
on the average, will ask fewer questions to get a correct answer
than will someone who just asks questions at random.

E. Roy John (Rochester, New York): It seems to me that
there is a large class of messages in which the a priori probability
cannot be evaluated by the receiver. One can think of messages
in which the rate of convergence of the total information of the
message is not linear for the components of the message and in
which the rate of convergence would depend upon the sequence
of the components. This might, as a matter of fact, be a charac-
teristic difference between certain languages. In a situation where
you do not have this advantage of being able to stipulate prob-
abilities— in which the probability of a given event is affected by
the preceding sequence — it seems to me you must modify your
treatment to provide an argument for the bit function, recog-
nizing that the information content of a specific event depends on
preceding events or context. Could you say something about how
you treat this kind of situation, since it seems to be much nearer
the situation in which we frequently find ourselves in the nervous
system than does the starting point from which you began here.

Saltzberg: Your question is a good one. It refers to the effects
of inter-symbol influence on information content. The fact that
there are transitional probabilities which have to be taken into
account in determining the information content in language, for
example, is included in the mathematics of information theory.
These transitional probabilities have the effect of making the
information content of a sentence much less than that calculated
by assuming that the sequences of letters and words are inde-
pendent of their predecessors. I should comment at this point on

What is Information Theory? 21

another aspect of information theory which I have not mentioned
before. Information theory is concerned with the properties of
ensembles of messages or objects. One of the properties which is
quite important in scientific analysis is known as ergodicity.
In analyzing" many problems, the assumption of ergodicity is one
that is a practical necessity rather than a statement of fact relative
to the nature of things. However, this simplifies analysis in that
it allows one to examine a long time sample of one of the mem-
bers of an ensemble and to conclude from this that he knows
something about the statistics of the ensemble. This is not always
true since, for example, it would imply that the statistics associated
with the EEG record of a single individual apply equally well to
another subject. If this were the case, then an ensemble of messages
composed of the EEG's of many subjects would be an ergodic
ensemble. In testing engineering components, one ordinarily takes
a single component and tests it for a long period of time and then
draws implications about the behavior of all similar components.
This is an aspect of statistical analysis and information theory
which, when applied to the life sciences, creates a great many
problems since one may not be aware that this assumption may
underly the mathematical formulation of certain problems.

Herman Blustein (Chicago, Illinois): How do you determine
the validity of the samples when you analyze the EEG's in this
manner and make a generalization from them?

Saltzberg: The validity of the sample is not the question here.
For example, an EEG record may be sufficiently long to give you
a good estimate of its properties for a particular individual. How-
ever, unless EEG's of different individuals are statistically similar,
or ergodic, this does not allow you to draw any conclusions about
the properties of another individual's EEG record.

Harold W. Shipton (Iowa City, Iowa) : The way the discus-
sion is going means, I think, that we have to say a little more
about the properties of noise, because when we deal with formal
information theory we use "noise" in exactly the way that we
used to use the phrase "Brownian movement." This is quite
acceptable. However, when we perform an experiment, we are
dealing with band limited noise, and we are also probably dealing
with nonrandom perturbations in the system. I would like to hear

22 Information Storage and Neural Control

from Dr. Saltzberg whether he wishes to introduce a second
term — noise in this physical sense — or whether he would also Hke
to consider things wliich are not related to the required signal
over a short-time epoch. There is a good example of tliis in the
field of EEG analysis. If you repeat an experiment in time, you
expect the signal-to-noise ratio to go up as -^/N , but in almost
any biological system you will find it goes up by rather more than
this simply because our noise is not "noisy," so to speak, in the
sense that it is not white.

Saltzberg: There are many things which people refer to as
noise that are quite diff"erent from one another. The different
types of noise have considerably different effects on the informa-
tion content of systems. For example, there is distortion which,
if reversible, does not reduce the information content of a message
at all. Although people commonly refer to this type of distortion
as noise, it is not noise in the context of information theory. You
have mentioned white noise, which is a special type of random
noise, and the chscussion on maximum rate of transmission of
information in the presence of noise is applicable to this lype of
noise. It is important to distinguish between this type of noise
and interference, which is sometimes referred to as noise. The
basic difference is that random noise is not coherent with any
signals to which meaning is assigned, while interference is an
undesired signal which is coherent with desired signals of some
otlier system. The improved signal-to-noise ratios that you men-
tioned for biological systems may have something to do with the
ability of biological systems to narrow their noise bandwidths by
providing certain kinds of adaptive filtering.

Blustein: Is this similar to a television signal in which the
audio signal is intact and the video is distorted, and yet one can
receive and interpret the signal?

Saltzberg: I am not sure of the analogy. I have to beg off on this.

Blustein: Does the system have to filter the signals?

Saltzberg: If the receiver has some a priori knowledge of what
it is looking for, then it can do an excellent job of minimizing the
effects of noise. One of the simple ways this is accomplished is
by means of frequency filters or correlators. If the information
signals occupy a narrow bandwidth, then narrowing the accept-

What is Information Theory? 23

ance band of the system by employing filters will reduce the
amount of random noise, since random noise occupies all parts
of the spectrum. The object is to use spectrum space for the signal
information, not the noise.

Shapiro: Are there actually two kinds of information involved
here, only one of which is treated in this way? Perhaps I should not
use the word information for the other kind, but a priori knowledge
about the nature of the system which the receiver may have,
whether it is a person or a machine, must be important. For
example, if the machine or the person knows that nothing is
coming over this channel except when some kind of an event
occurs, then when a lot of noise, i.e., a lot of signals, comes over,
this will be interpreted as meaning a lot of activity in the trans-
mitter. On the other hand, if the receiver knows from past experi-
ence that this system generates its own noise, then when a lot of
noise comes over the channel, the receiver says it does not know
what is going on. Only when a clear individual signal comes over
will it be interpreted as information. I think that there is another
set of values, which I suspect is what you mean by filter theory.

Saltzberg: I believe your comment relates to filtering in the
time domain. It is possible to design a system which simply stops
processing information when the signal is too badly corrupted by
noise. If cues, which are essentially a priori information, are avail-
able, then it is possible to use various types of time filtering. When
you do not have any a priori timing cues for determining when
you ought to process information, then frequency filtering is sug-
gested, providing you know something about the spectral region
which the signals occupy.

Peter Kellaway (Houston, Texas): It would help if you could
tell us something about your ow^n results. I understand you are
interested in analyzing the EEG. What sort of information have
you obtained by applying information theory and technique to
this type of analysis, and what type of information do you hope
to obtain?

Saltzberg: I can comment on some of the analysis of EEG
which was conducted in an attempt to establish how much infor-
mation is processed by one technic^ue of EEG analysis as compared
with the amount of information which is processed using another

24 Information Storage and Neural Control

technique of EEG analysis. The objective of this investigation was
to evaluate the information handling capability of zero crossing
analysis as compared to frequency analysis. Conventional fre-
quency analyzers were compared on an information theoretical
basis with the zero crossing analyzers which Dr. Burch of Baylor
is using in his EEG research. The particular problem treated was
concerned with how much information is abstracted by an analyzer
which processes only time point data associated with zero crossings
of the record and the time positions of its peaks and points of inflec-
tion. Knowing the time resolution which could be achieved with
an analyzer of this type, it was possible to establish the frequency
resolution that would be required of a frequency analyzer in order
to provide the same amount of information. I will admit that
this does not add much understanding of neurophysiological
processes, but it does give a basis for some engineering decisions
relative to whether one type is more effective in abstracting
information than another type. It turned out that the resolution
that could be achieved with practical period analyzers was much
greater than that which could be achieved with practical fre-
quency analyzers.

Kellaway: Would it matter if it were a physiological signal?
The signal that you are using could be anything, could it not?

Saltzberg: The signal could well be anything. However, what
one would like to achieve is a signal representation and a cor-
responding form of analysis emphasizing the physiological effects.
For example, if one attempts to extract information by analyzing
the coefficients of a Fourier series, the problem may be impossible
because the interesting infoi^mation is contained in small per-
turbations in many amplitudes of many frequencies. Since similar
small perturbations can be caused by noise, the presence of noise
would invalidate any physiological correlates being sought. How-
ever, if some other parameter associated with a different signal
representation were measured, it is possible that physiological
effects could cause this parameter to change grossly, which would
lead to a higher probability of valid pliysiological correlates.

Max E. Valentinuzzi (Atlanta, Georgia): Gan you say any-
tliing about the relation between information and organization?
Gan we say that these two words are equivalent? You have dealt

What is Information Theory? 25

with transmission of information from one point to another.
Suppose that now we are not transmitting information, but are
organizing a set of elements in a particular pattern or configura-
tion. What is the amount of information obtained by the system
in the transition from one state to the other?

Saltzberg: Whether we talk about the entropy of thermo-
dynamics or the information in a message, we are in principle
talking about organization, or, more precisely, about the prob-
abilities of the various arrangements of the component parts of
the system. The second law of thermodynamics states that entropy
must increase or, at best, remain constant, which is another way
of saying that the system is becoming more disorganized or ran-
dom. In communication systems, however, upon the receipt of
information, the disorganized or uncertain state of our knowledge
becomes more certain or better organized; therefore, we may
consider received information as negative entropy since it increases
the organization of the receiver.

Gregory Bateson (Palo Alto, California) : You separated rather
clearly the notion of measuring cjuantity of information from the
notion of ""meaning'' of the information measured, but it appears
to me that this becomes difficult when we have a secjuence of
items comprising a total message and so related that some of these
items reflect upon the significance of other items in the sequence.
In this case, the meaning of these meta signals is a very important
part of the whole economics of communication.

Saltzberg: I think your question refers to the very strong con-
straints which may exist between the elements of a signal. These
constraints are essentially the transition probabilities and the
intersymbol influences which are referred to in information theory.
We do not look upon a knowledge of these transition probabilities
as implying meaning in the sense that we talk about semantic
meaning. In other words, the constraints between a sequence of
elements comprising a signal are accounted for in measuring
information, but the importance of the signal, i.e., its semantic
value, is not.

Bernard S. Patrick (Memphis, Tennessee): Can you speak
for just a moment about redundancy or the use of redundancy in
communication systems?

26 Information Storage and Neural Control

Saltzberg: In any communications system which places a very
high priority on accuracy, redundancy is frequently employed.
In situations where it is not practical to increase the strength of
the signal in order to improve accuracy by increasing signal-to-
noise ratio, it becomes important to use redundancy to correct
the errors due to noise. In digital communication systems, re-
dundancy is often employed in the form of error-correcting codes.
The accuracy of language communication is greatly enhanced by
the redundancy of language. It is easy to get a qualitative feeling
for the amount of redundancy in the English language. For
example, consider a situation in which a sentence composed
of a sequence of symbols is transmitted and the first symbol re-
ceived is a t, the second a k, and the third an e. You would
have no trouble concluding that the word transmitted is "the"
because of the constraints in the language. The language structure
tells us that "tke" must be in error since "tke" is not a word.
Further, since h very frequently follows t in the English language,
there is not much doubt that the first word in the sentence is
"the." This is one aspect of how the redundancy of the language
increases the accuracy of communication. In fact, in communica-
tion systems which are signal power limited, it is necessary to
employ redundancy techniques to reduce the errors in com-
munication due to noise.

Ralph W. Gerard (Ann Arbor, Michigan): It was said that
maybe an example would illuminate some of the points that
would come up, and I think I can give one that might be helpful.
Limiting the spectrum in frequency or the interval of time in
which the significant signal is to be expected is helpful. Gregory
Bateson referred back to that in speaking of a para-signal, which
tells the meaning of the signal itself by indicating where in the
total space you must look for the signal. I wonder how many in
the room will understand the statement, ^^ Cur antrum santrum,
ovidiim, ovidum.'' Hands? None. That is because you thought I was
talking Latin, whereas I was actually talking German. Now I
will repeat it the same way. "'/Tz//? rant rum, Sand rum, ohwiedumm, oh
wie dummy Knowing that the language is German is the para-
signal; locating the message in the total space is what is meant by
having the proper set.

CHAPTER
II

BINARY REPRESENTATION OF
INFORMATION

Robert T. Gregory, Ph.D.
INTRODUCTION

T IS well known that most modern electronic digital computers
use the binary representation of numbers internally rather than
the more familiar decimal representation, although sophisticated
programming systems may allow the computer user to do almost
all of his communicating with the machine in decimal. The
reason for the fact that binary representation is in common use
is explained in the next section.

It is tiie purpose of this paper to review the binary representation
of numbers, including the word structure for a typical binary
computer, and to demonstrate some typical machine commands
that are available for manipulating patterns of binary digits. It
is hoped that this will provide some indication of the extreme
versatility of the electr'onic computer as an information processing
instrument and will encourage those who have not yet discovered
its usefulness to explore its potentialities.

REPRESENTING NUMBERS INSIDE A COMPUTER

It is well known to those who design the basic circuits for
digital computers that the optimum number base, B, for representing
numbers inside a computer, from the standpoint of economy of
electronic hardware needed, is, B = 3. To verify this let us recall
that the number of numbers that can be expressed using n digits,
base B, is B". For example, in the decimal numeral system if
n = 3, we can express 10^ numbers 000, 001, . . ., 999.

28 Injormation Storage and Neural Control

Assume that the number of electronic components required to
represent a single digit, base 5, is approximately proportional to
B. Thus, a rough estimate of the number of electronic components
required to represent n digits, base B, is

TV = n lu B,
where A"i is a constant.

If B" = P is held fixed, and we wish to find the value of B
which minimizes the number of electronic components, N, needed
to represent P numbers, we proceed as follows: Since

i?" = P,

we can write

and so

n In B = In P
= /v%.

cJN _ „
dB - ^^'

In B •
Thus, the number of electronic components needed is

KsB

In B •

DifTerentiating with respect to B yields

InB - r

. (In BY' _■

Setting this derivative equal to zero gives us

biB = 1,
or

B = r

= 2.71828 ....

For integer values of B the minimum occurs when B = 3, with
slightly greater values for B = 2.

Since tristable devices are almost nonexistent, and bistable devices
are plentiful, most engineers choose the binary numeral system
rather than the ternary numeral system when they design a
machine.

Binary Representation of Information

BINARY NUMBER REPRESENTATION

If we recall the definition of our standard positional notation
whereby the meaning of a digit depends on its position relative
to other digits in the number representation, then we note that
any positive integer may be written

(/„ . . . r/.//ir/o = r/o/i" + (JiB' + f/,/i' + . . . + daB"
where i:? > 1 is the base of the number representation and where
0 ^d,< B.

For example, if Z? = 10 the integer "fifty-seven" may be written
57 = 7 . 10' + 5 . kV
- 7 + 50.
\i B = 1 then "fifty-seven" becomes

Llll0011,,vo = [2'^ + 2' + 2' + 2i,,.„
= [1 + 8 + 1(> + 32],,„

where the subscript "two" indicates that binary notation is used
on the left of the equal sign and the subscript "ten" indicates
that decimal notation is used on the right.

Similarly, any positive fraction (less than one) may be written

OV/_if/_or/^3 . . . d-,n = (UB-' + r/_o^-' + d.^B'^ + . . . + r/_„,fi""',
where m does not have to be finite and 0 ^ «'_, < B. For example,
if /? = 2 then fi\e-sixteenths becomes

[0.0101]uvo =

2"' + 2-
1 + i,' .

.4 IbJten

Since positive numbers may be decomposed into an integer part
and a fraction part we may consider, as a more general example,
the binary representation of thirty-seven and nine sixty-fouiths.

[I00101.0010011t„,, = [2" + 2' + 2'

,„ + [2-^ + 2^^J

[1 + 4 + 82],,„ +

1+^
L8 ()4J

[37]ten +

!)
L()4jt

It is not our intention to go into great detail at this point and
discuss the procedures for converting from one number representa-
tion to another. We have merely tried to review, by means of

30 Information Storage and Neural Control

three examples, the well-known fact that positive numbers are
easily represented by a pattern of binary digits, that is to say,
by a pattern of "zeros" and "ones." (Binary digits are commonly
called bits, for short.)

Before continuing, it is necessary to recall that negative numbers
also have representations in terms of a pattern of bits. To demon-
strate this let us discuss methods for negative number representa-
tion inside a computer. The following systems are currendy used:
[1] Signed absolute values

[2] Complements with respect to some integral power of the
base

[3] Complements with respect to one less than some integral
power of the base.

The first method is simple — the machine contains the absolute
value of each number stored, with an indication of its sign. The
second and third methods involve number representation modulo
B'' and modulo (^^-1), respectively, where B is the number base,
and the machine registers are assumed to hold k digits.

To illustrate system [2], let /: = 9 and assume a decimal machine.
Thus, if we use the symbol = to mean "is represented by," then

126 « 000 000 126
and

-126 « 999 999 874,
since

-126 = 999 999 874 (mod 10').
Using system [3] we have

-126 « 999 999 873,
since

10' - 1 = 999 999 999,
and

-126 = 999 999 873 (mod 999 999 999).

System [3] is sometimes called the "nines complement" system
when B is ten. This is motivated by the fact that one merely takes
a digitwise complement with respect to nine in forming 999 999 873
as the negative representation of 126. System [2] is called the

Binary Representation of Information 31

"tens complement" system when B is ten, since the least significant
digit is actually complemented with respect to ten.

In a nine-digit binary machine using "ones complements," we
would have

[126]ten « 001 111 no,

and

[-126]ten « 110 000 001.

Thus we have reviewed, by means of examples, how both positive
numbers and negative numbers may be represented easily by a
pattern of bits.

PROCESSING BINARY INFORMATION
INSIDE A COMPUTER

Let us consider a typical modern high-speed computer which
is designed to process binary information in blocks of 48 bits.
Such blocks are called words, and we describe such a computer
as having a 48 bit word length. These words may be bit patterns
representing numbers (we discussed binary representations of
numbers in the previous section) or the words may be bit patterns
having a non-numerical interpretation. To the machine this is
immaterial.

The repertoire of machine commands for carrying out operations
on machine words includes commands for performing the basic
arithmetic operations of addition, subtraction, multiplication, and
division. More complicated mathematical tasks, such as the ex-
traction of square roots, solving algebraic equations, and so on,
are accomplished by using a combination of these basic commands.

In addition to the commands for performing basic arithmetic
operations, the machine is capable of executing commands which
perform operations of a non-numerical character. These are the
commands that make the modern electronic digital computer a
versatile information-processing instrument rather than just a
high-speed computing instrument.

We begin our discussion of non-numerical type commands
(although some of these may have a numerical interpretation
as well) by mentioning the shift cortimands. Consider the bit pattern
consisting of ones in the odd numbered positions and zeros in

Information Storage and Neural Control

the even numbered positions. We shall write the word in the form

101010 . . . 101010,

where the meaning of the three dots is obvious, and their use
enables us to avoid writing" all 48 bits.

A left shift of n bits causes the individual digits to be shifted
to the left n places in an "end-around" fashion, which means
that bits shifted off the left end are carried around and introduced
into the right end of the word. Thus, if n is an even integer, the
pattern displayed above will not appear to have changed following
the shift. On the other hand, if n is odd, the pattern will have the
appearance

010101 . . . 010101
following the shift.

This shifting operation can be quite useful. To illustrate this
we need to mention that the machine is capable of performing
branchmg operations, i.e., it can be made to do one thing if the first
bit of a word is a "one" and another thing if the first bit is a
"zero." This means that the machine is capable of performing
each of two sequences of operations depending on the nature of
the first bit of a word. Figure 1 will aid us in this discussion.

Fig. 1

If lines represent sequences of operations then we traverse the
path AB if the first bit of our word is a "one" and AC if the first
bit is a "zero." If we assume that we shall begin at point A many
times and if we desire to traverse the paths AB and AC on alter-
nate occasions, then we can use the word

101010

and the left shifting operation to do this. All we need to do upon
arrival at either of the points B or C is to command the machine

Binary Representation of Information

to perform an odd number of left shifts. This will cause the first
bit of our word to be alternately "one" and "zero."

Other examples of useful commands include commands to per-
form several logical operations. In order to describe a few of these
commands, reference will be made to Table I.

TABLE I

Logical Operations

O Logical Product
10 1 = 1
10 0 = 0
0 0 1 = 0
0 0 0 = 0

® Exclusive "Or'
1 ® 1 = 0
1 ® 0 = 1
0 ® 1 = 1
0 ® 0 = 0

For example, if we have the two words

A 10101010

and

Q, 11001100
we can generate the word

M 10001000

10101010
11001100

10001000

by performing the bit-by-bit logical product of the two words A
and Q,, that is to say, we can form A O Q = M.
If we have the two words

A 111000111

000111000

and

M 101010101 . . . 010101010
we can replace A by M © A giving

A 010010010 . . . 010010010
and as a final example, we can replace A by A © M giving

A 111010111 . . . 010111010

These examples merely illustrate the kinds of bit manipulation
that are possible, and no attempt has been made to be exhaustive.
As one gains experience in using such commands it is possible to
discover how versatile this new instrument is as an information
processor. Research workers from many diverse fields are con-
stantly finding ways to apply such machines to their problems.

CHAPTER
III

INFORMATION PROCESSING THEORY

Robert K. Lindsay, Ph.D.

'ESCARTES is usually credited with introducing the mind-
body problem to psychology. What he did was introduce the body
to psychology. In those days, of course, there were no card-carrying
psychologists, but there were many people who were interested
in the huinan thought processes, which were assumed to reside
in a mysterious nonentity called the mind. Descartes wished to
show how the mind influenced the motions of the body, and in
so doing he made some guesses as to how the body itself might have
something to do with decision making. The abstracted description
of the control mechanism which Descartes provided sounded much
like a description of the inechanical statues which were found in
the gardens of his day. He described nerves as hollow tubes
through which ran bell ropes of the sort used to summon servants.
These bell ropes, when stimulated, manipulated valves in the head
which directed the flow of animal spirits from the ventricles of
the brain to the inflatable muscles. The expansion of the muscles
brought about movement. The mind was, in this model, adjoined
to the body through the pineal gland, which served as a sort of
master control which could override any of the other valves, thus
maintaining the integrity of the free will.

Descartes' system, though somewhat obsolete today, was, in its
time, quite ingenious. Even though men are no longer profitably
viewed as garden decorations, Descartes and his notions can be
credited with having thrown a great deal of light on the working
of the human control system.

Although advances in physiology have shown the preceding
model to be inadequate, such knowledge has not eliminated the

Injormation Processing Theory 35

approach. Students of behavior still exhibit a propensity to describe
the human system in terms of the engineer's handiwork. In the
first half of this century, and still today, psychological models
took a form remarkably similar to the telephone switchboard, with
incoming signals being routed through connections, strengthened,
by degrees, through use, to trigger a response, with scarcely a
"by-your-leave" to their brothers under the skin. Psychology
moved back the boundaries of the mind as emphasis withdrew
from the mechanical procedures which performed the motions,
and moved toward the control procedures which decided what
motions would be made. The mechanical monster seemed too
clumsy, and an electrical monster was substituted.

As the preceding papers have indicated, the recent years have
seen some new tools, both conceptual and actual, added to the
engineer's gadget bag. These years have also seen some further
friendly borrowing of these tools by students of biology and
behavior. The new tool with which I am most impressed and
with which I hope to impress you is the digital computer. A great
deal of work has been directed in the last decade toward the
understanding of the neural bases of the control processes which
interest the psychologist, and a fair number of psychologists have
decided that switchboards are perhaps not the best model for
neurological processes. So now we hear that people are really
like electronic monsters.

We are not quite as physiologically naive as was Descartes.
We know that humans are not really made up of transistors,
resistors, or even electric wire. What, then, do we mean when we
say that humans are like computers?

Humans and other animals make decisions, behave, solve prob-
lems, and learn. Machines — digital computers — also do these
things. Superficially, at least, humans are like machines. C'an we
be more specific? If a machine does the same things that a human
does and fails in the same things in which humans fail, then
machine and man are alike at a more basic level of description.
The more details which can be replicated by the machine, the
closer is the comparison.

Once the basic features of a computer are pointed out to some-
one who has seriously attempted to analyze the human system,

36 Information Storage and Neural Control

some similarities are obvious, as are some points of dissimilarity.
A legitimate question yet remains: How does the existence of a
potentially remarkable device of this sort aid us in our present
work? There are at least two answers to this question.

The first answer is exemplified by the work of those who have
studied the brain as a computing machine. Turing (1) proved
that a very simple device is capable of computing any number
which a reasonable man might wish to call computable. In a
classic paper, McCulloch and Pitts (2) argued that, since mathe-
matical logic has been stated in a form where deductions become
a form of computation, a device of no greater complexity than a
Turing machine should be capable of performing any logical
computation, no matter how complex. They, in fact, proved that
elements no more complex than neurons were sufficient for this
purpose. That is, they demonstrated that to every logical proposi-
tion there corresponds a nerve net which can be constructed from
idealized neurons, and that the converse is also true. The brain,
thus, is not just in some vague sense like a computing machine;
the brain is a computing machine. The important activity of the
brain is its inputting, processing, and outputting of information.
Although we have had these computing machines — brains — around
for a long time, only recently have we had any others of comparable
complexity. To biology, the presence of the digital computer has
provided, in addition to a new source of interested human talent,
a manipulatable device which can be studied in vivo and whose
descriptors, as they are discovered, might profitably be applied
to the human machine. Studies of electronic systems, and of sys-
tems in general, have provided insights into some important
biological questions. To mention just one such question which
has received a lot of attention: How is it possible to construct a
reliable system out of billions of variable, unreliable parts? This
question has been attacked profitably by McCulloch (3, 4) and
von Neumann (5), among others.

The second answer is the one on which I wish to dwell more
extensively. It is frequently the case that, although we know the
properties of all components of a system, we are unable to predict
the behavior of the system if it is composed of many components.
It is true that not all of the relevant properties of neurons are

Information Processing Theory 37

known in sufficient detail. However, some of their basic features —
features which undoubtedly are critical in brain function — are
well established and can be described accurately. A lot of talent
has gone into speculating on the manner in which neurons inter-
act to perform the higher functions. One such theory is that of
the psychologist Hebb (6), who attempted to explain the phe-
nomenon of memory in terms which were physiologically sound
and yet psychologically relevant. Hebb proposed three phases in
the formation of memory traces. The first is reverberation, the
persistence of nervous activity after the termination of the initiating
stimulus. The second mechanism, the cell-assembly, consists of a
characteristic pattern of firing associated with a particular stim-
ulus configuration and comes into being upon adequate repetition
of the stimulus. To account for this, Hebb postulated that if one
neuron succeeded in firing a second, the synapse, by some un-
specified processes, should change so as to make this triggering
more probable in the future. The third mechanism, evolving from
the second, amounts to the passing of activity from one cell-assem-
bly to another as a result of the repeated temporal sequencing of
the corresponding stimuli. This mechanism, the phase-sequence,
is the primitive basis of expectancy, an important psychological
concept.

Although the neuronal properties which Hebb assumed are
well established, and the "growth" hypothesis is almost certainly
correct, it is not an easy task to show that these assumptions are
sufficient to cause the reverberation, cell-assembly, phase-sequence
organization postulated. Rochester et al. (7), attempted to demon-
strate the sufficiency of Hebb's assumptions in a novel way. They
instructed a digital computer to behave according to the assump-
tions, and then simply observed its behavior. It is interesting to
note that they were forced to make some additional minor assump-
tions before the theory was specified in sufficient detail to be
realized. But more important, they found that, although rever-
beration was easily achieved and cell-assemblies formed spon-
taneously after some suitable modification of the theory, phase-
sequences were not achieved. This work has given some important
clues as to what is lacking in the theory, and some specific altera-
tions have been proposed.

38 Information Storage and Neural Control

We see in the works of McCulloch and Rochester a feature
which distinguishes them from many other efforts at describing
brain activity and behavior. This new approach may be con-
trasted with many behavior theories which describe the product
or output of the system rather than the process by which the
output is obtained. Akhough it is perfectly reasonable to develop
a science of psychology from product models, psychological
theories would be more directly useful to neurophysiology if they
could be stated as process models. The absence of analytic tech-
niques and languages for describing processes has until recently
blocked any rigorous development of psychological process models.
The development of computer sciences offers hope of removing
these blocks.

In order to provide a clearer picture of what is meant by the
infoimation processing theory approach, I wish to contrast a
process model with three other types of theoretical descriptions.
All four of the models to be discussed deal with human language
production. The three non-process theories, in fact, purport to
explain exactly the same phenomenon; unfortunately, the infor-
mation processing model does not. However, I think the exposition
will not suffer excessively from this lack of aesthetics.

The phenomenon described by the three non-process models
is the strikingly regular statistical distribution of words produced
in speech and writing. The data are most often displayed in what
is called the standard curve, which is obtained as follows. A
passage of text is examined to determine which word occurs with
greatest frequency, which with second greatest frequency, and so
on. A graph is then made, with this rank plotted on the abscissa
and frequency of occurrence on the ordinate. Thus, if "the" is
the most frequent word, and if it occurs one thousand times, then
the point so determined is (1, 1000). Such graphs, made from a
wide variety of sources, are well-approximated by the equation

Jr =C,
where

/ = frequency,
r = rank, and
C = Constant.

Information Processing Theory 39

If the curve is plotted on log-log coordinates, this rectangular
hyperbola becomes a straight line with slope of minus one. The
above equation is equivalent to

nf = K,
where

/ = frequency of occurrence,
n = number of words of that frequency, and
K = Constant.

This form is the more usual representation of a frequency dis-
tribution.

The first explanation of this regularity which I wish to discuss
is due to Zipf (8), and exemplifies what I will call a mentalistic
theory, although I shall try to avoid defense of that term. The
basis of Zipf's explanation is the Principle of Least Effort, which
itself requires explanation. People, says Zipf, behave so as to
minimize effort, and this strategy underlies behavior of all forms.
He is at pains to emphasize that effort includes not only actual
work but mental effort as well, including the mental effort to
decide which path involves the least effort. And here is where the
trouble begins. Since a person is unable to predict the future
exactly, he must make guesses. The Principle then becomes the
statement that a human will behave so as to "minimize the average
rate of probable work." At this point it is clear that no problems
are solved by the Principle because, in order to make a prediction,
we must determine the subject's view of the world and understand
his decision process, which of course was the problem with which
we began.

Since this is an important point, let me state it somewhat
differently. Although Zipf provides elaborate discussion of what
he means by effort, he never gets around to telling us how it is
to be measured, nor does he ever rigorously state what is meant
by this Principle of Least Effort. Since the length of time over which
this undefined effort is to be averaged is also unspecified, it is
clear that we can adjust the foresightedness of our subjects to
obtain whatever results we desire. Since the word "probable" is
thrown in, our only recourse is to the subjective probabilities
of the subjects in order to apply the principle; and subjective

40 Information Storage and Neural Control

experience is by definition private. The Principle is seen to be an
elastic phrase which can be distorted to fit whatever data are
presented. Zipf has committed an error whicli psychologists have
been attempting to eliminate for fifty years. This is why I have
used the term "mentalistic" to describe Zipfs theory.

But let us take one quick look at how the principle is applied
to the word-frequency results. Zipf argues on teleological grounds.
From the viewpoint of the speaker's purpose, communication
would require least effort if one word could be used to convey
every meaning, for then no decision would be necessary. From
the auditor's viewpoint, every meaning should have a separate
code, for then his effort would be least. Thus, we have the Foice of
Unification in opposition to the Force of Diversification, tending to
create a vocabulary balance. I wish to quote the following passage
from Zipfs book as an example of the application of the Principle:

"We obviously do not yet know that there is in fact such a
thing as vocabulary balance between our hypothetical Forces of
Unification and Diversification, since we do not yet know that
man invariably economizes with the expenditure of his effort; for
that, after all, is what we are trying to prove. Nevertheless — and
we shall enumerate for the sake of clarity — if 1) we assume ex-
plicitly that man does invariably economize with his effort, and
if 2) the logic of our preceding analysis of a vocabulary balance
between the two Forces is sound, then 3) we can test the validity
of our explicit assumption of an economy of effort by appealing
directly to the objective facts of samples of actual speech that
have served satisfactorily in communication. Insofar as 4) we may
find therein evidence of a vocabulary balance of some sort in
respect of our two Forces, then 5) we shall find ipso facto a con-
firmation of our assumption of 1) an economy of effort." (9).

The argument which has been presented is scientifically sound:
deduce something from theoretical assumptions; if the deduction
is empirically verified, the theory has found support. Our only
quarrel is with the weakness of the prediction. All that has been
predicted is that passages of actual speech or writing will not be
repetitions of the same single word, nor will all words be different.
The absence of detailed specification of the constructs of the theory
leads only to predictions which are trivial, and yet the superficial

Information Processing Theory 41

rigor of the statement of the Principle gives the impression that
something has been said — something which sounds very reason-
able and powerful.

The weakness of such explanations would seem not to deserve
such extensive comment, and yet the problem has come up so
frequently, particularly in psychology, that apparently it does not
hurt to point out such fallacies. Not all cases are so obvious,
unfortunately, and many theories which appear rigorous to the
most competent of scientists are found at times to fail on this
same count. I shall return to this point later.

The amazing regularity found in the word-frequency data,
regularity which seems to be so hard to come by in the field of
human behavior, deserves more serious attempts at explanation.
Fortunately, other workers have attacked the same problem; and,
fortunately, for our purposes of today, one such approach illus-
trates a stochastic theory and another illustrates an application
of information theoretic concepts.

The stochastic model is due to Simon (10). The approach is
to postulate probabilistic decision rules and from these to derive
the statistical properties of a device which follows the rules. The
challenge is to postulate rules which will yield the statistical
properties of the observations, in this instance the frequency
distribution of words. It should be pointed out that the weaker,
i.e., the more general, are the underlying postulates, the better
is the theory. Thus, as in any theory, we wish to account for as
much as possible with as little as necessary.

Simon's basic model rests on only two assumptions. From these
he is able to derive a frequency distribution known as the Yule
distribution, which has all of the properties required for fitting
the word-frec|uency data. As a matter of fact, slight variations
on the assumptions yield slight differences in the resulting dis-
tribution. These various forms of the theory can be plausibly
associated with various real world situations, and the theory thus
accounts for several phenomena, such as the distribution of authors
by number of professional papers published, the distribution of
incomes, and the distribution of biological species by genera.
Furthermore, the steady state statistical properties are fairly insen-
sitive to minor changes in the assumptions.

42 Information Storage and Neural Control

With regard to word production, Simon argues that an author
selects words not only by association with other words he has
already put down in this passage but by imitation of the language
as well. In other words, the next word which an author will choose
is determined by the frequency distribution of his present effort,
i.e., the subject under discussion, and by the statistical properties
of the language he is using. We thus need to postulate two "birth
processes." Further, we will consider a passage of fixed length,
as is reasonable if we consider that our sample under analysis is
but a segment selected from the author's total word production.
Thus, we must postulate a "death process" which specifies which
words are dropped from the sample at one end of the passage as
we add words at the other end.

Let (3 be the proportion of words added by imitation, and let
f*{i) be the relative frequency of words which have occurred
exactly / times each. The following assumptions shall prove to
be sufficient.

Birth Processes

i. The probability of adding a word, already having occurred

i times, by association is {\-^)i f*{i)-
ii. The probability of adding a word, already having occurred
i times, by imitation is ^{i-c) /*(i).

Death Process

iii. If a word of frequency i is dropped, all instances of that
particular word are dropped; this occurs with proba-
bility /*(z).

Note that in both birth processes, the probability of adding a
word is proportional to the total number of occurrences of that
word and all other words used equally often. The assumption
that a word will be chosen with probability proportional to its
own frequency is a special case of our assumptions i. and ii.;
hence, the assumptions used are more general. The factors in-
volving jS are self explanatory. The constant, c, appearing in
assumption ii. may be made plausible by the following considera-
tions. In any given passage, not all of the words in the language
will have been used yet. We wish to allow the possibility that

Information Processing Theory 43

a word never before used in this sample will be produced. Thus,
we wish to attenuate slightly the probabilities attached to the
association process.

The death process, though intuitively less satisfactory, would
be true if all occurrences of a given word were closely grouped as
if associated only with the topic of the moment. Thus, if a page is
removed from the sample, it is likely that all occurrences of every
word on it will be removed.

Since we are discussing a sample of constant size, we may
write an equation to indicate that total births are balanced by
total deaths. But, furthermore, we are concerned with the so-called
steady state of this stochastic mechanism. This is the situation
which persists when the sample size is large enough that the
statistical distribution remains invariant. Thus, the number of
words dropped from the category witli relative frequency f*{i)
must be just balanced by the number of words entering that
category, which is the number of births in the category with
relative frequency /*(/-l). These requirements enable us to write
the following equation

births in (z — 1) minus births in (z) minus deaths in (0 = 0

(I-/3C-1) j*{i-\) - (i-^c) f*{i) - f*(i) = 0

which may be rewritten as

/•«=(:5^)/-o-i)

thereby recursively defining the desired quantity. This function
has the required properties.

In the example of the stochastic theory, then, the assumptions
are probabilistic decision rules and the deductions are made
analytically.

The third description of language production is due to Mandel-
brot (11) and exemplifies the application of information theoretic
concepts. Superficially, this approach is similar to that of Zipf,
for Mandelbrot derives the equation for the standard curve by
minimizing the cost of coding the speaker's ideas into words,
subject to the constraint of a fixed amount of infoimation trans-
mitted per word- However, Mandelbrot is quite specific as to
what he means by both information and cost.

44 Information Storage and Neural Control

Zipf argued that, while the speaker's effort (cost) would be
least if only one word were used, this situation does not persist
because the listener's decoding" efforts would be too great. Infor-
mation theory allows us to put this notion on a sounder base.
You have seen that a message which is always sent can convey
no information, and that the larger the vocabulary, or set of
alternatives, from which messages are selected, the greater is the
information which they convey. On the other hand, the process
of deciding which message is next to be sent is also more difficult
when the set of messages is larger. Mandelbrot has proposed that
the balance of these two factors may be conceived as the basis for
word statistics, and in this we see the similarities with Zipf. How-
ever, Mandelbrot has employed a specific definition of infoimation,
and has rigorously defined the probleni. Let us examine the main
features of his derivation for a problem which is formally identical
with the one stated above: Given a fixed average cost per word,
what will be the frequency distribution of the words to give inaxi-
mum information per word?

Let Cr be the cost of the r-th most frequent word, which occurs
with probability p^. Average cost per word, C, is then

C = X VrCr.
r

Also,

must hold. The problem is then to maximize

H = -IZ Vr log Pr

subject to the above conditions.
When this is done, it is found that

Vr = AAI

where A, B, and M are constants which have interpretations in
terms of the coding process. One further step is needed in order
to complete the derivation, and this involves relating C,- and rank, r.
Mandelbrot has managed to show that if words are coded
"optimally," the resulting word statistics will be correct. Suppose
that words are coded from' some elementary units. These units

Information Processing Theory 45

are undefined, being like, perhaps, phonemes, but not necessarily
so identified. It is only necessary to assume that words are com-
posed of these units and that the cost of a word is equal to the
sum of the costs of the units. To illustrate, take the special case
where each unit has the same cost attached to its use. The least
expensive words are tlien the ones composed of single units. If
there are M units, there are M such minimum cost words, M~
double unit words (second in cost), and so on.

The rank of a word will be determined by the number of words
which can be coded with Cr or fewer symbols. For example, if
words are coded as binary sequences, M = 2, and there are
fourteen codes of three or fewer digits (0, 1, 00, 01, 10, 11, 000,
001, 010, Oil, 100, 101, 110, ill). Thus a word of cost 3 will have
rank Hand )•{?>) = 14.

In general,

c c

r{Cr) =i:ii/^=i:ii/^- 1

Cj.

+ 1

1 -

- M

1 -

- M

(71/-

1 - 71/
1 - M

so that

M - 1

71/^' - 1 = /• (71/ - l)iW-'

Now,

Cr = Cr log,/ 71/

= log,; [(71/'''- 1) + 1]

= log,/ [(7I/'''- - 1) + .^7 (71/ - l)-\/M {M - !)-']

, r + 7l/(J/-l)-'
= log,/

71/ (71/ - ir'

= log,/ (/• + M {M - I)-') - log,/ .1/ + log,/ (.1/ - 1)
which is of the form

Cr = log,/ (/• + m) + /
where m and jn are factors independent of r. Mandelbrot shows
that the general form of the expression for CV \s> the same no matter

46 Information Storage and Neural Control

what coding rules are assumed, provided that the coding results
in a ranking of words by cost.

Substituting this last equation in our first result yields
Pr = P ijr -\- my ,
which reduces to the standard equation when w = 0 and B = — 1 .
The additional parameters allow closer fit; but since each has a
"physical interpretation," we are not really cheating.

My purpose is not to compare the adequacy of these three
particular theories — this has been argued elsewhere: Simon (12,
13, 14), Mandelbrot (15, 16), Rapoport (17) — but to contrast
the theoretical style. The procedure of Mandelbrot, then, is to
start from ceitain assumptions and to deduce the resulting prop-
erties. In his case, the assumptions were stated in information
theoretic terms and the deductions were analytic.

My final example illustrates the information processing ap-
proach. Unfortunately, as I rhentioned earlier, it does not deal
with the same data, although it is concerned with verbal pro-
duction. Hence, we may contrast the underlying notions even if
we cannot compare theoretical validity.

This description is due to Yngve (18) who has attempted to
explain some of the salient features of English grammar. As a
starting point, Yngve has pointed out that English often provides
several grammatically correct and semantically equivalent ways
of saying the same thing, and that some of these ways are quite
complicated. On the other hand, the grammars of formal mathe-
matical notations, such as that of algebra, impose severe limits
on the number of forms permitted, and yet these restrictions do
not hamper expressive power nor limit "sentence" length. Let
us consider just two examples. In English the standard form of
modification places modifiers before that which is modified. Thus,
we have such phrases as "the big, happy man." But we may also
reverse this order — which logically should be completely ade-
quate— in such phrases as "a man as tall as a circus giant." Why
do we not avoid such discontinuous constituents (some modifiers
in front, some behind) and use the more consistent form "an as
tall as a circus giant man" or "an as a circus giant tall man"?

Secondly, note that English provides both active and passive
voices: "Johnny gave the ball to Billy" and "The ball was given

Information Processing Theory Al

to Billy by Johnny." Surely, since both are equivalent, we are
just complicating things by allowing two grammatical forms. In
algebra we do not have a symbol \ as in "B\A," which means
the same as "A/B", but we may say in English "B divided into
A" as well as "A divided by B."

To explain these and many other aspects of English grammar,
Yngve postulates a mechanism for sentence production. Assume
that the brain has a large memory in which are stored rules such
as S = NP + VP; NP = T + N; VP = V + A; T = the; T = a;
A = away; V = went; V = ran; N = man. Such rules define a
grammar in that they can generate sentences if used in the fol-
lowing fashion:

S = NP + VP

-T+N+V+A
= the man went away
or S = a man ran away

By selecting various rules, we may generate various sentences, all
grammatical.

However, in order to generate sentences in the prescribed left-
to-right fashion, it is necessary that we complete the expansion
of the left-most phrases while "keeping" our place," i.e., remem-
bering the higher order rules which are guiding" the sentence
production. If we had a scratch pad on which to keep our place,
its contents at various staoes mio'ht look like this:

Verbalized

On scratch pad

NP VP

TN VP

the N VP

the

N VP

the

man VP

the man

V A

the man

went A

the man went

away

man went away

48 Information Storage and Neural Control

CUearly, the type of grammar rules will determine both how
much we have written down at any time and the maximum
capacity required of the scratch pad. Since rules may be used
recursively, i.e., we permit rules such as S = S + and + S, we
might generate grammatical sentences which exceed the capacity
of any given scratch pad. This is not such a danger in algebraic
notation, which is not generally used as a spoken language except
for short expressions, but it could be critical in spoken English.
Since human span of attention is quite limited — and we have
some pretty consistent evidence as to what this limit is — English
has evolved rules of grammar which spare our mental scratch pads.
For example, we can see that elaborate phrases which occur at
the beginning of a sentence must be expanded while keeping in
mind the structure of that which is to follow. Grammarians ad-
monish us not to use such "top-heavy" sentences. It is not sur-
prising to find that we have been provided with alternate ways of
modifying nouns, and that these ways allow us to postpone some
of the modifiers until we have gotten rid of the object of modi-
fication. Discontinuous constituents are such mechanisms.

The same argument accounts for the existence of the passive
voice when the active is just as accurate. If the subject of a sentence
is greatly elaborated, we can postpone it until later by making it
the predicate of a sentence in the passive voice. Note how the
following sentence, used as an exaniple by Yngve and taken from
a U. S. patent, organizes the information so that one need not
expand the middle while keeping in mind other features: "The
said rocker lever is operated by means of a pair of opposed fingers
which extend from a pitman that is oscillated by nieans of a crank
stud which extends eccentrically from a shaft that is rotatably
mounted in a bracket and has a worm gear thereon that is driven
by a worm pinion which is mounted upon the drive shaft of the
motor." The same sentence can be expressed in the active voice,
but this requires a memory which is beyond the capabilities of
most of us. The sentence is ungrammatical for that reason, accord-
ing to Yngve's model: "A pair of opposed fingers (that extend
from a pitman (which a crank stud (that extends eccentrically
from a shaft (which is rotatably mounted in a bracket and which
a worm gear (that a worm pinion (which is mounted upon the

Information Processing Theory 49

drive shaft that the motor has) drives) is on)) osciUates)) operate
the said rocker arm."

Tiie preceding examples, tiien, represent four approaches to the
same general type of observation. I have called them mentalistic,
statistical, information theoretical, and information processing
theoretical. The latter consists of postulating" some sort of mechan-
istic decision procedure; the operation of the mechanism is then
examined and compared with human behavior. Assumptions are
stated as processes; the method of deduction is not analytic. For
processes more complex than that in the Yngve example, the de-
duction often takes the foim of specifying the processes for a digital
computer, the running of which then provides the predictions.

There are yet several points of this discussion which deserve
more elaboration. First, why go to all the trouble and expense to
build and instruct this device when we might do better to hire a
mathematician, whose services are certainly cheaper, to solve the
problem analytically? This is certainly a good suggestion, and
many people who have resorted to simulation might better have
resorted to mathematics. But the systems which are of major
interest to the psychologist and biologist have the property of
being complex. Mathematics, although it has earned its place of
respect in science, is not a completely developed discipline. The
task of writing equations for the human system is far too difficult.
Some attempts have been made to describe mathematically cer-
tain learning processes, for example. Bush and Mostellar (19),
Estes (20); but it has been necessary to limit the complexity of
the equations in the interest of getting them solved. Learning
processes have pretty well resisted linear descriptions. It is, how-
ever, possible to define in computer terms systems which cannot
be defined in normal mathematical notation; and if the system
can be defined as a computer program, a computer can simulate
the behavior of the system. It is important to realize that writing
a program is analogous to writing an equation, and running the
program is analogous to solving the equation. It is then clear
what I meant when I said that the program is a theory: it is a
theory in the same sense that a mathematical equation is a theory —
it makes some well-defined assumptions and makes some predic-
tions which are rigorously deduced from these assumptions.

50 Information Storage and Neural Control

With this analogy in mind, it is easier to elaborate on the other
points. One may argue that having discovered one set of opera-
tions which accounts for the behavior of a computer system does
not assure us that the same set of operations is involved in the
human system. This is a truism which also applies to mathe-
matical theorizing; that is to say, more than one equation can
fit the same set of data. Ultimately, we must live with this prob-
lem, for if a theory accounts for all data within its domain, then
it is as good as a theory can be even though there is no assurance
that its underlying assumptions have any basis in reality. Such
considerations have forced philosophers of science to conclude that
reality has no meaning; we can only ask if the assumptions work,
not if they are real. The job of the scientist is that of the inven-
tor who creates descriptions, not of the explorer who discovers
reality.

Even leaving this ultimate state aside, it is important to con-
tinue on this same point, but at a more practical level. If we have
a program which accounts for a small segment of human behavior,
how have we progressed? Seldom are we satisfied with a theory
of small segments of behavior. Let us expand our program until
it is more encompassing. If this can be done by making use of
some of the same postulated operations, we achieve the parsimony
which we seek. Let us look at programs written by other people
to describe other things. If they consist of markedly similar por-
tions, then we again have made progress. Eventually, when a
certain process or feature has turned up frequently enough as an
asset, we may forget our philosophy and begin to look within the
human system to see if we cannot find independent evidence for
the existence of some such process. We have thus generated two
types of hypotheses: those which make predictions about similar
types of behavior, and those which give us clues about the com-
position of the organism. I shall return to some examples of the
latter at the conclusion of this paper.

The third point on which I wish to elaborate is a matter of
practical research strategy. The process of simulation provides an
important fringe benefit which becomes apparent only after trial.

It has long been a feature of psychological theorizing that
would-be theories suffer from chronic vagueness. The result is

Information Processing Theory 51

a theory which can be stretched to fit anything. The genesis of
this difhcuhy lies in the fact that the theorist knows what he is
saying and so does his audience. Hence, it is often possible to put
together assumptions which, logically, will not fit, or to make
deductions which, logically, do not follow. These unfortunate
juxtapositionings may go unnoticed by an intelligent theorist and
his informed listeners, who can readily and unwittingly supply
the missing pieces, ignore the excesses, and beg the answer which
they know is there even if it is not. The computer, though, is a
very stupid audience. From one point of view, it may prove more
valuable now while it is stupid than later when it is not; for today
it will not tolerate vagueness. When a theorist with an idea sits
down to convey his idea to a inachine he almost invariably finds
that he must first sharpen it up. And when the machine attempts
to simulate the idea, the theorist almost invariably finds it will
not do what it is supposed to do.

These lengthy elaborations on a fairly concise statement point
up the similarities between the process of computer simulation
and the other techniques of theory construction. The computer has
not answered the many problems which were formulated by these
other techniques. The computer will not make scientists out of
programmers. It is just another way of theorizing which has
certain special advantages, certain special disadvantages, and the
same old problems.

1 have attempted to show how process models may be stated
and why computer simulation is often an appropriate means for
their analysis. It is quite legitimate to ask what such efforts to
date have implied about information storage and neural control
or, to be more classical, neurophysiology. When computer sci-
entists discover processes which appear to be useful building
blocks for explaining human behavior or for constructing artificial
intelligences, it is natural to ask if actual mechanisms for per-
forming these processes can be found within the central nervous
system. The observations of the reflex led Sherrington to inquire
as to its basis, with a great deal of benefit to science. Pavlov ex-
amined the conditioned reflex and based his psychology on it.
The discovery of more complex processes could likewise direct
efforts in neurophysiological research.

52 Information Storage and Neural Control

Of course, such procedures are dangerous, and I hesitate to
make any very strong suggestions. The danger lies in the fact that
a theory which embodies an hypothesized mechanism, Hke any
otlier theory involving an assumption, can only prove the suf-
ficiency of the hypothesis, not its necessity. Anyone who accepts
directions from a psychologist runs the risk of getting" lost. None-
theless, I will indicate a few possibilities based on mechanisms
which have been found useful in psychological and computer
theory.

One observation which has proved highly important to psy-
chological process models has appeared in the preceding discussion
of Yngve's hypothesis. I refer to the concept of a limited scratch
pad, or immediate memory, as it is called. It has often been recog-
nized that permanent memory can persist even after severe dis-
turbance of the ongoing cerebral activity, such as that brought
about by freezing or electroshock. Since any form of persistent
trace must undoubtedly require periods of time, at least on the
order of seconds, for establishment, then some temporary form of
storage, basically different from the permanent form, must be
utilized to maintain the information until it can be permanently
stored. Miller (21) has shown that the capacity of this immediate
memory, as inferred from a variety of psychological studies, is
remarkably constant. This capacity is not measured in bits of
information, however, but in terms of the number of symbols
which can be temporarily remembered; i.e., a subject may retain
about seven binary digits, about seven decimal digits, or about
seven monosyllabic adjectives, all of which differ in amount of
information as defined by Shannon. Thus, the hunian is capable
of conceptually complex activity largely because he is capable of
dealing with informationally rich symbols, and he is provided
with a capacity which is largely independent of the richness of
his thoughts.

By measuring a subject's success at discriminating various
numbers of stimuli which differ along one diniension, one finds
that the capacity of the human communication channel is rela-
tively constant at about seven discriminations. If one then gives
the subject the task of discriminating stimuli which vary on two
dimensions, one discovers that the subject, although unable to

Informatioti Processing Theory 53

distinguish forty-nine categories, can do better than in the one-
dimensional case. For example, a subject who can discriminate
wiiich of ten positions a point occupies on a line cannot place the
point in one of one hundred cells of a square, but can manage
only twenty-five. This is just what would be predicted if ten cells
of immediate memory were divided into two groups of five. In
other words, the compound discrimination reduces the accuracy
of discrimination for each dimension, but still allows independent
examination of each.

The question arises as to the underlying neurological structure.
Is there a single set of pathways which performs this function for
all inputs including internal inputs? It seems unlikely, though not
impossible, that such a set of pathways is localized in one geo-
graphic position in the brain; but even if it is diffusely distributed,
as are other memory functions, one may still ask if one set serves
in common. Little work of the kind summarized by Miller has
been done on cross-modality studies, but one wonders if there is
a "final common path" for all sense modalities.

Cllosely related to the notion of informationally ricii symbols
is the concept of a hierarchically organized memory. It is fairly
clear from both logical and psychological considerations that
nriemory organization is such that one trace can evoke a number
of others, each of which can in turn evoke a number of others,
and so forth; i.e., one trace is associated with several others, and
any one of them can be elicited without eliciting the otiiers. Such
structures liave largely been ignored in classical stimulus-response
models, where the theories have been concerned with the forming
of a single association between two traces. Neurophysiological
theories, perhaps reflecting the concern of the psychologist, have
concentrated on exploring the method of single associations. Some
meaningful questions might be asked as to the adecjuacy of linear
neurological models for explaining hierarchical structures.

One such question is related to the concept of set, which has
been found extremely useful, if not necessary, in psychological
theories, and which has turned up under a variety of names with
only minor variations in meaning. It is recognized that a subject
can be "set," by instructions or by other experimental manipula-
tions, so as to give responses of a certain class, to perform operations

54 hiformation Storage and Neural Control

more quickly, or to overlook completely otherwise obvious solution
paths in a problem situation. If one were to instruct a computer
so that it had this capability, it would be required that the set
information, given before the critical task, provide information
(or set switches) at a number of different places in the piogram.
This is generally accomplished by setting a "flag," which is tested
by various subroutines, or by setting several flags, one in each
subroutine. The result is a memory structure which might be
called diffusely localized. This type of signal must be extremely
flexible, and must be controlled by the executive program; i.e.,
it must be at a higher level in the process structure. To my knowl-
edge, no information exists concerning the cerebral mechanism
which could explain such a phenomenon, nor has anyone worried
much about it. Although perhaps other mechanisms are con-
ceivable, it seems necessary that communication channels of some
sort must exist between the higher control centers and several
lower centers, or that the nerve nets which define processes must
be constructed so that they can be rapidly, but temporarily altered
by some signal in a higher control center.

Finally, I wish to point out a feature underlying all of the
computerized brain models which deal with the learning or growth
of connections between neurons. Such models have been proposed
as the basis for such complex functions as pattern recognition
(Rosenblatt, 22); yet each rests on fairly simple and standard
assumptions of the sort discussed above in connection with Hebb's
growth hypothesis: "If neuron B fires immediately after neuron A,
the probability increases that A will fire B." Although such a
process is quite feasible, no direct physiological evidence defines
its mechanism, so the assumption remains a psychological one.
It is almost certain to be correct, and yet perhaps we should not
give up the search for alternate mechanisms — if not to replace
this notion, then to complement it. For example, the firing of
neuron A followed by the firing of neuron B might increase the
efficiency of all other connections at the A-B synapse as well. Or
perhaps the A-B "growth" takes place only if B subsequently fires
C, which bears some relation to A. The neurological mechanisms
underlying these suggestions are not so plausible as those of the
Hebb hypothesis, but if they are true they might have a profound

Information Processing Theory 55

effect on the behavior of a highly interconnected net. Here is
where computer simulations might be used to explore new pos-
sibilities. By studying the organizing effects of such additional
mechanisms, which are just as easily programmed, we might
reinitiate some originality into essentially similar models.

The fact that psychologists and biologists are beginning to
think in terms of processes in addition to stimulus-response associa-
tions and equations provides a more obvious link between their
work and that of the physiologist. It is the promise of this new
link which has revitalized discussions of cross-fertilization resulting
in conferences with titles like this one. The value of these new
conceptions remains to be seen, but it is probably safe to assume
that anything which brings our disciplines closer together can do
no harm.

REFERENCES

1. Turing, A. M.: On computable numbers, with an application to

the Entscheindungs-problem. Proc. London Math. Sac, series 2, 42:
230-265, 1937.

2. McCulloch, W. S., and Pitts, W.: A logical calculus of the ideas

immanent in nervous activity. Bull. Math. Biophysics, 5.- 11 5-1 33,
1943.

3. McCulloch, W. S.: Agathe Tyche, — of nervous nets — the lucky

reckoners. Proc. Syrnp. on Mechanization of Thought Processes, Ted-
dington, England, f959.

4. McCulloch, W. S.: The reliability of biological systems, in Self-

Organizing Systems, Interdisciplinary Conference on Self-Organizing
Systems, ed. by Yovits, M. C. and Cameron, S., New York,
Pergamon Press, 1960.

5. von Neumann, J.: Probabilistic logics and the synthesis of relial^le

organisms from unreliable components, in Automata Studies, Shan-
non, C. E. and McCarthy, J., Princeton, Princeton University
Press, 1956.

6. Hebb, D. O.: The Organization of Behavior; a Neuropsychological Theory.

New York, Wiley & Sons, 1949.

7. Rochester, N., Holland, J. H., Haibt, L. H., Duda, \V. L.: Tests

of a cell assembly theory of the action of the brain, using a large
digital computer. IRE Trans, on Information Theory, IT-2;80-93,
Sept., 1956.

56 Information Storage and Neural Control

8. Zipf, G. K.: Human Behavior and the Principle oj Least Effort. Cambridge,

Addison-Wesley, 1949.

9. Zipf, G. K.: Ibid., p. 22.

10. Simon, H. A.: On a class of skew distribution functions. Biometrika,

42.-425-440, 1955.

11. Mandelbrot, B.: An informational theory of the statistical structure

of language, in Information Theory, by Jackson, W., London,
Butterworths, 1953.

12. Simon, H. A.: Some further notes on a class of skew distribution

functions. Information and Control, J.-80-88, 1960.

13. Simon, H. A.: Reply to "final note" by Benoit Mandelbrot. Infor-

mation and Control, 4;21 7-223, 1961.

14. Simon, H. A.: Reply to Dr. Mandelbrot's post scriptum. Information

and Control, 4.- 305-308, 1961.

15. Mandelbrot, B.: A note on a class of skew distribution functions.

Analysis and critique of a paper by H. Simon. Information and
Control, 2:90-99, 1959.

16. Mandelbrot, B.: Final note on a class of skew distribution functions:

analysis and critique of a model due to H. A. Simon. Information
and Control, 4.- 198-21 6, 1961.

17. Rapoport, A.: Comment: the stochastic and the 'teleological' ration-

ales of certain distributions and the so-called principle of least
effort. Behav. Sci., 2;147-161, 1957.

18. Yngve, V.: A model and an hypothesis for language structure. Proc.

Am. Phil. Soc, 704:444-466, 1960.

19. Bush, R. R., and Mostellar, F.: Stochastic Models of Learning, New

York, Wiley & Sons, 1955.

20. Estes, W. K.: Toward a statistical theory of learning. Psychol.

Rev., 57:94-107, 1950.

21. Miller, G. A.: The magical number seven, plus or minus two: some

limits on our capacity for processing information. Psychol. Rev.,
<5J:81-97, 1956.

22. Rosenblatt, F.: The perceptron: a probabilistic model for informa-

tion storage and organization in the brain. Psychol. Rev., (55:386-
408, 1958.

PART II— INFORMATION IN BIOLOGICAL SYSTEMS

Moderator: Heather D. Mayor, Ph.D.

CHAPTER
IV

GENETIC CONTROL OF PROTEIN SYNTHESIS

Harrison Echols, Ph.D.

INTRODUCTION

kjOME ten years ago the work of Beadle, Tatum, and Horo-
witz (1) led to the famous "one gene-one enzyme" hypothesis,
which asserted that gene control over cell metabolism is exerted
through genetic determination of the structural specificity of
enzymes. I would like to discuss our present knowledge and
beliefs concerning genetic control of protein synthesis by starting
with this concept of the "structural gene" and inquiring into the
chemical nature of the gene and into the process by which the
gene controls protein specificity. Finally, I shall briefly consider
the concept of "regulatory genes" concerned with controlling the
rate of action of the structural genes.

CHEMICAL IDENTIFICATION OF GENES

It is now generally accepted that deoxyribonucleic acid (DNA)
stores the genetic information of the cell. The evidence for this
comes chiefly from work with bacteria and bacterial viruses, and
is based primarily on three types of genetic transfer experiments:
transformation, virus infection, and bacterial conjugation (2). In
transformation experiments purified DNA extracted from one
bacterial population has been shown to carry genetic information
to another bacterial population. For example, DNA from a strain
of Bacillus sub til is which possesses the ability to synthesize the
amino acid tryptophan can confer this biosynthetic ability on a

60 Information Storage and Neural Control

strain of B. siibtilis which previously could not synthesize tryp-
tophan.

Evidence that DNA is the genetic material in a DNA-protein
virus comes from studies of the infection of Escherichia coli with
bacteriophage T2. Virtually all of the DNA of the virus enters
the infected bacterium, and virtually none of the associated
protein enters. Finally, in bacterial conjugation, DNA is trans-
ferred from a donor to a recipient strain of E. coli. The amount
of DNA transferred is proportional to the number of genes trans-
ferred, again suggesting that the DNA carries the genetic in-
formation.

There is, then, excellent evidence that DNA is the genetic
storage material in bacteria and some viruses (there are ribonucleic
acid (RNA) containing viruses in which the RNA has been shown
to be the genetic material). The generalization to higher organ-
isms of this picture of DNA as the storehouse of genetic information
rests largely upon the observations that the DNA content per cell
nucleus is proportional to chromosome number; haploid sperm
cells, for example, have one-half the DNA of diploid somatic
cells (3). Further, the chromosomal DNA is quite stable meta-
bolically as befits a genetic storage unit. At present, however,
much of our belief in the idea that genes are universally DNA
comes from a feeling that nature ought to be universal about
such things as the storage and transfer of genetic information,
so that what holds true for bacteria should hold true for man.

If we accept DNA as the genetic material, we can then ask how
such a molecule stores genetic information. The simplest hypothesis
concerning this point follows from a consideration of the chemical
structure of DNA. DNA is a polymer of deoxyribonucleotides
linked together by phosphate bridges between deoxysugars to
give a sugar-phosphate "backbone" with purine and pyrimidine
side groups (Fig. la). The only topographic feature of this covalent
"primary" structure which forms a likely candidate for informa-
tion storage is the base sequence of the purines and pyrimidines.

A consideration of the probable three-dimensional structure of
DNA tends to reinforce this view. The Watson-Crick model (4)
for DNA structure proposes that the molecule consists of two
chains forming a double helix with hydrogen bond pairing between

Genetic Control of Protein Synthesis

NH..f l,Ot°'

.. T
.. T

XL...

.. C
A

T ..
A

.. T
A

r ._

-.JL

G-

C ..

[a]

[b]

Fig. 1. The Structure of DNA. (a) Part of a polynucleotide chain showing the
sugar-phosphate backbone with purine (adenine) and pyrimidine (thymine)
side groups, (b) Schematic representation of base pairing between the two chains.
The sugar-phosphate chains are represented by the parallel vertical lines and
the bases by horizontal lines, (c) The double-helix. Base pairs are represented

by horizontal lines.

the bases adenine (A) and thymine (T) and between guanine (G)
and cytosine (C) (Figs, lb and Ic). A and T are called comple-
mentary bases because of this pairing phenomenon, and, similarly,
G and C are complementary. This model is now supported by
evidence from a variety of chemical and physical experiments.
Since the double helix model reveals no new irregularities in
topography, one feels reasonably confident that the mode of
storage of genetic information in DNA is in the linear sequence
of the four bases A, G, T, C along the DNA chain. The linear
aspect of the information storage mechanism is supported by
genetic studies which indicate linearity of the fine-structure genetic
map (5), the order of mutations within a genetic region controlling
a sinsie metabolic function.

GENES AS DETERMINANTS OF PROTEIN STRUCTURE
(The Coding Problem)

We have sketched briefly the evidence that genes are DNA and
that the "genetic code"' consists chemically of the base sequence

Information Storage and Neural Control

of the DNA. Let us now discuss how a gene imparts catalytic
specificity to an enzyme. Enzymes consist of a linear chain of amino
acids (the primary structure), coiled in part into an a-helix (the
secondary structure), and folded into a compact and specific
three-dimensional structure (the tertiary structure) (Fig. 2).

I
N

0=C
H-C-

H-N
I

CH3-C-

H
I

CH,-Q

=0
H

[c3

'' lb]

ft]

Fig. 2. The Structure of Protein, (a) Part of a polypeptide chain showing the
peptide bonded backbone with side gi'oups characteristic of individual amino
acids (here alanine and phenylalanine), (b) Schematic representation of the
a-helix showing the hydrogen bonds required to maintain it. (c) The folded
polypeptide chain in myoglobin providing the specific three dimensional struc-
ture of tlie protein (as determined by the x-ray crystallographic work of Kendrew

and collaborators) (19).

The working hypothesis for the past few years concerning gene
control over protein specificity, usually called the sequence hy-
pothesis (6), states that the base sequence of the DNA specifies
the primary structure of the protein — the sequence of amino acids.
The original argument was based primarily on two points: first,
the base sequence of DNA is linear, and the only corresponding"
linear object in the protein is the amino acid sequence; second,
since proteins diff'er widely in amino acid composition, it was
difficult to see how such differences could arise other than by
genetic specificity. The argument is now much stronger. A number

Genetic Co?itrol of Protein Synthesis 63

of substitutions of one amino acid for another have been found in
the abnormal hemoglobins (7) which are presumed to be products
of a mutationally altered globin gene. In addition, mutant bac-
terial strains producing an altered alkaline phosphatase (8) and
an altered tryptophan synthetase (9) have been shown to have
substituted one amino acid for another. Similarly, a number of
substitutions have been described in tlie tobacco mosaic virus "coat
protein" (10).

From the sequence hypothesis, it is a short step to the usual
statement of the "genetic coding problem": how the sequence of
four bases in DNA specifies the twenty amino acids commonly
occurring in protein. The first step toward "solving" the coding
problem is really to show that the problem as stated exists— to
demonstrate that the base sequence of DNA does specify the amino
acid sequence of the protein. On the protein side, evidence that
mutations can cause amino acid substitutions has been men-
tioned. On the DNA side, the determination of nucleotide sequence
is not possible at present, but a prediction (or corollary) to the
sequence hypothesis has been used to arrive at an experimentally
feasible system. This prediction states that the order and relative
position of point mutations within the structural gene for a par-
ticular protein, presumably reflecting base alterations, should
correspond to the order and relative position of amino acid sub-
stitutions in proteins produced by these mutated genes.

Work on the bacterial enzymes alkaline phosphatase (8) and
tryptophan synthetase (9) has shown that two mutations linked
genetically affect amino acids in the same region of the respective
proteins, so that we can feel some confidence that the sequence
hypothesis is correct. Can one determine which bases code which
amino acids by this combined genetic and protein chemical
approach? The answer is probably yes, provided that mutagens
specific for a single base can be developed and used; but the
number of amino acid substitutions which must be accumulated
is almost prohibitively large. Recently, a much more direct
approach to working out the nature of the genetic code and
probably its explicit solution has appeared somewhat unexpectedly
on the scene. This approach indicates that the future of the work
with mutationally altered proteins probably lies in the realm of

64 Information Storage and Neural Control

protein chemistry — in the effect of amino acid substitutions on
protein structure and specificity — and in the confirmation of the
correctness of the biochemical approach which we shall now
consider, rather than in the determination of the code for each
amino acid.

THE MECHANISM OF PROTEIN SYNTHESIS AND THE
BIOCHEMICAL APPROACH TO THE GENETIC CODE

The attempt to understand the intei mediate steps by which
genetic information is transferred into specific protein structure
obviously poses a very interesting biological problem. As recently
as a year ago, however, no one would have predicted that a crude
cell-free extract of E. coli could be forced, even in principle, to
yield precise information about the genetic code. The discovery
which revolutionized the coding search and opened what might
be called the biochemical approach to the genetic code was the
finding of Nirenberg and Matthaei (11) that one could trick the
E. coli extract into making a most unnatural protein — the polyamino
acid polyphenylalanine — by adding a most unnatural piece of
genetic material — the polyribonucleic acid of uridylic acid (poly U).

To explain the significance of this experiment, it is necessary
first to describe briefly present ideas on the mechanism of protein
synthesis. There is believed to be a flow of information from DNA
through RNA to protein involving three classes of RNA: ribosomal
RNA, transfer RNA, and "messenger" RNA. Chemically, all of
these RNA's are polymers with a sugar-phosphate backbone like
DNA, but with ribose sugar instead of deoxyribose, and with the
base uracil (U) instead of thymine (T).

Ribosomal RNA exists in the cell in cytoplasmic ribonucleo-
protein particles (ribosomes), which are generally considered to
be the cellular sites of protein synthesis (12). Messenger RNA is
assumed to carry the genetic information detailing the specific
amino acid sequence of the protein from the DNA to the ribosome.
Presumably the messenger RNA binds to the non-specific ribosome
(probably to ribosomal RNA) and serves as the information
bearing "template" for protein synthesis (13). Transfer RNA's
bind amino acids specifically (with the aid of enzymes). They are

Genetic Cotitrol of Protein Synthesis

thought to carry amino acids to the ribosome-messenger complex
and to act as an "adapter" to position amino acids in the proper
place for their polymerization into specific proteins (12). The
transfer RNA presumably "recognizes" the code for a particular
amino acid in the messenger RNA in order to provide specific
positioning of the amino acid.

The evidence that transfer RNA is an intermediate in protein
synthesis is very good, at least in in vitro systems, and there is
strong experimental support for the idea that ribosomes are the
site of protein synthesis from both in vivo and in vitro studies (12).
The question of whether there is a distinct messenger RNA loosely
attached to nonspecific ribosomes, or whether the genetically
specific RNA is built into ribosomes as they are synthesized, giving
specific ribosomes, is still a matter of some controversy. In the
case of virus infected E. co/i, there is strong evidence favoring the
loosely bound messenger view (14). At present the model described
(and shown schematically in Figure 3) is the most adequate to ex-
plain existing experimental results.

. DNA

poly

T-RNH-AA

Protem T-RNVAA
/ ^

Fig. 3. Schematic representation of the normal protein synthesizing system (on
the left) and the synthetic system (on the right). DNA has been removed from
the synthetic system by the enzyme deo.xyribonuclease and the synthetic mes-
senger poly U replaces the normal messenger RNA.

One can imagine a simple base-pairing mechanism by which
all of this can occur. The messenger RNA may be synthesized
with a DNA primer by a base-pairing, enzyme-catalyzed process
which produces a "complementary" copy or translation of the

Information Storage and Neural Control

DNA in which each base in the RNA is the complement to eacli
base in the DNA. For example, the sequence ATGC in DNA
would be translated into UACG in the RNA because U, replacing
T in RNA but having similar base pairing properties will form a
hydrogen-bonded base pair with A, A with T, C with G, and
G with C. An enzyme has been found which appears to catalyze
this process. Messenger RNA may bind to ribosomal RNA by
means of rather general regions of base complementarity. Finally,
transfer RNA need only have a base sequence complementary
to the messenger RNA base code for its particular amino acid
to fulfill its function, since pairing of the complementary bases
will correctly position the amino acid. If the DNA sequence AAA
codes the amino acid phenylalanine, then the messenger RNA
will have the complementary sequence UUU and the transfer
RNA for phenylalanine a sequence AAA. The UUU sequence
in the messenger RNA will pair with the AAA sequence in the
transfer RNA to provide for the insertion of phenylalanine into
its genetically determined site in the protein (Fig. 4). The gene
DNA and its messenger RNA are equivalent in informational
content, since one is a direct translation of the other.

.((>Ala

lAirrAiriATA

t • I I • I

iu iu iu iu iu i m-pna

RNA Flare

Ribosomal 5ur?cxce

Fig. 4. Hypothetical base pairing scheme for protein synthesis. Poly U is shown
in its role of messenger. The poly U chain binds loosely to a segment of ribosomal
RNA flaring out from the "protein surface" of the ribosome. Transfer RNA for
phenylalanine is presumed to contain an AAA sequence complementary to the
UUU of the poly U and therefore "positions" a sequence of phenylalanines for
polymerization into polyphenylalanine.

Genetic Control of Protein Synthesis 67

The implication of the Nirenberg experiment is that polyuridylic
acid is the messenger RNA for polyphenylalanine and that a
sequence of U is the messenger RNA code for plienylalanine (or a
sequence of A is the DNA code). The "synthetic" and "normal"
systems are compared in Figure 3. Since there exists an enzyme,
discovered by Ochoa and Grunberg-Manago (15), which will
catalyze the random synthesis of ribonucleotides into a polymer,
there is now a very powerful tool available for investigating the
genetic code. For example, a mixed polymer of A and U provides
for sequences of AAA, AAU, AUA, UAA, AUU, UAU, UUA,
and UUU, choosing only triplets for purposes of illustration,
(It should be noted tiiat at least three bases per amino acid are
required if four bases are to specify twenty amino acids.) If poly
AU is added as a synthetic messenger, then amino acids coded by
the above triplets will be incorporated into a polypeptide chain.
Even if some of the triplets are "nonsense" in that they do not
specify an amino acid, by using a large excess of U some poly-
peptide formation can be assured by providing a polyphenyl-
alanine 'handle" so that those triplets which spell an amino acid
will not be lost.

This approach has been pursued very successfully by the Ochoa
and Nirenberg groups to describe the most probable code letter
for fourteen of the twenty amino acids (16, 17). To carry out the
synthetic messenger experiment, the coli extract is first treated
("preincubated") to remove existing messenger RNA. Existing
DNA is removed by the enzyme deoxyribonuclease so that new
messenger RNA cannot be synthesized. Then synthetic messenger
RNA is added, and the amount of C^^ amino acid incorporated
into protein-like material (insoluble in trichloroacetic acid) is deter-
mined by radioactivity measurements. Any significant incorpora-
tion of a CI'^ amino acid, using the UA polymer as the messenger
RNA, implies that the code for that amino acid consists of some
combination of A and U or of a sequence of A. One can then
hope to separate a 2U1A from a 1U2A or a 3A code by deter-
mining the ratio of the observed incorporation of a given amino
acid to that of phenylalanine and comparing this ratio with that
expected for the calculated number of 3U, 2U1A, 1U2A, and
3x\ sequences (using a polymer with U in large excess so that the

68 Information Storage and Neural Control

numbers will be quite different, and assuming that 3U is the code
for phenylalanine).

The way to complete the determination of the genetic code by
discovering the actual sequence of bases is also clear in principle
using the biochemical approach. It should be possible to add
small, known ribonucleotide sequences to poly U enzymatically
and to use these messengers to produce polyphenylalanine plus
the amino acids coded by these sequences (if any). Unless there are
some large surprises lurking around the corner, the genetic code
for E. coll may well be officially solved within the next three
years or so. There remains the question of whether the coli code
is common to all organisms, although most of the limited infor-
mation available argues for universality. Even if the code is
different in higher organisms, the techniques evolved for the coli
system should be generally applicable. All that is needed is a
crude, cell-free, protein-synthesizing system plus the proper syn-
thetic messenger to trick the system.

CONTROL OF THE RATE OF PROTEIN SYNTHESIS

(The Regulatory Problem)

The process by which genetic information is converted into
protein specificity is rapidly becoming spelled out, and the com-
plete unraveling of the exact nature of the genetic code providing
this specificity of protein structure is on the horizon. However,
the genetic control necessary to provide for the adaptive skill of
the microorganism and for the much more complicated growth
pattern of the differentiated organism cannot be accounted for
simply by the ability of genes to control protein structure. The
structural gene, structural messenger, and ribosome constitute a
protein factory, always working at the same rate for all proteins.
It seems obvious that there must exist regulatory genes involved
in turning on and off the structural genes and in varying the
enzyme complement of the cell.

Recent work with bacteria has shown the existence of genes
which serve to control the rate of protein synthesis in response to
changes in external conditions. Normally, the production of the
lactose-hydrolyzing enzyme ^-galactosidase by the bacterium

Genetic Control of Protein Synthesis 69

E. coli can vary roughly a thousand-fold up to a maximum of
some 6 per cent of the cellular protein if a jS-galactoside, an
"inducer," is present in the growth medium. Mutants have been
isolated which have lost this control of the rate of enzyme syn-
thesis. Jacob and Monod (13) have divided these "constitutive"
mutants into two genetically and functionally distinct classes
designated i^ and o^ By studying the dominance properties of
these mutations in partially diploid strains carrying both i+ and
i~ and both 0+ and o*" (both inducible and constitutive genetic
structures) Jacob and Monod have developed a model of the
control process (Fig. 5). This model proposes that a "repressor"
material is made under the control of the i gene, which they call
a regulator gene, and that this repressor binds to a site near the
structural gene, the O or operator gene, preventing formation of
the structural messenger.

Requlator Operator jtructural

Gene bene Genes

Repression or Tnduction Proteins

Fig. 5. The model proposed by Jacob and Monod for the mechanism controlling
the rate of action of the structural gene. A repressor material is made under the
control of the DNA of the regulator gene. This repressor material acts (after
possible metabolite activation) by binding to a DNA site adjacent to the struc-
tural gene or genes subject to rate control by the repressor and preventing the
formation of structural messengers.

In this model the regulation is negative; genes are noimally
functional and are turned off by a repressor. Similar analysis of
the system controlling alkaline phosphatase synthesis (18) has

70 Information Storage and Neural Control

indicated that a gene involved in the regulation of this enzyme
can hav^e a positive effect, i.e., can be involved in turning on a
gene to its full capacity. Therefore, the generality of the model
proposed for the /3-galactosidase system is not at present estab-
lished; but the essential feature of the model — the proposed
existence of specific gene products which exert a controlling
influence over the rate of synthesis of the structural messenger
RNA — is very appealing. It serves as a valuable guide to future
experimental efforts aimed at trying to understand the control
process at a chemical level.

CONCLUDING COMMENTS

We have considered: 1) the chemical nature of the gene;
2) the "sequence hypothesis" which serves as the basis for our
definition of the genetic coding problem; 3) the evidence sup-
porting the sequence hypothesis from combined genetic and
chemical studies; 4) the recent rather dramatic progress of the
biochemical approach; and, finally, 5) the problem of regulation.
We cannot at present unequivocally separate fact from fancy.
However, the evidence now extant certainly favors our main
conclusions: 1) that the genetic information of an organism is
contained in the base sequence of its DNA; 2) that the base
sequence of the DNA of "structural genes" specifies the amino
acid sequence of proteins; 3) that an RNA "messenger" carries
the genetic information from the structural gene to the ribosome
for protein synthesis; and, finally, 4) that the base sequence of
the DNA of certain "regulator genes" specifies a material which
exerts a controlling influence over the rate of protein synthesis.
It should be emphasized, however, that most of the evidence for
these conclusions comes from work with microorganisms and that
the generalization to higher organisms is chiefly an act of faith.

REFERENCES

1. Horowitz, N. H.: Biochemical genetics of neurospora, Advances in

Genetics, J.' 33, 1950.

2. Levinthal, C: Coding aspects of protein synthesis. Revs. Mod. Physics,

J7.-249, 1959.

Genetic Control of Protein Synthesis 71

3. Ris, H.: The Chemical Basis of Heredity, ed. by McElroy and Glass.

Baltimore, Johns Hopkins Press, 1957.

4. \Vatson, J. D., and Crick, F. H. C: The structure of DNA. Cold

Spr. Harb. Symp. Qiiant. Biol, 7S.T23, 1953.

5. Benzer, S.: On the topology of the genetic fine structure. Proc. Nat.

Acad. Sci., Wash., 45:1607, 1959.

6. Crick, F. H. C: On protein synthesis. Symp. Sac. Expt. Biol., 12:

138, 1958.

7. Ingram, V. M.: Hemoglobin and Its Abnormalities, Springfield, Thomas,

1961.

8. Rothman, F.: Cold Spr. Harb. Symp. Qjiant. Biol, 26.-1961, in press.

9. Yanofsky, C, Helinski, R., Mahng, B.: Ibid.

10. Wittman, H. G.: Comparison of the tryptic peptides of chemically

induced and spontaneous mutants of tobacco mosaic virus. Virology^
12:609, 1960.

11. Nirenberg, M., and Matthaei, J. H.: The dependence of cell-free

protein synthesis in E. coli upon naturally occurring or synthetic
polyribonucleotides. Proc. Nat. Acad. Sci., Wash., 47.-1588, 1961.

12. Berg, P.: Specificity in protein synthesis. Ann. Rev. Biochem., 30:29 o,

1961.

13. Jacob, F., and Monod. J.: Genetic regulatory mechanism in the

synthesis of proteins. J. Mol. Biol, 3.-318, 1961.

14. Brenner, S., Jacob, F., and Meselson, M.: An unstable intermediate

carrying information from genes to ribosomes for protem synthesis.
Nature, 190:576, 1961.

15. Grunberg-Manago, M., and Ochoa, S.: Enzymatic synthesis and

breakdown of polynucleotides; polynucleotide phosphorylase. J.
Am. Chem. Soc, 77.-3165, 1955.

16. Lengyel, P., Speyer, J. F., Basilio, C, and Ochoa, S.: Synthetic

polynucleotides and the amino acid code, IV. Proc. Nat. Acad.
.Sci.', Wash., 48:282, 1962.

17. Martin, R. G., Matthaei, J. H., Jones, O. W., and Nirenberg, M. W.:

Ribonucleotide composition of the generic code. Biochem. and
Biophys. Research Comm., 6.-410, 1962.

18. Garen, A., and Echols, H.: Properties of two regulating genes for

alkahne phosphatase. J. Bad., 83:291, 1962.

19. Kendrew, J. C, Dickerson, R. E., Strandberg, B. E., Hart, R. G.,

Davies, D. R., Phillips, D. C, and Shore, V. C: Structure of
Myoglobin. A three-dimensional Fourier synthesis at 2 A reso-
lution. Nature, 185:422, 1960.

72 Information Storage and Neural Control

DISCUSSION OF CHAPTER IV

Mike McGlothlen (Houston, Texas) : What about suppressor
genes where you have a mutation of the structural gene and then
a counter-mutation of the type that causes the still mutated
structural gene to produce normal enzymes?

Harrison Echols (Madison, Wisconsin): The theory which is
now usually advanced to explain these suppressor mutations is
that a suppressor is a mutation which has affected the translation
mechanism; i.e., it has perhaps affected the ability of the soluble
RNA to bind the correct amino acid. The soluble RNA then makes
mistakes which partially rectify the mutational mistake. For
example, suppose that the original change in the protein was a
substitution of the amino acid alanine for glycine and that the
suppressor mutation is such that some of the time, in protein
synthesis, glycine is put back in place of alanine. In this case,
you would now get a reduced level of the original premutation
type of protein. Certain suppressors may also involve a change in
the concentration of some cell constituents, which leads to the
activation of a mutationally altered protein.

McGlothlen: Would you care to say anything about the origin
of the secondary and tertiary structure of proteins? Presumably,
the sequence of amino acids is controlled by sequences in DNA,
but what about the folding, etc., that produces the active un-
denatured form of an enzyme?

Echols: We think that this comes about purely from a deter-
mination of the primary structure. The secondary structure is a
matter of solution thermodynamics. A repeating chain of amino
acids forms an alpha helix if the solvent is not too hard on hydrogen
bonds. To get the specific three-dimensional structure is a tougher
problem. However, we can imagine that as the newly synthesized
protein comes off the ribosome, there are regions of the protein
which are capable of bonding and are in very close proximity to
each other. There is actually some evidence that the primary
structure does determine the three-dimensional structure of the
protein ribonuclease. This derives from experiments in which the
protein is unfolded and then caused to fold again. One can break
all four of the disulfide bonds by reduction to SH, and unwind
the protein into a completely random coil. One would expect,

Genetic Control of Protein Synthesis 73

just by considering random re-formation of four disulfide bonds,
tliat 105 possible alternative forms of the protein should exist.
However, what is found is that oxidation to bring back the disulfide
bonds produces something like 90 per cent enzymatically active
protein. So even though the system is far from physiological, with
this protein, at least, one appears to get the total three-dimensional
structure purely from the primary structure.

Arthur Shapiro (New York, New York): A protein is, of
course, made up primarily of amino acid chains; but most pro-
teins, particularly the specific ones— enzyme proteins — do contain
polysaccharides and do contain very specific, important, and
critical terminal groups. Is it the general notion now that the
DNA chain contains the information that determines these non-
amino acid fractions in the protein molecule as well, or is this
supposed to be a different kind of thing? If so, is there any clue
as to what?

Echols; In general, as proteins and enzymes have been purified
more carefully and more successfully, they have been found in
most cases to contain nothing except amino acids. I would feel
that the appearance of a sugar group or some other moiety attached
to a protein would either be a nonspecific accident or the result
of a specific site built into the amino-acid-determined structure
of the protein. In other words, I think the complete specificity
of the protein comes about because the DNA specifies the amino
acid sequence.

Frank Morrell (Palo Alto, California): We know of some
agents that can alter DNA, such as x-ray, etc. Could you elaborate
on the sorts of agents that can selectively alter base sequence in
RNA?

Echols: One which is widely used is nitrous acid. This removes
the amino groups from bases, producing a change of the base
cytosine into the base uracil in RNA.

Morrell: What is the consequence of alteration of the base
sequence in RNA without simultaneous alteration of DNA?

Echols: Mutagenic agents which aff'ect RNA but not DNA are
not known. Thus, in treating a bacterium with a mutagenic
chemical, both the DNA and the RNA are involved. To my
knowledge, no one has been able to purify a messenger RNA.

74 Information Storage and Neural Control

Until this is done, it will not be possible to study the effects of
specific agents on the RNA and on the resulting proteins. The
only kinds of messenger RNA's which are available are the syn-
thetic ones.

James E. Darnell, Jr. (Cambridge, Massachusetts): The viral
RNA's were the first to be treated with deaminating agents.
Schuster's work with tobacco mosaic virus (TMV), which showed
that deamination of cytosine resulted in mutation, confirmed the
fact that the chemical change is preserved in the progeny particles.
The deamination, which results in mutation of TMV particles
and change in the protein code, is assumed to be preserved from the
initial change in the RNA of the virus. If one considers viral
RNA's as messengers, which they are, then this type of messenger,
at least, can be treated with mutagens, and the residual damage,
if you will, can be preserved.

Echols: I suppose I am being unfair to the viral RNA's, although
it has not been clearly established that the viral RNA functions
directly as a messenger.

Heather D. Mayor (Houston, Texas): There are clear in-
dications from viral RNA that the seat of genetic information
can be RNA as well as DNA. I think there is quite good evidence
that an RNA virus can act as its own messenger. I should like to
ask Dr. Echols if he has any information from mutations on closely
positioned bases in DNA. Is there any evidence that the code is
indeed a triplet sequence rather than, say, a multiple of six? In
fact, are there any data indicating that six bases could represent
the fundamental unit of the code?

Echols: I think that there is no compelling evidence, even
taking Crick's work into consideration, defining the size of the
coding unit. However, the work of Nirenberg and Ochoa cer-
tainly suggests that the number of bases which code an amino
acid cannot be an exceedingly large number. If a large number
were required, the only thing which should promote incorporation
of most amino acids would be a polymer containing all four bases.

Mayor: If you have four bases and a triplet code, you could
possibly get codes for sixty-four different amino acids instead of
the twenty we know to be involved. Do you think that dif-
ferent combinations of bases may code the same amino acids?

Genetic Control of Protein Synthesis 75

Echols: The work which Nirenberg and Ochoa have done
suggests that there may be a degeneracy in the code; i.e., there
may be more than one coding unit which specifies a given amino
acid. They only use polymers containing uracil, but they have
already worked up to nineteen or twenty amino acids. This
suggests that there is either a degeneracy in the code or a rather
surprising selection for "sense" codes containing uracil. Also,
incorporation of at least one amino acid is promoted by polyiners
containing different coding units.

CHAPTER
V

CODING BY PURINE AND

PYRIMIDINE MOIETIES IN ANIMALS,

PLANTS, AND BACTERIA*

Saul Kit, Ph.D.

INTRODUCTION

HE transfer of genetic information in biological systems may
be represented by the following schematic diagram:

DNA

Filial cells

Parental cell

Figure 1

Implicit in this schematic diagram are three concepts: 1) that
genetic information is coded in deoxyribonucleic acids (DNA);
2) that the transfer of genetic information from parental to filial
cells involves the replication of the DNA and distribution of
"equal" amounts to the daughter cells; and 3) that the expression
of genetic potential within a cell involves the transcription of
information from DNA to "Informational" ribonucleic acids

*This investigation was aided in part by grants from the American Cancer Society,
The Leukemia Society, Inc., and The National Cancer Institute (CY-4064, CY-4238).

Pyrimidine Moieties in Animals, Plants, and Bacteria

(RNA) and the translation of the Informational-RNA for specific
protein synthesis.

The key to the understanding of tliese concepts is tlie model for
the structure of DNA proposed in 1953 by Watson and Crick (77).
Watson and Crick suggested that DNA consists of two helical
polynucleotide chains of opposite polarity which are twined round
one another (Fig. 2). Each chain is built from four mononucleo-
tide units which are joined together by 3', 5' phosphodiester
bonds. Each of the four mononucleotide units consists of either a

Fig. 2. Simplified model of die DNA double helix showing hydrogen bonding

and the DNA strands of opposite polarity. P = orthophosphate; S = deoxyribose;

A = adenine; T = thymine; G = guanine; G = cytosine.

Information Storage and Neural Control
OH K .OH

HjC O— PO3H2

HjC O PO3H2

Ribose Phosphate Deoxyribose Phosphate

Fig. 3. Chemical formulas for tlie pentose phosphates found in DNA and RNA.

N^C — NH.

N=C— OH

N — C — NH

^CH

N— C— N

■NH,

HC C — N^ HjN-C C— N^ 0=C

H— N CH

Adenine (A)

Guanine (G)

Cytosine (C)

N==C — OH
0=C CH
H N CH

N:=:C OH

0=C C CH,

H N — CH

-NH.

o=c

■CH,

H— N CH

Uracil (U)

Thymine (T) Methyl Cytosine (MC)

Fig. 4. Chemical formulas for the purine and pyrimidine bases found in DNA

and RNA.

Pyrimidine Moieties i?i Animals, Plants, and Bacteria 79

purine or a pyrimidine base connected in nucleoside linkage to
deoxyribose-5 '-phosphate (Fig. 3). The purine and pyrimidine
bases are adenine (A), guanine (G), cytosine (C), and thymine (T)
(Fig. 4). In addition, methyl cytosine (MC) may partly replace
cytosine in the DNA of certain plant and animal cells and glu-
cosylated hydroxymethylcytosine may replace cytosine in the
DNA of the T-even bacteriophages. The four bases A, G, C, and
T are the symbols of the genetic alphabet, just as dot and dash
are the symbols of the Morse code. Triplets of bases, such as TTT
or TGC, may be the letters of the genetic alphabet and each
tri]3let may specify a particular amino acid of a protein chain.
Tims, the sequence of triplets along a polynucleotide chain would
determine the amino acid sequence of a protein.

The two DNA chains are held together by hydrogen bonds
between the bases, each base being joined to a companion base
on the other chain (Fig. 5). The pairing" of the bases is specific,

o /^ HO-P = 0

0=d-OH X r -\j ^ ^

VvO^ 0 — ,v

HO H

T A H OH

I HO

0»d-OH

O x2

E I

_x cf V°V

HO H I

-a M

0 >>/

G C H OH

Fig. 5. Hydrogen bonding between deoxyadenylic acid and thymidylic acid and
between deoxyguanylic acid and deoxycytidylic acid.

80 Information Storage and Neural Control

adenine (A) going with thymine (T) and guanine (G) going with
cytosine (C). The phosphate groups of the DNA chains are
accessible to hydrogen or hydroxyl ions and to dyes and are,
therefore, on the outside, whereas the bases occur opposite one
another on the inside. From x-ray diffraction studies, it has been
deduced that there is a succession of flat nucleotides spaced
3.36 A apart and standing out perpendicular to the fiber axis.
The structure is relatively rigid and serves as a template for either
its own replication or for the replication of "Informational" RNA.
Plausible mechanisms for DNA replication and for spontaneous
mutations were embodied in the proposals of Watson and Crick
(77). These mechanisms are strongly supported by a large number
of experiments. According to the proposal for DNA replication,
a twin stranded DNA molecule partially unwinds, and each base
attracts a complementary free nucleotide already available for
polymerization within the cell. These free nucleotides, whose
phosphate groups already possess the free energy necessary for
polyesterification, then link up with one another, after being held
in place by the parental template chains, to form a new poly-
nucleotide molecule of the required nucleotide sequence. Thus,
each DNA strand serves as a template for the synthesis of a com-
plementary strand. The replication process can be schematically
represented as follows:

-A-C-T-G-->-A-C-T-G-

-A-c-T-G-/* : : : :

'••• -T-G-A-C-

-T-G-A-C-\

-T-G-A-C-->-T-G-A-C-
Parental DNA '.'.'.

Duplex DNA Chains ....

After Unwinding -A-C-T-G-

New DNA
Duplexes

It is a corollary of the Watson-Crick hypothesis that a change of
one or a few nucleotides in the DNA sequence will be mutagenic.
Mechanisms for spontaneous mutation and for experimentally

Pyrimidine Aioieties in Animals, Plants, and Bacteria 81

induced mutations have been suggested on the basis of this con-
cept. Watson and Crick (77) pointed out that the specificity in
DNA structure (adenine pairing with thymine and guanine with
cytosine) resuhs from the assumption that each of the bases
possesses one tautomeric form which is very much more stable
than any of the otlier possibilities. The fact that the compound is
tautomeric, however, means that the hydrogen atoms can occasion-
ally change their location. Thus, a spontaneous mutation might
be caused by a base occurring very occasionally in one of the less
likely tautomeric forms at the moment when tlie complementary
chain is being formed. For example, whereas adenine will nor-
mally pair with thymine, if there is a tautomeric shift of one of
its hydrogen atoms, it can pair with cytosine. The next time
pairing occurs, the adenine (having resumed its more usual
tautomeric form) will pair with thymine, but the cytosine will
pair with guanine, and so a change in the sequence of bases will
have occurred.

Mutations may also be induced by chemical agents. Let us
consider two categories of chemically induced mutations: 1) those
which result from the conversion of one nucleotide base in a
DNA chain co another nucleotide base (transition), and 2) those
which result from the deletion of a base from the chain.

The conversion of cytosine to thymine may be effected by
adding nitrous acid to cells (58). Cytosine is deaminated by
nitrous acid to uracil. When DNA which has been deaminated
in this way undergoes replication, the uracil will attract adenine
during the complementary base pairing. The next time pairing
occurs (second cycle of replication) the adenine which had paired
with uracil will now pair with thymine. Hence, following two
cycles of DNA replication, the original cytosine-guanine base pair
will have been converted to a thymine-adenine base pair.

A mutagenic change from thymine to cytosine may be induced
in cells by the use of the thymidine analog, bromodeoxyuridine (21).

A nucleotide base change in the DNA chain expresses itself
as an amino acid change in the protein chain whose synthesis is
controlled by the altered DNA.

A third mutagenic agent, nitrogen mustard, may alkylate some
of the guanine groups of the DNA chain at the N(7; position.

82 Informatio7i Storage and Neural Control

Two stages of degradation follow: 7-alkylguanine splits off, and
a slow fission of the sugar phosphate chain follows (41). If in the
process of replication of the DNA which has been exposed to
nitrogen mustard, the guanine of the template is skipped, the
resulting sequence of nucleotide bases in the daughter chain will
be altered. Another mutagen which apparently acts by deleting
a base from the DNA chain is proflavin, an acridine derivative,
A series of T-4 bacteriophage mutants induced by proflavin have
been studied by Crick et al. (13). The mutants in almost all cases
manifest a complete inactivation of the function of the eene

Equations 1 through 3 depict schematically some current ideas
about genetic coding (13):

(TRIPLET)

I ' I II 1 I 1 I 1 I 1 I 1

[1] TGCTGCTGGTGCTGGTGGTGA---

I - ' — '-I— I— 1 — 1— I — l-_l_l_l_l_l_l_l_l_|_|__L I
Starting '

point Normal DNA Ghain

I I I 1 I 1 1 1 r

"1 r

[2] TGGTGTTGGTGGTGGTGGTGA -

I — I — I — I — l^-l I I I I I ^1 L_l l_l__l_L_l._[

Starting: T

^iD

point Gonversion of G to T by Nitrous Acid

(Gene may still be functional, amino acid in
protein chain is changed).

[3]TGGTGTGGTGGTGGTGGTGA

I — ' — I— I— I— 1^1 I I I L_l I I l_I_^l_^l__I_|

Starting T

point G deleted from Ghain by Proflavin or Nitrooen

Mustard

(Gene inactivated).

It is assumed that a triplet of three nucleotide bases (for example,
TGG) codes each particular amino acid in the protein chain. It is
further assumed that the DNA chain is translated from a fixed
starting point and that the genetic code is nonover lap ping. If one base
in the chain is altered (for example, the change of G in the second

Pyrimidine Moieties in Animals, Plants, and Bacteria 83

triplet of Equation 1 to T in the second triplet of Equation 2),
only one amino acid in the resulting protein chain will be altered;
that is, the amino acid coded by TGT replaces the amino acid
coded by TGC. The protein with the changed amino acid may
still be functional or partly functional.

If a nucleotide base is deleted following nitrogen mustard or
proflavin treatment, the gene is inactivated. In Equation 3, it is
seen that G has been deleted from the second triplet of Ecjuation 1.
With a nonoverlapping code, the second triplet now becomes
TCT, the third triplet becomes GCT, etc. In other words, all
triplets from TCT on are changed. Thus, all amino acids in the
protein chain which are coded by the second triplet to the last
triplet are changed, and the new protein chain cannot function.
As a result, the gene controlling the synthesis of that protein has
been inactivated.

For a more detailed discussion of mutation mechanisms at the
chemical level, the reader is referred to the papers of Freese (21),
Lawley (41) and Crick et al. (13).

It is apparent that a knowledge of the number of DNA molecules
in a given cell and of the entire nucleotide sequence of each mole-
cule, along with the code by which DNA and RNA sequences are
translated to the amino acid sequences of proteins, would suffice as a
"blue print" for describing any organism. Such a total description
is, of course, not available to us as yet. We do, however, know cer-
tain characteristics of the DNA of many species of bacteria, plants,
animals, and viruses. The amount of DNA per cell is known in many
instances. This, in a sense, tells us how "thick" each "genetic book
of instructions" is. We also have knowledge of the average nucleo-
tide composition of the DNA of different species, that is, of how fre-
quently the "alphabet symbols" are repeated in each book. There is
also some knowledge of the range of composition within a particular
cell. These topics will be discussed in the second section of this
paper.

In the third section of this paper, I shall consider the composition
of RNA molecules and the proposed mechanisms for transcribing
information from DNA to RNxA. Finally, I shall briefly touch on
the translation of the DNA-RNA code to amino acid sequences
of proteins.

Information Storage and Neural Control

THE DEOXYRIBONUCLEIC ACIDS (DNA)
Amount of DNA per Cell

The amount of DNA per cell varies greatly from the simplest
to the most complex organisms. Some representative values
are shown in Table I. Mammals, reptiles and amphibians often
contain about six picograms of DNA per cell; but many fish
and birds contain only about a third of this amount. Bacteria and
fungi cells have about 1/100 the amount of DNA found in the
higher animals. Larger viruses such as vaccinia and T-even bac-
teriophage have about 1/10,000 the amount of DNA per particle
and the smallest viruses, such as bacteriophage 0X174 and the
Shope papilloma virus have one millionth the amount of DNA
per particle found in the cells of higher animals.

TABLE I

DNA Content Per Cell of Various Species
(All Values Expressed as Picograms DNA Per Cell or Virus Particle)

2.6x10-6

Reference

<^X174 phage

(65, 66)

T-2 phage

2x10-"

(66)

Rabbit papilloma virus (Shope)

6.6xl0-«

(56)

Vaccinia virus

3 X 10-^

(56)

E. coli B (log phase)

0.0137

(26)

Clostridium

0.0245

(75)

Yeast (diploid)

0.05

(50)

Neurospora

0.017

(47)

Fish

Shark

5.46

(75)

Sturgeon

3.2

(75)

Carp

3.49

(75)

Perch

1.9

(75)

Catfish

1.89

(75)

Barracuda

1.37

(75)

Amphibians

Frog

15.0

(75)

Toad

7.33

(75)

Reptiles

Green turtle

5.27

(75)

Wood turtle

4.92

(75)

Alligator

4.98

(75)

Birds

Domestic fowl

2.34

(75)

Guinea hen

2.27

(75)

Mammals

Man

6.8

(75)

Rabbit

6.5

(75)

Rat

5.7

(75)

Mouse

5.0

(75)

Pyrimidnie Moieties in Animals, Plants, and Bacteria 85

Since the molecular weight of Shope papilloma virus is about
4x 10" (78) and the molecular weight of an average nucleotide
base pair is about 600, it is obvious that Shope papilloma virus
has a total of about 4 x 10V6 x 10", or 6600 nucleotide base pairs
in its DNA. Vaccinia virus has roughly 300,000 base pairs; bac-
teria have roughly 20x10*^ base pairs, and mammalian cells a
total of about 7x10^ base pairs. If there are no restrictions as to
the proportion in which base pairs occur or as to the sequence in
which they occur, the number of different DNA molecules possible
is 4", where n is the number of base pairs. Thus it is clear that
DNA provides an adequate basis for gene specificity.

The increase in the relative amount of DNA from the lowest to
the highest forms of life reflects the need for an increasing number
of genetic units for embryogenesis and differentiation and for
various regulatory mechanisms.

How Large are DNA Molecules?

The molecular weight of Shope papilloma virus is about 4x10''
(78). The weight average molecular weights of most of the DNA
preparations which have been studied are about 5-14 x 10^ How-
ever, the molecular weight of DNA may actually be much greater
than this. Very high molecular weight DNA has been isolated
from the T-even bacteriophages and it is possible that the entire
genome of the T-even bacteriophages consists of one long DNA
chain having a molecular weight of 90x10'' to 150x10" (15).
There is reason to believe that very long DNA molecules are
partially fragmented to smaller pieces when they are isolated from
tissues and viruses.

Average Composition of DNA

The average nucleotide base composition of DNA molecules
may be measured by hydrolyzing the DNA and then measuring
the nucleotide bases after they have been resolved by paper
chromatography, ion exchange chromatography, or paper elec-
trophoresis. To the extent that a mixture of DNA molecules can
be partially separated, the distribution of base compositions among
the molecules of the mixture can also be estimated.

Two other important methods are available for measuring the
molar nucleotide composition of DNA: 1) equilibrium sedimenta-

Injormation Storage and Neural Control

.1^

^;^

ic:

-s^

sr-

«u

-w

l=/l

CN]f\l-i^->^TfTtul\OVOr--COCOOOOOCNCNCNCNCN^-iT--0]01M010!tN]MT+Tj-LriiOv-iO

r-'T-.tococ<-ir<-i':t-LnLnsoi^r-r^[~-oooooooooooo^-^^^^^T-Hroco^^ij-)Ln

C^C7^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^OOOOOOOOOOOOOOC^

Oh ro

Group

9484
RD

75 a

Group

35
3478

< -* O] J

<!, p:, HH ^ ^ c\ ~~^<N T^

a a o

h 2 « S

S 6

3 3

'-3 '-v

P 3

^ '-a

ETC oj '^ .2

M IJ > C in

;i3 bp >. oj C
"3 "^ ^ 'b 3

P =^ S 3 ^
fs o 2 « jS

en 3

3X!

.2-2 S

O rt

.cti

>- Hj ^
P 3 O

^r, cj c^ ^^

p 03 CL en -O

0) (/] „ 3 t^

■O C; 3 « 3
•-< tn C — -^ '

y 2i 3'

' _ tH to: 3 brj tn
> 3 n 3 o > -s

■^i='~— oj^o— oo-^n

., .„_ m — .^c^sS^rt^iJ'-"

UU22OM0HpqOi-If0c/3fiHKpqEEc^ciI|

o 'S

fl tn .22

a en r3

I °-c o

O lU Sh
I _Q ^O ^

i-.^-i-rio'-'i:— 5
o o o o x.y o y ^

3 3-5

^28= 10^:3^^33 =

&Hh-lh-l

Pyrimidine Moieties in Animals, Plants, and Bacteria

JS
ho

-0

^°l^
>-i^

;coQ,

>t^

CA m o o

CN iri -; .

3- u-iH

3-^

■c

u
br

__,

,_,

5-'

0
0

a7i

c«

-n

«j

'-H

i-l

ryi

i-<

r/l

XI _a

0-c

Z 0 iz; > 2; w w w a

'H o

w;^

O >. w nj 3

^ ^ c c •-

six; n! 03 «
Di c/3 c/5 C/3 W

u e S '-'

S S C X

crt O a;

ij ,. e w s

XI i: isi

; 3 3 ^ 3
0 3 C i^ h

! Ji («

p 73 « -M 3 u

J5 O U l) s^ 3

M w 03 o «j r;

.-y) oa CL, < Q «

3 3

O 2j

.2,0

"3 n! C

3 3 3

r r- ^ ■

S S c«

D _j ^j

T3 3

2 t; ii -T. «

iOc^p:

0 0

-1

s a

X3
0
N

0
■a
0

X 0
0 -a
0 3

0 0
a H.

u u

C^Qpi

Oh C^ t^ c)?

JJ X

-^ X

^ r

88 Information Storage and Neural Control

tion in CsCl density gradients; and 2) the estimation of the melting"
temperature (T,„) of DNA by a study of the change in DNA
absorption as a function of temperature (16). The latter two
methods have the advantages that less material is required to
analyze the DNA and that an estimate of the variation from the
mean of nucleotide base composition can be made. Of the three
methods, density gradient centrifugation has the highest accuracy,
requires the least DNA per experiment, and permits the detection
of DNA molecules of unusual base composition even where the
latter comprise less than 5 per cent of the total DNA. A further
discussion of density gradient centrifugation will be presented later.

Since DNA is double stranded* and the guanine and adenine
of each chain are paired, respectively, with the cytosine and
thymine of the complementary chains, the total purine bases
(A+G) are equal to the total pyrimidine bases (C+T) and the
total 6-amino bases (C+A) are equal to the total 6-keto bases
(G+T). However, the ratios of guanine plus cytosine to adenine
plus thymine (G+C)/A+T) are not the same in different or-
ganisms and provide a parameter by which organisms can be
characterized.

The molar (G + C) content of the DNA of seventy-two different
bacterial species, thirteen species of higher plants, ten species of
algae, four species of fungi, two species of protozoa, sixteen species
of invertebrates, twenty-three species of animals, twelve bacterial
viruses, six animal viruses and rickettsiae, and twelve insect
viruses have been measured and are shown in Tables II through IX.

The molar per cent (G+C) varies from 26.5 per cent in the
protozoan, Tetrahymena, to 73 molar per cent (G + C) for the
bacterium, Mycobacterium phlei. Of the 170 species listed in Tables
II through IX, 112 have DNA molecules whose average molar
(G + C) contents are 40 to 60 per cent. This is not surprising. It is
probable that Kutagenic transitions of adenine-thymine to qua-
nine-cystosine base pairs in one part of a DNA molecule are
compensated for by transitions from quanine-cystosine to adenine-
thymine base pairs in other parts of the molecule so that the molar
per cent (G + C) remains, on the average, close to 50 per cent.

*Exceptions to this statement are the DNA of the bacteriophages, 0X174 and
SI 3, which are single stranded (65, 66).

Pvrimidine Moieties in Animals, Plants, and Bacteria

^_,

ffi

«J'

^ ^ r-< r^ 00 ■r- ■^

ON CM 00 CO ^-1
Tf lO CO lO NO

\ocMcor^r^t-~-oocNNO -^-^
ssoooooocM-^r^oooo

COT)--*'<t'<^'^'<^'*'*"*'*"*

in t^ o o 00

rt b: o 5 &. c a^s «j h

O >,J2 > !-

2 '^-'

^ >

S"^!^ ^? «

> JS . — «

I S "rt -S J3 3 (^ u 03
i<C/3CU&hUO-i<C/:3

riaticum

ipiens

•denschel

1 ■?

m ?3

3 «-^

•s 0-^1

"2 H S

fci M 'S 3

n3 u ^
C 4^ 0

Ddictyon i
trodesmu
ella ellips
desmus q

0 u-S
■a 2 ^

g rt cfl «

^--2g

S -C j:: J=

1 >,

§: K < O c/3

"SoiOH

D ii

- .s

3 >-
U 2

3 6
u S

cq Ci

Information Storage and Neural Control

tV)

■g

f^"

•^

■&^

CM CS CM Tl-

so r^

■b , '^

all
S c "

^ n! « rt
"^ O

Q ^fo

C« C

3 w M >ii o 3 « M V

> ^

oooo-*oooo-<too-^oooo

CNvOCM^cOcOOOMO-*

O Ln M fO un r^
o o o o o o
r- r~- r^ t~~ r-~ r-

8 -a

f o ,-^* •

S" > DM

■'-' "^ w +-<

r- •« S-i 4J 5"

a nj ij -^ i!

^ dJ ■•-' o *^

^ O i- oj « 2

3
C

-0

Echinoc
Psamme
Oak mo
Paraccn
Arbacia
Silk woi
Arbacia
House f

ii S

S ii 3;

S 6

« j> JQU

|l3(jCCoCCu

t^ B3

Pyriffiidine Moieties in Animals, Plants, and Bacteria

o txi o:^ o:)

m 00 00 00 ro

fe§&§^°fe?

&?fe5

£5&-5

Tj- (N] (N] ri

rO ■"

TT -"T

Tj- tT ^ ^

^ Tl-

-:!■

■^ Tt-

&5&°fe?£?&?£^°^^°&§fe? ^6?^fe?65^^^

(^1 o

o o

O O

o o

r^ r^ Tj- Lo
■^ m Tj- Tj-

o o o o
o o o o

o o o o

00OOC->OOO'>-'Oi-'
CsOOCnOCsOOOO

s s

V v JJ

„ c

'H.'a

3 3

E B

^ c

be-

t' ^

-? -^ ^

N U U

<^ a

gl^

5^1H

« w

^ si VI

QUO

G;-D bo

•— tti re B n

u -— - j3 re nj

'-' ^ ., -C ;3
■- u ^ « c P,

man

Lass 1

bull

calf

monk

dog

rabbi

Chine

goldc

rat

O O

I-- CO

^ o

CN o o cv 1

so i~~ r^ so

'~-'—

-^^

J^ 3

CW^

M r^

^ 4^

aj re

-S3

bB*

C '^

3^=;

-J

i-T

.S u

re JJ

u •>

JD ;s

Information Storage and Neural Control

TABLE VII
The Densities and Base Compositions of Bacteriophage DNA

Density Gradi

'ent

Centrifu

■gat ion

Chemical Analysis

Phage

Density gcm-^

, G+C

Reference

%G+C

Reference

T2 +

1.700

(66)

T4 +

1.698

34.4

(66)

T6 +

1.707

34.2

(66)

1.702

(60)

(66)

0X174 + + +

1.72

(65)

(66)

Salmonella Al + +

43.4

(66)

Phage alpha

1.704

(11)

42.5

(11)

1.705

(60)

(66)

1.710

(60)

(66)

1,712

(60)

49.6

(66)

Xvir

1.710

(60)

(66)

P22

(66)

+T2, T4, T6 contain hydroxymethylcytosine
+ + Preliminary or tentative data
+ + + Single stranded DNA

TABLE VIII
DNA Base Composition of Animal Viruses and Rickettsia

Density Gradient Centrifugation

Chemical Analysis

Virus

Density gcm-^
1.714

%G+C
50

Reference
(78)

% G+C

Reference

Shope papilloma

(78)

Vaccinia

1.698

(38)

40.6

(10)

Fowl pox

(52)

Rickettsia burnetti

1.704

(60)

(10)

Rickettsia prowazeki

30.8

(10)

Rickettsia rickettsii

37.5

(10)

TABLE IX
Base Composition of DNA of Insect Viruses (3)

Host Species

%G+C

Polyhedral viruses

Porthetria dispar

58.7

Lymantria monacha

51.5

Clioristoneura fumiferana

51.2

Ptychopoda seriata

47.6

Malacosoma americanum

42.7

Malacosoma disstria

42.2

Bombyx mori

42.7

Colias philodice eurytheme

42.5

Neodiprion sertifer

37.3

Tipula poluclosa Merg. (T.I.V.) *

31.5

Capsular viruses

Cacoecia muriana

37.6

Choristoneura fumiferana

34.8

"Reference (74).

Pyrimidine Moieties in Animals, Plants, and Bacteria 93

The variation in composition of DNA molecules among dif-
ferent species of microorganisms is very great. C/ostricIium perfringens
has only 32 molar per cent (G + C) while at the other extreme
of the distribution, Alycobacterium phlei has 73 molar per cent
(G + C) (Table II). Fungi vary from 36 to 54 per cent (G + C)
in the four species investigated and the two protozoan strains so
far measured {Tetrahymena and Euglena) contain 26.5 and 47 molar
per cent (G+C), respectively (Table IV). The range of average
DNA values among algae is also rather great — 36.9 molar per
cent (G+C) for diatomic algae to 64 molar per cent (G+C) for
the green alga, Chlamydomonas reinhardi (Table III). The dis-
tribution of average values among different species of higher
plants (Table III) and invertebrates (Table V) is much narrower:
Plant DNA composition varies from 35 molar per cent (G+C)
for tobacco leaves to 48.4 molar per cent for Triticum vulgare, and
ainong invertebrate species the values vary from 34.9 molar per
cent for the echinoderm, Echinocardium cordatum to 44 molar per
cent (G + C) for the crab, Carcinus maenas.

The range of values is very narrow indeed for the average
composition of the twenty-three vertebrate DNA species so far
examined. The values range from 40 to 44 molar per cent (G + C)
(Table VI). The range for DNA aniinal viruses appears to be
greater than that for the host animal species: 38 molar per cent
(G+C) for fowl pox virus and 50 molar per cent (G+C) for the
Shope papilloma virus of rabbits (Table VIII). Various insect
viruses manifest in their DNA average (G + C) contents of 31.5 to
58.7 per cent (Table IX), a range which is also somewhat broader
than the compositions of the few insect DNA's so far studied.

The T-even bacteriophage DNA's have about 35 molar per
cent (G + C), a value outside the range of the bacterial host in
which the viruses grow {E. coli, 51 per cent (G+C) (Table VII).
The DNA from a number of other bacteriophages (T-1, T-3, T-7)
and the lysogenic phages (X, P22, salmonella Al) have average
molar (G+C) contents which are very similar to those of the
bacterial host cells {E. coli. Shigella, Salmonella).

Base Composition of DNA Strands

Since a DNA molecule consists of two complementary nucleotide
strands of opposite polarity, the question arises as to whether the

Information Storage and Neural Control

C3H LUNG

C3H BRAIN

Fig. 6. Ultraviolet photograph of the banding of mouse DNA and Streptomyces
viridochromogenes DNA in a CsCl density gradient. The photograph was taken
after centrifugation for twenty-four hours at 25° at 44,770 rev. per minute. The
Streptomyces band (p25° =[l.729 gcm-^) appears at the left. Two bands with
mean densities of 1.701 and 1.690 gcm-^ were obtained with mouse DNA. The
narrow band at the right is due to the meniscus of the solution.

two Strands have similar or grossly dissimilar average nucleotide
compositions. Only fragmentary data are presently available.
There are indications that the DNA strands of animal cells differ
slightly in thymine, and hence, in adenine content (14). The
DNA strands of the bacteriophage, alpha, have been separated

Pyrimidine Moieties in Animals, Plants, and Bacteria

and shown to differ in density (11). The density differences could
reflect differences in the base compositions of tlie strands. Tliere
are alternative explanations, however. For example, one strand
might contain more glucose residues attached to hydroxymethyl-
cytosine than tlie other strand.

Equilibrium Sedimentation of DNA in CsCl Density Gradients

Equilibrium sedimentation of DNA in CsCl density gradients
provides an elegant method for characterizing DNA with respect
to average nucleotide composition and heterogeneity of composi-
tion (46). Following the centrifugation of a DNA solution for
twenty-four hours in a CsCl density gradient, the DNA tends to
form a band at a position in the cell corresponding to its effective
buoyant density. A photograph, taken with ultraviolet light, of the
banding of bacterial and mouse DNA is shown in Figure 6, and
a microphotodensitometer tracing of the photograph is presented
in Figure 7. The white (ultraviolet light absorbing areas) in the

24 Hours, 25°C (CsCI-p= 1.7208)

24 Hours, 25°C (Cs CI- p= 1.7165)

Fig. 7. Microdensitometer tracing of photograph of the banding of mouse DNA
and Streptomyces DNA after density gradient centrifugation. See Figure 6.

96 Information Storage and Neural Control

center part of the photograph correspond to Streptomyces virido-
chromogenes DNA and to two mouse DNA bands, respectively.
The effective mean buoyant density of the Streptomyces virido-
chromogenes DNA band is 1.729 gcm~^ The corresponding values
for the principal and minor mouse DNA bands are 1.701 and
1.690 gcm~^ (Table II and Table VI). The mean densities of the
bands can be measured with an accuracy of ±0.001 gcm~^

It has been shown by Rolfe and Meselson (54) and by Schild-
kraut et al. (60) that the mean effective buoyant densities of double
stranded DNA bands vary linearly with the molar per cent (G+C)
content of the DNA. For example, Streptomyces viridochromogenes
DNA (density = 1.729 gcm~^) contains 73 molar per cent (G+C),
Escherichia coli DNA (density = 1.710 gcm"^) contains 51 molar
per cent (G+C), and mouse DNA (density = 1.701 gcm~^) con-
tains 42 molar per cent (G+C). Thus, if the density of DNA is
measured by equilibrium sedimentation in CsCl, the molar per
cent (G + C) can be calculated. TABLES II to VIII show the
densities of DNA preparations from various sources and the
agreement between molar per cent (G + C) as calculated from
the densities of the bands and as determined directly by chemical
analyses.

The standard deviations of the DNA bands expressed in density
units (a ) depend upon at least two factors: 1) the molecular
size of the DNA, and 2) the heterogeneity of DNA composition
within the sample. This follows from the following considerations.
The centrifugal field tends to drive the DNA into a region where
the sum of the forces acting on a given molecule is zero. This
concentrating tendency is opposed by Brownian motion, with the
result that at equilibrium, the macromolecules are distributed with
respect to concentration in a band of width inversely related to
their molecular weight (46).

When a DNA population consists of molecules which differ
considerably in density and in molar per cent (G+C), discrete
and nonoverlapping DNA bands may be formed. For example,
two discrete DNA bands are formed with mouse DNA (Figs. 6, 7)
and there is no overlapping between the mouse DNA (p = 1.701
gcm"^) and the Streptomyces (p = 1.729 gcm"^) DNA bands. On
the other hand, if a DNA preparation consists of a heterogeneous

Pyrimidine Moieties in Animals, Plants, and Bacteria 97

population of molecules differing only slightly in density and in
molar per cent (G + C), the DNA bands will overlap, and there
will be an increase in the overall standard deviation of the band.
So long as the total variance of a band and the number average
molecular weight of a DNA preparation are known, it is possible
to calculate the contribution which heterogeneity of composition
makes to the band width (16, 36, 54). An independent estimate
of the heterogeneity of composition of a DNA preparation may
also be made from the DNA thermal denaturation curves. These
independent estimates agree satisfactorily.

Nonoverlapping Bands

Density gradient centrifugation experiments have permitted a
number of interesting conclusions concerning DNA. First, as
already mentioned, the DNA obtained from many bacterial
species form discrete bands which do not overlap. These observa-
tions indicate that the respective organisms have no DNA mole-
cules with common density, and, by inference, that they have no
DNA molecules with common nucleotide base composition (16).
Since the metabolism and replication of bacteria do have much in
common, it is generally thought that many of their proteins should
be identical or very similar. Yet all current discussions of the
way in which DNA can control the sequence of amino acids in
proteins require a direct correlation between the composition of
the DNA and of the protein. There are a number of ways in
which this dilemma can be resolved. The simplest approach is to
assume that some amino acids are coded by more than one nucleo-
tide triplet. For example, each of the triplets UCC and UAC
might specify the amino acid threonine. Thus, the dependence of
the amino acid composition of proteins on the DNA nucleotide
composition would not be exacting and DNA molecules having
different compositions could code very similar proteins. A genetic
code in which two or more nucleotide triplets are used to code a
particular amino acid is called a '"degenerate" code (13).

There are a number of arguments which can be advanced in
favor of this hypothesis. By studying nitrous acid induced mutants
of bacteriophage, Tessman (73) demonstrated that each of the
complementary DNA strands is functional. The experiments "* of

98 Information Storage and Neural Control

Chamberlin and Berg (7) suggest that genetic information can
be transcribed from either of the two DNA strands to infor-
mational-RNA. Thus, when single stranded 0X174 DNA was
used as a template for RNA polymerase, an RNA strand having
a composition complementary to the 0X174 DNA was formed.
However, when single stranded 0X174 DNA was used as a tem-
plate for DNA polymerase, double stranded DNA was formed.
The double stranded 0X174 DNA could then be used as a primer
for RNA polymerase and, in this case, RNA was formed having
a composition identical to that of double stranded 0X174 DNA.
If both of the complementary DNA strands are ultimately trans-
lated from the same fixed starting point, the foregoing experi-
ments would suggest that complementary nucleotide triplets code
the same amino acid. On the other hand, if the complementary
DNA strands are translated from opposite starting points, the
results would point to one of two possibilities: 1) that two dif-
ferent nucleotide triplets code the same amino acid, or 2) that the
complementary DNA strands are identical even though they have
opposite polarities. The second of these possibilities would further
require that one half of a given DNA strand be complementary to
the opposite half. The latter restrictions do not seem to apply
to the 0X174 DNA studied by Chamberlin and Berg (7, 65).
Davern's experiments (14) also suggest that these restrictions are
unlikely as a general proposition. Hence, some form of a "degen-
erate" genetic code seems to be the most appealing hypothesis at
this time.

Unimodal and Bimodal Distributions

The DNA from almost every species so far examined forms one
discrete band after density gradient centrifugation. Streptomyces
viridochrornogenes DNA (Figs. 6, 7) illustrates this unimodal dis-
tribution. Several examples have now been found in which DNA
manifests a bimodal distribution. Mouse DNA illustrates the bimodal
DNA distribution (36). Mouse DNA manifests a major component
having a density of 1.701 gcm~^ and a second minor component,
comprising about 8 per cent of the total DNA, having a density
of 1.690 gcm~^ (Figs. 6, 7). A bimodal DNA distribution is also
observed with guinea pig DNA. In this case, the major component

Pyrimidine Moieties in Animals, Plants, and Bacteria 99

is the lighter one (1.697 gcm~^) and the minor component is the
heavier one (1.703 gcm"^^) (36).

Since the density of DNA is linearly related to the molar per
cent (G+C), it is possible that the mouse and guinea pig minor
components consist of a population of DNA molecules differing
in molar per cent (G+C:). However, alternate explanations for
the minor component may be offered. This point will be resolved
when the guinea pig and mouse minor DNA components are
isolated in pure forin.

An interesting bimocial distribution has been found by Sueoka
(72) in crab testes DNA (Table V). The major DNA component
has a density of approximately 1.705 gcm"^ However, a very
light minor component also occurs which is indistinguishable from
a double stranded polynucleotide in which only two of the four
nucleotide bases are present. The bases involved are adenine and
thymine and the polymer is called the (deoxy-A-T) polynucleotide.
The function of this unusual polynucleotide, deoxy-A-T, is
unknown.

Heterogeneity of Composition of DNA

Since the DNx^ of any species is quite heterogeneous, it is of
interest to compute an upper bound on the standard deviation
{aac) of the distribution of the guanine-cytosine base pairs over
the population of DNA molecules. The upper bound of {ctgc) is
given as:

[4] O-GC max = 10 O- density

where o- density is the standard deviation of the DNA distribution
in the CsCl density gradient (54). It should be pointed out that
the actual value of (tqc lies considerably below the calculated
upper bound because thermal motion of the DNA molecules con-
tributes significantly to band width.

The DNA's of nine bacterial species form bands in the density
gradient with o- density in no case greater than 0.003 gcm~^ The
corresponding upper bound on the standard deviation, (tqc, of
the molecular content of guanine plus cytosine is therefore in no
case greater than 0.03. It is remarkable that the standard deviation
of guanine-cytosine content within the molecular population of

100 Information Storage and Neural Control

any one bacterial species covers less than one tenth of the range
over which the mean guanine-cytosine content varies among the
various species.

Doty and co-workers (1^) have sliown that the total variance
of the DNxA. bands equals the sum of the variance due to molecular
weight {(tmw'^) and that due to density heterogeneity (or density)-

[5] C'r = (J''mW + 0-" density

Since the variance due to molecular weight can be estimated,
<^^deiisity can be calculated. The latter value can be expressed in
units of molar per cent (G+C); that is, in terms of ctqc.

For bacterial DNA, Doty, et al. (16) have shown that age is
actually equal to about ±1.7 molar per cent (G + C).

The values for animal tissues are considerably greater (Table VI).
The standard deviation in units of density (o-density) ranges from
0.0037 to 0.0047 gcm"~^ The latter value is equivalent to a standard
deviation corresponding to the molecular content of guanine plus
cytosine of about 3 molar per cent (G + C).

DNA obtained from various adult tissues of mice and from mouse
tumors do not differ significantly in their effective buoyant den-
sities or in the standard deviations of the density gradient bands.
Although all differentiated tissues of a given organism are pre-
sumed to have identical genomes, there is evidence that normal
tissues and tumors differ genetically. The fact that no significant
differences between the DNA of normal and of malignant tissues
have been found does not necessarily contradict the latter concept.
Instead, it reflects the fact that existing techniques are insufficiently
sensitive to detect such differences. It is quite possible that thou-
sands of point mutations exist in the genomes of cancer cells.
These could not be detected by the relatively gross physical
methods so far employed.

Although significant differences between the DNA of adult
animal tissues and the DNA of tumors have not been found, it
has been possible to recognize specific differences between the
DNA of various species of higher animals (37). As shown in Table
VI, frog, turtle, and alligator DNA are slightly heavier than other
vertebrate DNA's. Chinese hamster and frog DNA have relatively
low standard deviations for the DNA bands. Also, mouse and
guinea pig DNA manifest bimodal distributions.

Pyrimidine Moieties in Animals, Plants, and Bacteria 101

Another point of interest is the fact that the small heterogeneity
of base composition among the DNA molecules of an organism
seems to be true for smaller regions within molecules (72). This
indicates that the intramolecular distribution of (G+C) and
(A+T) pairs is fairly unifoim, although in short regions (for
example, tri- or tetranucleotides) nonrandomness has been demon-
strated.

The Formation of Hybrid DNA Molecules and Their Use in
Studies of DNA Homologies

DNA molecules having the same average molar nucleotide com-
position do not necessarily have the same nucleotide sequence along
the DNA chains. Since techniques for measuring the nucleotide
sequence are not currently available, possible sequence homologies
between DNA molecules from different sources must be investi-
gated by indirect methods. There have been two general approaches
to this problem so far: 1) an analysis of the distribution of oligo-
nucleotides in partial hydrolysates of DNA; and 2) a study of
DNA hybrids.

Burton (6) has measured the distribution of short chain oligo-
nucleotides in acid degradation products of DNA. Differences
could be detected in the distribution of dinucleotides and tri-
nucleotides of four animal and four bacterial species.

The formation of hybrid DNA molecules has been investigated
by Schildkraut and co-workers (61). These studies depend upon
the fact that each DNA molecule consists of two complementary
strands which can be separated in solution. Strand separation can
be accomplished by heating the DNA to a temperature which
will "melt" the hydrogen bonds which hold together the double
stranded helix. One of the DNA preparations to be tested is
labeled with N^^ C''\ or deuterium, so that it will form a heavy
band when it is centrifuged in a density gradient. The second
DNA preparation is of normal density. The heavy and the light
DNA molecules are mixed and the strands are separated by
heating. Wlien DNA is slowly cooled, the complementary strands
attract each other and the hydrogen bonds are reformed {renatura-
tion). Thus, the DNA duplexes are reconstituted.

Let us consider the situation when two DNA molecules from
the same species are renatured, but where one molecule is labeled

102

Information Storage and Neural Control

with heavy isotopes and the second is of normal density. Upon
renaturation, hybrid molecules of intermediate density will be
formed:

[6]

Normal Density
DNA

II, , II

Heavy Density
DNA (n'^, c'^)

Upon Renaturation Gives

Heated Gives

Single Strands

Hybrid DNA of
Intermediate Density

The normal density DNA, "heavy" density DNA, and "inter-
mediate" density DNA can be resolved as discrete bands by
density gradient centrifugation. Hybrid formation, then, is de-
tected by the presence of a new DNA band of intermediate density.
It should be emphasized that hybrid formation can take place
only when long regions of the nucleotide sequences of DNA
molecules are identical or very nearly so. Molecules having the
same average (G+C) content but differing in the sequence of
G, C, T, and A along the polynucleotide chain will not form
hybrids.

The possibility that renaturation and hybrid formation might
take place between the DNA of bacterial strains with close taxo-
nomic, physiological, and genetic relationships was investigated
by Schildkraut et al. (61). Hybrid formation was readily demon-
strated between the DNA of E. coli and of six other E. coli strains.

Interspecies hybridization of DNA was also demonstrated in
certain instances for bacteria having the same nucleotide content
of (G + C). Thus, the DNA from B. subtilis and B. natto formed
hybrids, and in addition, the DNA from E. coli K-12 formed
hybrids with those from E. coli B. and Shigella clysenterioe. Sig-
nificantly, these are instances where genetic exchange has been

Pyrimidine Moieties in Aiiimals, Plants, and Bacteria 103

demonstrated by conjugation or transduction. No hybrid formation
was detected between the DNA oi E. coli K-12 and that oi Salmonella
typhimurium. The latter bacteria mate but transduction from one
to the other occurs only to a very limited extent, if at all.

Aside from the taxonomic importance of this technique, it offers
a rational approach to the study of genetic compatibility where
genetic exchanges have not been demonstrated. In addition, the
technique has found application in connection with the problem
of information transfer between DNA and RNA. The latter
experiments will be discussed in the next section of this paper.

THE RIBONUCLEIC ACIDS (RNA)

General Characteristics

The ribonucleic acids (RNA), which mediate the transfer of
genetic information between DNA and proteins (Fig. 1), differ
chemically from DNA in several ways (35): 1) The sugar com-
ponent of RNA is ribose, instead of deoxyribose (Fig. 2); 2) Uracil
(U), instead of thymine, is the 6-keto-pyrimidine base in RNA
(Fig. 3); 3) RNA is a single stranded, flexible polynucleotide coil
unlike DNA which is rather stiff and double-stranded; and 4) Most
RNA molecules are much shorter in length than DNA. Also,
RNA is less stable in alkaline solutions than is DNA.

Four classes of RNA are known: l)transfer-RNA, 2) ribosomal-
RNA, 3) messenger or informational-RNA, and 4) virus-RNA.

Transfer-RNA

Transfer-RNA (T-RNA or S-RNA) consists of a family of
molecules which function in the activation of amino acids and in
the transfer of the activated amino acids to the ribosomal tem-
plates so that they can be linked together to form proteins. Prob-
ably, a different and characteristic transfer-RNA molecule is
required for each of the twenty amino acids. Yeast T-RNA
specific for the activation of the amino acid, valine, has recently
been obtained by Stephenson and Zamecnik (70) in highly
purified form (65-80 per cent). Holley et al., (31) have partially
purified the alanine, valine, and tyrosine T-RNA of yeast and
have studied the oligonucleotide content of ribonuclease digests.

104 Information Storage and Neural Control

T-RNA molecules have a molecular weight of about 25 to
30,000 (80 to 100 nucleotide chain length). The sedimentation
constant of T-RNA is about 4S. Three additional characteristics
are of interest: 1) It has been shown that guanine mononucleotide
terminates one end of the T-RNA chain, 2) cytidylic acid-
cytidylic acid-adenylic acid is the trinucleotide which terminates
the other end of the T-RNA chain, and 3) each T-RNA chain
contains an unusual mononucleotide, pseudouridylic acid (PsU).
The function of pseudouridylic acid is unknown. An amino acid
can be attached to the adenylic acid end of the chain as shown
schematically in Equations 7 through 9 (30):

[7] Amino acid + ATP — ^ Adenyl ~ Amino acid + Pyro-
phosphate

[8] Adenyl « Amino acid + G- — PsU— -C-C-A -^

(T-RNA)

Adenylic acid + G PsU C-C-A « Amino acid

(T-RNA with activated amino acid)

[9] T-RNA « Amino acid + Ribosomes -^ T-RNA +
Ribosomes (Amino Acid)

Transfer-RNA comprises approximately 10 per cent of the total
cellular RNA. The mononucleotide content of the T-RNA from
a number of different sources has been determined (Table X).
T-RNA molecules from all sources have a high content of guanine
and cytosine (53-61 molar per cent (G + C)).

TABLE X

% (G + C) Content of Transfer — RNA

Species

(%G+C)
59

%Psoudoun

<dylic

Rejerence

Rabbit appendix]

(63)

Rat liver

3.95

(51)

E. coli'

59.6

1.22

(51)

E. coli^

61.0

2.1

(17)

Saccharomyces cerevisiae

53.1

3.05

(51)

Yeast alanine-T-RNA

61.3

3.7

(31)

Yeast valine-T-RNA

56.5

4.7

(31)

Yeast tyrosine-T-RNA

56.5

4.7

(31)

D. pneumoniae

52.6

(82)

Micrococcus lysodeikticus

56.8

(82)

Pyrimidine Moieties in Animals, Plants, and Bacteria

105

Ribosomal-RNA and Total Cellular RNA

Ribosomal RNA comprises the bulk of the RNA of cells (approxi-
mately 80 per cent). The ribosomes, particles consisting of about
half protein and half RNA, are the organelles where amino acids
are assembled into protein chains. Ribosomal-RNA seems to con-
sist of two components having sedimentation constants of about

TABLE XI

Guanine and Cytosine Content of Ribonucleic
Acids from Various Organisms

Bacteria

%G+C
50.0

Reference

Proteus vulgaris

(2)

Pasteurella tularensis

50.8

(1)

Escherichia coh

54.8

(1)

Azotobacter vinelandii

55.8

(1)

Sarcina lutea

56.9

(1)

Mycobacterium tuberculosis BCG

59.3

(1)

Algae

Ghaetocerus decipiens

52.1

(1)

Rhabdonema adriaticum

54.8

(1)

Scenedesmus quadricauda

56.1

(1)

Hydrodictyon reticulatum

56.4

(1)

Thalassiosira nordenscheldii

56.5

(1)

Hig/ier Plants

Equisetum sp. (spores)

52.9

(1)

Zea mays (germs)

54.8

(1)

Phaseolus vulgaris (sprout)

55.2

(1)

Pea

(69)

Tobacco leaves

56.9

(45)

Fungi

Saccharomyces cerevisiae

(2)

Penicillium stolonigerum

50.6

(2)

Aspergillus niger

56.2

(2)

Protozoa

Euglena gracilis

54.8

(2)

Tetrahymena pyriformis

39.1

(2)

Higher Animals

Rat liver

64.5, 60

(33, 69)

Rabbit appendix

(63)

Mouse tissues

64.0

(34)

Ghicken tissues

59.7

(39)

Duck liver

56.9

(45)

Calf liver

65.0

(44)

Sheep liver

71.2

(44)

Pig liver

64.1

(44)

Gat brain

(44)

Starfish eggs

(44)

Rana catesbiana eggs (frog)

(20)

Invertebrates

Spider oocytes (cytoplasm)

52.1

(18)

(Tegenaria domestica)

106 Information Storage and Neural Control

25S and 16S (see reference 35). The molecular weights of these
components are of the order of magnitude of 500,000 and 1,000,000.
It was formerly believed that ribosomal-RNA functioned as a
template for protein synthesis but recent experiments have cast
doubt on this concept (4, 27). Possibly, ribosomal-RNA is inert
with respect to genetic coding. Ribosomal-RNA is only slowly
synthesized in the cell and the mechanism by which it is synthesized
is unknown. There is no obvious relationship between the com-
position of either ribosomal-RNA or T-RNA and that of the
DNA of a given organism.

Since ribosomal-RNA comprises the bulk of the RNA of a cell,
the nucleotide composition of ribosomal-RNA is similar to that
of the total cellular RNA. The mononucleotide compositions of
the total RNA of bacteria, algae, higher plants, fungi, protozoa,
and higher animals are shown in TABLE XI and those of the
ribosomal-RNA of several organisms in Table XII.

TABLE XII

% G + G OF RiBOSOMAL RNA

Species and Fraction

46.7
46.6

Reference

Saccharomjces cerevisiae
Large granules
Small granules

(51)
(51)

E. coli B

Large granules
Small granules

54.1
53.5

(51)
(51)

Rat liver

Microsomes

60.9

(51)

Rabbit appendix

Aqueous phenol extract

62.5

(63)

Mouse Tissues

Microsomes — aqueous phenol extract

(34)

The molar per cent (G+C) of the total RNA and the ribosomal-
RNA of organisms generally increases as the molar per cent
(G+C) of the DNA increases. However, the correlation between
total RNA and DNx^ composition is rather weak (1) and the
change in RNA composition from one species to another is much
less than that of the DNA. Belozersky and Spirin (2) have com-
piled the RNA nucleotide composition of fifty-five strains of
bacteria. Some representative values are shown in Table XI. The

Pyrimidine Moieties in Animals, Plants, and Bacteria 107

molar per cent (G+C) varies from 50 per cent (G+C) for Proteus
vulgaris to 59 per cent (G + C) for Alycobacterium tuberculosis. The
values for most bacterial species are close to 55 per cent (G+C).
The DNA nucleotide values for Proteus vulgaris and Mycobacterium
tuberculosis are 39 per cent and 67 per cent (G + C), respectively,
(Table II); and, as mentioned previously, the DNA values for
all species range from 32 per cent to 73 per cent (G + C).

The RNA molar nucleotide composition of algae varies from
52 to 56 per cent (G + C) (Table XI), whereas the corresponding
DNA values manifest a much broader variation, that is, from 37
to 64 mole per cent (G + C) (Table III). The RNA molar nucleo-
tide values of higher plants vary over a 4 per cent (G+C) range
(52.9 to 56.9) and the values from fungi vary over a 6 molar per
cent (G + C) range (50 to 56). On the other hand, the DNA
values for higher plants range from 35 molar per cent (G + C) to
48 molar per cent (G+C) (Table III), and the DNA values for
fungi vary from 36 to 54 mole per cent (G + C) (Table IV). The
molar per cent (G + C) is much greater in protozoan RNA than
in protozoan DNA (Tables IV, XI). The nucleotide composition
of the RNA of higher animals is extremely high, over 60 per cent
(G+C); while the DNA of the animal species contains about 40
to 44 per cent (G+C) (Tables VI, XI, XII). No significant
differences have so far been detected between the RNA base
composition of different tissues of the same animals or between
normal tissues and tumors (34, 39, 44).

Virus RNA

A third kind of RNA is that found in plant, animal, and bacterial
viruses (Table XIII), RNA viruses are capable of replicating
within cells in the absence of new DNA synthesis (55, 64) but the
mechanisms by which the RNA templates of the viruses are
replicated are not known. Conceivably, an RNA strand might
serve as a template for the replication of a complementary strand.
However, there is no evidence that RNA duplexes exist or that
strands of complementary base composition occur in RNA viral
populations. The molecular weight of the RNA of viruses is
approximately 2 million (9, 22). It is rather interesting that all
but one of the thirteen plant and animal RNA viruses contain

108 Information Storage and Neural Control

TABLE XIII

Base Composition of Ribonucleic Acids of Viruses

%G+C

Reference

Bacterial virus of E. colt K-12

52.7%

(43)

Plant Viruses

Turnip yellow mosaic

55.3

(59)

Tobacco ringspot

47.9

(59)

Tomato bushy stunt (BS3)

48.7

(59)

Southern bean mosaic

49.0

(59)

Tobacco mosaic

43.8

(59)

Aucuba mosaic

43.9

(59)

Rib grass

43.8

(59)

Potato X

44.6

(59)

Animal Viruses

Poliomyelitis (Mahoney)

46.5

(57)

Influenza A (PR-8)

44.1

(56)

Influenza B (Lee)

41.4

(56)

Encephalomyocarditis

46.7

(19)

Rous sarcoma

50.9

(12)

41.4 to 50.9 mole per cent (G + C) (Table XIII). Many DNA
viruses have similar values (Tables VII through IX).

Messenger-RNA (Also called Informational-RNA and Com-
plementary-RNA and Abbreviated C-RNA)

The lack of correlation between DNA nucleotide composition
and ribosomal nucleotide composition provided a paradox for
some time. It was believed that genetic information was coded in
DNA but that the actual assembling of amino acids into proteins
occurred on the ribosomes. The fact that proteins are not syn-
thesized directly on the genes demanded the existence of an inter-
mediate information carrier. This intermediate information carrier
was generally assumed to be ribosomal-RNA. However, it was
difficult to reconcile this with the following facts: 1) ribosomal-
RNA is relatively stable; 2) it is remarkably homogeneous in size
and in nucleotide composition although this homogeneity reflects
neither the range of size of polypeptide chains nor the variation
in nucleotide composition observed in the DNA from different
sources; 3) the capacity of bacteria to synthesize a given protein
does not survive beyond the integrity of the corresponding gene;
and 4) regulation of protein synthesis in bacteria seems to operate
at the level of the synthesis of the information intermediate by
the gene rather than at the level of the synthesis of the protein
(4, 27).

Pyrimidine Moieties in Animals, Plants, and Bacteria 109

This paradox appears to have been resolved by the hypothesis
that ribosomal-RNA is not the intermediate carrier of information
from gene to protein, but rather that ribosomes are non-specialized
structures wliicii receive genetic information from the genes in
the form of an unstable intermediate or messenger (4). Although this
radical revision in concepts was introduced as recently as May,
1961, an impressive array of supporting evidence has now been
amassed. This evidence will be discussed briefly in this section.
A schematic representation of the mechanism of information
transfer between DNA and informational-RNA (messenger-RNA)
is shown in Figure 8.

DNA DOUBLE HELIX

NEW RNA CHAIN

Fig. 8. Hypothetical representation of the transcription of genetic information
from the DNA double heUx to "Informational-RNA."

It is assumed that DNA can act as a template for the syn-
thesis of a new messenger-RNA chain. The mechanism is not
unlike tliat by which the DNA chains are replicated. Probably,
the DNA double helix partially unwinds. Each base then attracts
a complementary free ribonucleotide already available for poly-
merization within the cell. The free ribonucleotides, whose phos-
phate groups already possess the free energy necessary for poly-
esterification, then link up with one another, after being held in
place by the DNA template chains, to form a new ribopoly-
nucleotide molecule. Thus, DNA serves as a template for the
synthesis of a complementary RNA strand. The newly formed

110 Information Storage and Neural Control

complementary-RNA (C-RNA) strand then dissociates from the
DNA, and moves to the vicinity of nuclear or cytoplasmic ribo-
somes where it serves as a template for protein synthesis. Mean-
while, the DNx^ strands probably spontaneously return to the
double stranded form.

Messenger-RNA (C-RNA) rapidly becomes radioactive when
cells are incubated with radioactive uridine or orthophosphate.
To account for the high turnover of this species of RNA, it has
been suggested that it is very unstable and that possibly it is
degraded after it has fulfilled its function in protein synthesis (4).
The fact that the bulk of the cellular RNA differs markedly in
composition from the DNA of a given organism and also from the
C-RNA suggests that C-RNA is present at very low concentrations
in the cell. As commonly isolated, C-RNA has a sedimentation
coefficient of only 9 to 12S, but this may be due to the fact that the
9 to 12S molecules represent degraded messenger-RNA chains.

The first evidence for the existence of messenger-RNA came
from the experiments of Volkin and Astrachan (76). Using isotope
labeling, Volkin and Astrachan were able to show that in bacterial
cells infected with a bacteriophage, such as T-2, there was a high
turnover in a minor RNA fraction. This RNA fraction had an
apparent nucleotide composition which corresponded to that of
the DNA of the phage and was markedly different from that
of the host DNA. The experiments of Volkin and Astrachan
were subsequently confirmed and extended by Nomura and
co-workers (49).

In 1960, Rich demonstrated that it was possible to form a
specific and complementary helical complex involving a synthetic
DNA strand (polydeoxyribothymidylic acid) and a synthetic RNA
strand (polyriboadenylic acid) (53). Schildkraut et al. (62) em-
ployed density gradient centrifugation experiments to show that
a hybrid complex between polydeoxyguanylic acid and poly-
ribocytidylic acid was also possible.

In the same year, it was shown by several laboratories (5, 79,
80, 23, 71) that an RNA polymerase enzyme was present in
bacteria and in animal cell nuclei. The purified enzyme could
catalyze a net synthesis of new RNA froin uridine triphosphate,
adenosine triphosphate, guanosine triphosphate, and cytidine

Pyrimidine Moieties in Animals, Plants, and Bacteria 111

triphosphate, but DNA exercised a directing role in the enzymatic
synthesis of RNA. DNA from several sources, having widely
different base compositions, could serve as primers for the synthesis
of RNA by KNA polymerase, but the newly synthesized RNA
had a composition which reflected the composition of the DNA
primer which was einployed. With the purified enzyme, RNA
synthesis only took place if DNA was present.

Single stranded DNA from phage 0X174 could serve as primer
in which case the RNA had a composition complementary to the
DNA of 0X174 (7). If double stranded 0X174 DNA was enzy-
matically prepared with DNA polymerase and the double stranded
0X174 DNA was then used as a primer for the synthesis of RNA
by RNA polymerase, the RNA product had a composition similar
to double stranded 0X174 DNA. In addition, RNx\ synthesis was
primed by heat denatured DNA (23, 80). These results suggested
that each of the complementary DNA strands could serve as tem-
plates for the synthesis of Messenger-RNA. However, it is not yet
definite that this happens in vivo. Possibly, only one of the DNA
strands serves to specify the sequence of messenger-RNA and the
second strand constitutes "nonsense" information.

Not only is the composition of the newly synthesized RNA
dependent on the composition of the DNA primer, but the se-
quence of the ribonucleotides in the new RNA is determined by
the sequence of deoxyribonucleotides in the DNA. This has been
demonstrated in two ways. First, nearest neighbor sequence studies
have been carried out by Furth, Hurwitz, and Goldman (24) and
by Weiss and Nakamoto (81). Second, it has been demonstrated
that enzymatically synthesized RNA formed with a T-2 phage
DNA primer can be heated with the T-2 DNA and slowly cooled
so as to permit hybrid formation by renaturation (25). The hybrid
has been demonstrated by density gradient centrifugation experi-
ments. Hybrid formation occurs between C-RNA specific to T-2
phage and T-2 phage DNA, but not between G-RNA of T-2
phage and E. coli DNA or sea urchin DNA.

The existence of T-2 specific RNA, which was initially inferred
from the isotope experiments of Volkin and Astrachan, was estab-
lished by Nomura, Hall, and Spiegelman (49). Newly synthesized
RNA was separated from the bulk of cellular RNA using both

112 Information Storage and Neural Control

zone electrophoresis in starch columns and centrifugation in
sucrose gradients. T-2 specific RNA had a higher electrophoretic
mobility and a greater heterogeneity in size than the principal
normal RNA components. The T-2 specific RNA was found to
be bound to the ribosomes, but with a linkage very sensitive to
disruption by low magnesium levels.

Renaturation and hybrid formation experiments were per-
formed to establish that sequence complementarity existed be-
tween "T-2 phage specific RNA'' and T-2 phage DNA (28).
RNA-DNA complex formation was demonstrated in mixtures of
heat denatured T-2 phage DNA and purified T-2 RNA subjected
to the slow cooling process. The success of the hybridization
experiments suggested immediately that the original observation
by Volkin and Astrachan (76) of a similarity in base composition
between T-2 RNA and DNA was indeed a reflection of a more
profound homology. Hybrid formation was specific. Heterologous
DNA from Psendomonas aeruginosa, E. coli, or phage T-5 did not
yield DNA-RNA hybrids with T-2 RNA. This led to the con-
clusion that the nucleotide sequences of T-2 DNA and RNA
were complementary.

In further experiments Spiegelman, Hall, and Storck (68)
demonstrated the natural occurrence of DNx\-RNA hybrids in
phage infected E. coli cells. Finally, Hayashi and Spiegelman (29)
and Gros et at. (27) have demonstrated the presence of natural
DNA-RNA hybrids in uninfected bacterial cells.

THE GENETIC CODE

The recent experiments of Nirenberg and Matthaei (48) and
of Ochoa and collaborators (42, 67) represent a major break-
through and give promise of providing the key to the entire
genetic code within one or two years. Nirenberg and Matthaei (48)
were able to develop a cell free ribosomal system from E. coli in
which the amount of incorporation of amino acids into proteins
was dependent upon the addition of heat stable RNA preparations.
Transfer-RNA could not replace the active RNA fraction which
presumably contained some messenger-RNA.

Of particular interest was the most important observation that
the addition of a synthetic polyribonucleotide, polyuridylic acid.

Pyrimidine Moieties in Animals, Plants, and Bacteria 113

specifically stimulated the incorporation of the amino acid L-phen-
ylalanine into a protein resembUng poly-L-phenylalanine. In this
system, advantage was taken of the fact that poly-L-phenylalanine
is poorly soluble. Hence, it precipitated out of solution and was
readily isolated. The obvious implication of the experiments was
tliat polyuridylic acid was functioning as a synthetic template —
messenger-RNA: Hence, uridylyl-uridylyl-uridylyl (UUU) was
probably the nucleotide triplet which coded for the amino acid
L-phenylalanine.

Other synthetic polyribonucleotides were quickly tested in this
system by Speyer, Lengyel, Basilio, and Ochoa (67). On the basis
of the latter experiments, the nucleotide code letters for the twenty
amino acids commonly found in proteins have been identified.
The letters for an assumed triplet code are presented in Table
XIV. It should be pointed out that the sequence of bases in the
triplets is known only for UUU (code letter of L-phenylalanine).
The proposed code letters are in excellent agreement with amino
acid replacement data on nitrous acid mutants of tobacco mosaic
virus (TMV). In experiments with nitrous acid mutuants of

TABLE XIV

The Proposed Genetic Code (42)

L-Amino Acid

RNA Triplet

phenylalanine

UUU

valine

2U-1G

3.
4.

cysteine
isoleucine

2U-1G
2U- lA

5.
6.

tyrosine
lysine

2U-1A
1U-2A

serine

2U- IC

leucine

2U- IC, (2U- 1A;2U - IG)

proline

1U-2C

10.

threonine

1U-2C, (lU- lA-lC)

11.

glycine

1U-2G

12.
13.

tryptophane
histidine

1U-2G
lU-lA-lC

14.

arginine

lU-lC-lG

15.

glutamine

lU-lC-lG

16.

alanine

lU-lC-lG

17.

methionine

lU-lA-lG

18.

glutamic

lU-lA-lG

19.

aspartic

lU-lA-lG

20.

asparagine

1U-2A, (lU- lA- IC)

Note: During the transcription of genetic infor-
mation from DNA to RNA, thymine (T) is
replaced by uracil (U).

114 Information Storage and Neural Control

TMV, it has been shown that in one mutant proHne is replaced
by leucine, and threonine is replaced by serine. Since the effect
of nitrous acid is to convert cystosine to uracil, this would imply
that there are changes of 1U-2C to 2U-IC for the proline to
leucine replacement in the TMV protein and changes of 1U-2C
to 2U-1C for the threonine to serine replacement. The replace-
ment of serine by phenylalanine and of glutamine by valine in
another mutant is also consistent with the proposed genetic code
(Table XIV).

The code need not contain the triplet CCA. The CCA is the
terminal sequence of transfer-RNA to which the activated amino
acid is attached during protein synthesis.

The recent progress in the field of genetic coding is impressive.
However, many important questions must still be solved. Three of
these are: 1) How a triplet of nucleotides can sterochemically
account for the coding of an amino acid; 2) how ribosomal-RNA,
transfer-RNA, and messenger-RNA are held together on the
ribosomal particles; and, 3) the mechanisms by which ribosomal-
RNA, transfer-RNA, and viral RNA are synthesized.

There is every reason to be optimistic that these and other
important questions will soon be resolved.

REFERENCES

1. Belozersky, A. N.: Nucleic Acids of Microorganisms. Nucleoproteins,

p. 199-229. Proceedings of the Eleventh Institute International de
Chimie Solvay, Brussels, June 1-6, 1959, New York, Interscience,
Inc. 1960

2. Belozersky, A. N. and Spirin, A. S.: Chemistry of the Nucleic Acids

of Microorganisms. The Nucleic Acids III, ed. by E- Chargaff and
J. N. Davidson, New York. Academic Press, Inc., 1960, p. 147.

3. Bergold, G. H.: Biochemistry of Insect Viruses. The Viruses I, ed. by

F. M. Burnet and W. M. Stanley. New York, Academic press.
Inc., 1959, p. 505.

4. Brenner, S., Jacob, F. and Meselson, M.: An unstable intermediate

carrying information from genes to ribosomes for protein syn-
thesis. Nature, 79(9.- 576-581, 1961.

5. Burma, D. P., Kroger, H., Ochoa, S., Warner, R. C. and Weill,

J. D.: Further studies on deoxyribonucleic acid dependent en-

Pjrimidine Moieties in Animals, Plants, and Bacteria 115

zymatic synthesis of ribonucleic acids. Proc. Nat. Acad. Sci. USA,
47:1A9-152, 1961.

6. Burton, K.: Frequencies of nucleotide sequences in deoxyribonucleic

acids. Biochem. J., 77.-547-552, 1960.

7. Chamberlin, M. and Berg, P.: Deoxyribonucleic acid-directed syn-

thesis of ribonucleic acid by an enzyme from Escherichia coh.
Proc. Nat. Acad. Sci. USA, 45.- 8 1-94, 1962.

8. Chargaff, E.: Isolation and Composition of the Deoxypentose Nucleic

Acids and of the Corresponding Nucleoproteins. The Nucleic Acids /,
ed. by E. Chargaff and J. N. Davidson, New York, Academic
Press, Inc., 1955, p. 307.

9. Cheng, P. Y. : On the similarity of chain length of nucleic acids.

Proc. Nat. Acad. Sci. USA, 45.-1 557-1 560, 1959.

10. Cohen, S. S.: Comparative biochemistry and virology. Advance Virus

Res., J.-1-48, 1955.

11. Cordes, S., Epstein, H. T., and Marmur, J.: Some properties of

the deoxyribonucleic acid of phage alpha. Nature, 797:1097-1098,
1961.

12. Crawford, L. \'. and Crawford, E. M.: The properties of Rous

sarcoma virus purified by density gradient centrifugation. Virology,
7J.-227-232, 1961.

13. Crick, F. H. C, Barnett, L., Brenner, S., and Watts-Tobin, R. J.:

General nature of the genetic code for proteins. Nature, 792.- 1227-
1232, 1961.

14. Davern, C. I.: Bias in i^ase-pair orientation in deoxyriljonucleic

acids. Nature, 188:2Qd-209, 1960.

15. Davison, P. F., Freifelder, D., Hede, R., and Levinthal, C: The

structural unity of the DNA of T2 bacteriophage. Proc. Nat. Acad.
Sci. USA, 47.- 11 23-1 129, 1961.

16. Doty, P., Marmur, J., and Sueoka, N.: The Heterogeneity in Prop-

erties and Functions of Deoxyribonucleic Acids. Brookhaven Sym-
posia in Biology No. 12, Structure and Function of Genetic Elements,
1959, p. 1.

17. Dunn, D. B., Smith, J. D., and Spahr, P. F.: Nucleotide composition

of soluble ribonucleic acid from Escherichia coli. J. Molec. Biol.,
2.-113-117, 1960.

18. Edstrom, Jan-Erick: Composition of ribonucleic acid from various

parts of spider oocytes. J. Biophys. Biochem. Cytol., 5'.-47-51, 1960.

19. Faulkner, P., Martin, E. M., Sved, S., Valentine, R. C. and Work,

T. S.: Studies on protein and nucleic acid metabolism in virus-
infected cells. 2. The isolation, crystallization, and chemical charac-

116 Information Storage and Neural Control

terization of mouse encephalomyocarditis virus. Biochem. J., 80:
597-605, 1961.

20. Finamore, F. J. and Volkin, E.: Some chemical characteristics of

amphibian egg ribonucleic acids. J. Biol. Chem., 2J6.-443-447, 1961.

21. Freese, E.: On the Molecular Explanation of Spontaneous and

Induced Mutations. Brookhaven Symposia in Biology No. 12,
Structure and Function of Genetic Elements, 1959, p. 63.

22. Frisch-Niggemeyer, W.: Absolute amount of ribonucleic acid in

viruses. Nature, 778:307-308, 1956.

23. Furth, J. J., Hurwitz, J., and Goldman, M.: The directing role of

DNA in RNA synthesis. Biochem. Biophys. Res. Commun., 4:362-361 ,
1961.

24. Furth, J. J., Hurwitz, J. and Goldman, M.: The directing role of

DNA in RNA synthesis. Specificity of the deoxyadenylate-deoxyth-
ymidylate copolymer as a primer. Biochem. Biophys. Res. Commun.,
4:431-435, 1961.

25. Geiduschek, E. P., Nakamoto, T., and Weiss, S. B.: The enxymatic

synthesis of RNA: Complementary interaction with DNA. Proc.
Nat. Acad. Sci. USA, 47; 1405-1 41 5, 1961.

26. Gillies, N. E. and Alper, T. : The nucleic acid content of Escherichia

coU Strains B and B/r. Biochim. Biophys. Acta, 43:\82-\87, 1960.

27. Gros, F., Hiatt, H., Gilbert, W., Kurland, C. G., Risebrough, R. W.

and Watson, J. D.: Unstable ribonucleic acid revealed by pulse
labelhng of Escherichia coli. Nature, 790.-581-585, 1961.

28. Hall, B. D. and Spiegelman, S.: Sequence complementarity of T2DNA

and T2 specific RNA. Proc. .Nat. Acad. Sci. USA, 47.-137-146, 1961.

29. Hayashi, M. and Spiegelman, S.: The selective synthesis of infor-

mational RNA in bacteria. Proc. Nat. Acad. Sci. USA, 47: \ 564-
1580, 1961.

30. Hoagland, M. B.: The Relationship of Nucleic Acid and Protein

Synthesis as Revealed by Studies in Cell-free Systems. The Nucleic
Acids III, ed. by E. ChargafT and J. N. Davidson, New York,
Academic Press, Inc., 1960, p. 349.

31. Holley, R. W., Apgar, J., Merrill, S. H. and Zubkoff, P. L.: Nucleo-

tide and oligonucleotide compositions of the alanine-, valine-, and
tyrosine-acceptor "Soluble" ribonucleic acids of yeast. J.Am. Chem.
Soc, 5J.-4861-4862, 1961.

32. Iwamura, T. and Myers, J.: Changes in the content and distribution

of the nucleic acid bases in chlorella during the life cycle. Arch.
Biochem., 84:261-211, 1959.

Pyrimidine Moieties in Animals, Plants, and Bacteria 117

33. Kirby, K. S.: A new method for the isolation of ribonucleic acids

from mammalian tissues. Biochem. J., 64.-405-408, 1956.

34. Kit, S.: Base composition of the ribonucleic acids of normal tissues,

tumors, and tissue subfractions. Arch. Biochem., 55'; 1-9, 1960.

35. Kit, S.: Studies on the structure, composition, and metabolism of

tumor RNA. Cell Physiology of Neoplasia, Austin, University of
Texas Press, 1960, p. 337.

36. Kit, S.: Equilibrium sedimentation in density gradients of DNA

preparations from animal tissues. J. Molec. Biol., J;711-716, 1961.

37. Kit, S.: Species differences in animal deoxyribonucleic acids (DNA)

as revealed by equilibrium sedimentation in density gradients.
Nature, 79J.-274-275, 1962.

38. Kit, S. and Dubbs, D. R.: (unpublished).

39. Kleinschmidt, W. J.: The composition of ribonucleic acid and

deoxyribonucleic acid of normal and neoplastic tissue. Cancer Res.
79.-966-969, 1959.

40. Kok, I. P. and Opalskii, A. F.: Comparison with respect to nucleotide

composition of deoxyribonucleic acids isolated from insects and
mammals by various methods. Biochemistry {USSR), 25.-839-844,
1961.

41. Lawley, P. D.: The action of Alkylating Agents on Nucleic Acids

and Their Relation to Other Mutagens. The Molecular Basis of
Neoplasia. Austin, The University of Texas Press, 1962, p. 123.

42. Lengyel, P., Speyer, J. F. and Ochoa, S.: Synthetic polynucleotides

and the amino acid code. Proc. Nat. Acad. Sci. USA, ^7.- 1936-1 942,
1961.

43. Loeb, T. and Zinder, N. D.: A bacteriophage containing RNA.

Proc. Nat. Acad. Sci. USA, 47:282-289, 1961.

44. Magasanik, B.: Isolation and Composition of the Pentose Nucleic

Acids and of the Corresponding Nucleoproteins. The Nucleic Acids I,
ed. by E. Chargaff and J. N. Davidson, New York, Academic
Press, Inc., 1955, p. 373.

45. Makhinko, V. I. and Block, L. N. (USSR): personal communication.

46. Meselson, M., Stahl, F. W. and Vinograd, J.: Equilibrium sedi-

mentation of macromolecules in density gradients. Proc. Nat. Acad.
Sci. USA, 43:581-588, 1957.

47. Minagawa, T., Wagner, B. and Strauss, B.: The nucleic acid content

of Neurospora Crassa. Arch. Biochem., 50.-442-445, 1959.

48. Nirenberg, M. W. and Matthaei, J. H.: The dependence of cell-free

protein synthesis in E. coli upon naturally occurring or synthetic
polyribonucleotides. Proc. Nat. Acad. Sci. USA, 47.- 1588-1 602, 1961.

118 Information Storage and Neural Control

49. Nomura, M., Hall, B. D., and Spiegelman, S.: Characterization ol

RNA synthesized in Escherichia coli after bacteriophage T2 infec-
tion. J. Molec. Biol, 2.-306-326, 1960.

50. Ogur, M., Minckler, S., Lindegren, G. and Lindegren, C. C: The

nucleic acids in a polypoid series of Saccharomyces. Arch. Biochem.,
40:175-184, 1952.

51. Osawa, S.: The nucleotide composition of ribonucleic acids from

subcellular components of yeast, Escherichia coli and rat liver
with special reference to the occurrence of pseudouridylic acid
in soluble ribonucleic acid. Biochim. Biophys. Acta, 42:2AA-25A, 1960.

52. Randall, C. G. and Gafford, L. G.: The Nucleic Acid oj Fowl Pox Virus.

Abstracts, Bacteriological Proceedings, 1961, p. 148.

53. Rich, A.: A hybrid helix containing both deoxyribose and ribose

polynucleotides and its relation to the transfer of information be-
tween the nucleic acids. Proc. Nat. Acad. Sci. USA, 46: 1044-1053
1960.

54. Rolfe, R. and Meselson, M.: The relative homogeneity of micro-

bial DNA. Proc. Nat. Acad. Sci. USA, 45.T 039-1 043, 1959.

55. Salzman, N. P.: The rate of formation of vaccinia deoxyribonucleic

acid and vaccinia virus. Virology, 70.- 150-1 52, 1960.

56. Schafer, W.: The Comparative Chemistry of Infective Virus Particles

and of Other Virus-specific Products: Animal Viruses. The Viruses I,
ed. by F. M. Burnet and W. M. Stanley, New York, Academic
Press, Inc., 1959, p. 475.

57. SchafFer, F. L., Moore, H. F. and Schwerdt, C. E.: Base com-

position of the ribonucleic acids of the three types of polio virus.
Virology, 70.- 530-537, 1960.

58. Schuster, H.: The reaction of nitrous acid with deoxyribonucleic

acid. Biochem. Biophys. Res. Commun., 2."320-323, 1960.

59. Schuster, H.: The Ribonucleic Acids of Viruses. The Nucleic Acids III

ed. by E. Chargaff and J. N. Davidson, New York, Academic
Press Inc., 1960, p. 245.

60. Schildkraut, C. L., Marmur, J. and Doty, P.: Determination of the

base composition of deoxyribonucleic acid from its apparent
density in CsCI. J. Molec. Biol, (in press).

61. Schildkraut, C. L., Marmur, J. and Doty, P.: The formation of

hybrid DNA molecules and their use in studies of DNA homologies.
J. Molec. Biol, (in press).

62. Schildkraut, C. L., Marmur, J., Fresco, J. R. and Doty, P.: Forma-

tion and properties of polyribonucleotide-polydeoxyribonucleotide
helical complexes. J. Bwl. Chem., 236:PC 2-4 (Feb.) 1961.

Pyrimidine Moieties in Animals, Plants, and Bacteria 119

63. Sibatani, A., Yamana, K., Kimura, K., and Takahashi, T.: Frac-

tionation of two metal^olically distinct classes of ribonucleic acids
in animal cells and its l:)earing on cancer. Nature, 786:215-217, 1960.

64. Simon, E. H.: Evidence for the nonparticipation of DNA in viral

RNA synthesis. Virology, 7J.- 105-1 18, 1961.

65. Sinsheimer, R. L.: A single stranded deoxyribonucleic acid from

bacteriophage 0X174. J. Molec. Biol., 7.-43-53, 1959.

66. Sinsheimer, R. L.: The Nucleic Acids of the Bacterial Viruses. The

Nucleic Acids III, ed. by E. ChargafF and J. N. Davidson, New
York, Academic Press Inc., 1960, p. 187.

67. Speyer, J. F., Lengyel, P., Basilio, C. and Ochoa, S.: Synthetic

polynucleotides and the amino acid code. II. Proc. Nat Acad. Sci.
USA, 48:63-68, 1962.

68. Spiegelman, S., Hall, B. D. and Storck, R.: The occurrence of

natural DNA-RNA complexes in E. coli infected with T2. Proc.
Nat. Acad. Sci. USA, 47; 11 35-1 141, 1961.

69. Spirin, A. S.: The "temperature effect" and macromolecular struc-

ture of high-polymer ribonucleic acids of various origin. Bio-
chemistry (USSR), 2(5.-454-463, 1961.

70. Stephenson, M. L. and Zamecnik, P. C: Purification of valine

transfer ribonucleic acid by combined chromatographic and chem-
ical procedures. Proc. Nat. Acad. Sci. USA, 47.- 1627-1 635, 1961.

71. Stevens, A.: Net formation of polyribonucleotides with base com-

positions analogous to deoxyribonucleic acid. J. Biol. Chem., 236
PC43-45 (July) 1961.

72. Sueoka, N.: Variation and heterogeneity of base composition of

deoxyribonucleic acids: A compilation of old and new data. J.
Molec. Biol., J.- 3 1-40, 1961.

73. Tessman, I.: Mutagenesis in Phage 0X174 and T4 and properties

of the genetic material. Virology, 9.-375-385, 1959.

74. Thomas, R. S.: The chemical composition and particle weight of

tipula iridescent virus. Virology, 74.-240-252, 1961.

75. Vendrely, R.: The Deoxyribonucleic Acid Content of the Nucleus.

The Nucleic Acids II, ed. by E. Chargaff and J. N. Davidson, New
York, Academic Press, Inc., 1955, p. 155.

76. Volkin, E. and Astrachan, L.: RNA Metabolism in T-2 Infected

Escherichia coli. The Chemical Basis of Heredity, Baltimore, The
Johns Hopkins Press, 1957, p. 686.

77. Watson, J. D. and Crick, F. H. C: The Structure of DNA. Cold

Spring Harbor Symposium on Qjiantitive Biology, /5'."123-131, 1953.

120 Information Storage and Neural Control

78. Watson, J. D. and Littlefield, J. W.: Some properties of DNA from

Shope papilloma virus. J. Molec. Biol., 2.- 161 -165, 1960.

79. Weiss, S. B. and Nakamoto, T.: Net synthesis of ribonucleic acid

with a microbial enzyme requiring deoxyribonucleic acid and four
ribonucleic triphosphates. J. Biol. Chem., 236:PCA9-20, 1961.

80. Weiss, S. B. and Nakamoto, T.: On the participation of DNA in

RNA synthesis. Proc. Mat. Acad. Sci. USA, 47.-694-697, 1961.

81. Weiss, S. B. and Nakamoto, T.: The enzymatic synthesis of RNA:

Nearest neighbor base frequencies. Proc. Nat. Acad. Sci. USA, 47:
1400-1405, 1961.

82. Woese, C. R.: Composition of various ribonucleic acid fractions from

microorganisms of diflferent deoxyribonucleic acid composition.
Nature, 759;920-921, 1961.

DISCUSSION OF CHAPTER V

E. Roy John (Rochester, New York): The topic which you
have reviewed is of particular interest to neurophysiologists be-
cause of several recent findings which seem to implicate RNA
in the storage of information in the nervous system. I was par-
ticularly interested by the fact that, in the system as you describe
it, RNA seems to be relatively insulated from the cellular environ-
ment in which it is located. Since a cellular information storage
mechanism must somehow reflect events in the environment, it
would seem that RNA would have to be excluded from considera-
tion if these processes were as immutable as you describe them.
Therefore, I would like to ask explicitly what you might expect
if for example, radical changes in ionic concentration were to
occur in the environment where the processes which you have
described were taking place. Would the outcome still be quite
as determinate as the impression that you gave? In relation to
this question, I recall a paper by Rudenberg and Tobias, in which
they were suggesting that a certain amount of calcium is bound
to RNA in axoplasm. Is it possible that various ionic substances
can bind to RNA in certain stages of these processes, modifying
the processes, so that the macromolecule which will be syn-
thesized is not completely specified by the RNA?

Saul Kit (Houston, Texas) : I think that your point is extremely
interesting. There are two aspects on which I might comment.

Pyrimidine Moieties in Animals, Plants, and Bacteria 121

First, the whole question of the ions is an extremely important
one. Ions are bound to the phosphoric acid groups of the nucleic
acid molecules. Possibly, they are involved in the regulation of
gene function. A DNA molecule has two functions — either to
serve as a template for its own replication — or to serve as a tem-
plate for the replication of RNA. What controls these alternative
functions? What are the controls which determine which and how
many of the present cells will be functional? The answer to these
questions possibly lies in the interaction between the nucleic acids,
cations, and certain proteins. Nucleic acid configuration and
length may be greatly modified by the ionic environment in which
the nucleic acid is found. This process may thus regulate some of
the cellular biosynthesis.

There was a point brought out in the discussion of Dr. Echol's
paper that should be made more explicit. It is possible that nucleic
acids function in the spread of information among cells. This may
be accomplished either by the free nucleic acids or by the viral
nucleic acids. The latter may be thought of either as messengers
or as transducing viruses. Herriot, in particular, has been in-
terested in the biological significance of extracellular nucleic acids.

Mike McGlothlen (Houston, Texas): In the synthesis of RNA,
the DNA would carry a code dictating the formation of RNA,
which in turn dictates the formation of proteins. Your last slide
showed the RNA forming on the DNA template a homologous
process to that proposed for reproduction of DNA. Why do you
have to have this apparent splitting of hydrogen bonds? Why not
have a similar code mechanism in the DNA, such as two or three
units of the DNA chain dictating the reproduction of one unit of
the RNA chain? Why the same mechanism for the production
of RNA and DNA from a DNA chain, rather than two different
methods involving dissimilar codes?

Heather D. Mayor (Houston, Texas): I think this question
results from difficulty in understanding the diagram. We do not
really know whether one part of the molecule is busy transcribing
RNA, while at the same time the rest of it is replicating DNA.
Even though these points are not settled, such a diagram is useful
for the purpose of documenting what is known to be going on.
It simply indicates the double helix coming apart in this manner

122 Information Storage and Neural Control

and transcribing in the same letters, but perhaps smaller, the
information which is in the DNA. Whether this is done by three
bases together in the DNA, or whether the number is twenty is
not at the moment important as long as we get the general effect
across.

Kit: The problem that faces us is this: Proteins vary greatly
in their length; they can be very short or they can be very long.
How could we obtain proteins of various sizes if the messenger-
RNA's were all of uniform sizes? I was deliberately ambiguous
on this point in the diagram because we do not really know how
many proteins are coded by one DNA molecule. There might
be several. In other words, we do not know where the periods
and where the commas are on the DNA chain.

Mayor: I am interested to see that the minimal infective
amount of DNA in the animal viruses appears to be around
4 X 10*^, whereas, the molecular weight of DNA, for instance, in
T-2 phage is about 120 x 10^ Would this be because more in-
formation is necessarily contained in the DNA of phage in com-
parison with the animal viruses, or would this be a problem in
redundancy again? Do you think that there must be more infor-
mation contained in the DNA of T-2 than in rabbit papilloma
SV-40, or any of the animal viruses?

Kit: I think it is quite likely that there is more information in
vaccinia or T-2 phage than in ^X174 polio or Shope papilloma
virus. The minimum information that must be present in a virus
is the information necessary to specify the protein coat of the
virus. For tobacco mosaic virus this would have to be enough
information to specify a protein having a molecular weight of
17,500. This would require about 900 nucleotides on the basis
of a triplet code, x^ctually, there are about 6,000 nucleotides in
a TMV-RNA chain. Thus, it is very likely that there is additional
information even in tobacco mosaic virus, or in other small
viruses. I think that Dr. Darnell will elaborate on this point in
connection with the genetic information brought in by T-2 phage
DNA for specific protein synthesis.

CHAPTER
VI

VIRUS ACTION AND REPLICATION*

James E. Darnell, Jr., M.D.

INTRODUCTION

VV HEN the genetic composition of organisms is tiiought of in
terms of information storage, it is immediately apparent why
viruses, which represent the smallest storehouses of biological
information, and thus probably the least complicated, have been
such popular research tools. Since the discox'cry of the nucleo-
proteinic and molecular nature of viruses in 1936, the study of
virus action and replication has contributed greatly to the present
knowledge of how genetic information is stored and expressed.
I will limit my discussion primarily to a summary of the events
in bacteriophage infection, the most thoroughly understood virus
cycle, and to a brief discussion about recent work using poliovirus
as a model for studying animal virus replication.

MACROMOLECULAR EVENTS IN
BACTERIOPHAGE INFECTION

Bacteriophage infection is initiated by attachment of the phage
particle to a susceptible cell followed by the injection of the DNA
of the phage through a hole produced in the cell wall by a lyso-
zyme contained within the phage tail (1).

Two types of response to infection by phage may occur in
bacteria: 1) the lytic response in which phage multiplication is
accompanied by cell death and lysis; 2) the lysogenic response in
which the phage genome becomes integrated with the host cell

*This work was supported in part by a research grant from the National Institutes
of Health (C-5789}.

123

124 Information Storage and Neural Control

and the ability to make this phage is transmitted as a heritable
property of the cell. Temperate phage infections may lead to
either of these results; intemperate phages can only cause the
lytic response. We shall be concerned mainly with describing the
synthetic capacities of cells undergoing the lytic response to infec-
tion by the intemperate T-even series of phages.

The entry into the cell of the DNA from such a phage brings
about immediate and dramatic changes in synthetic events within
the cell. Cellular DNA, RNA, and protein syntheses seem to be
stopped immediately and no further cell division takes place (2).
The DNA of the cell is digested by a DNAse and contributes
nucleotides to phage DNA (3, 4, 5). Although many enzymes and
metabolic pathways within the cell are able to function (2), the
integration of events which previously led to cellular macro-
molecular synthesis and continued cell growth is disrupted. Recent
experiments from several different laboratories (6) offer a reason-
able explanation of the subsequent events in the course of synthesis
ol new phage particles.

An outline of the new work is best begun by describing the
concept of messenger RNA as it functions in phage infection.
Current ideas of the genetic control of protein synthesis delegate
to DNA the role of carrier of genetic information. The structural
site of protein formation has been shown to be the ribosome (7, 8),
which, however, is composed of RNA and protein (9). Thus, it
has been presumed that some RNA molecule probably served to
transport information from the DNA to the ribosome. The first
evidence of such an RNA was obtained by Volkin and Astrachan
(10), who found in phage-infected cells that just after infection
a species of RNA was formed which had base ratios (substituting
uracil for thymine and cytosine for 5-hydroxymethylcytosine)
similar to those of infecting phage DNA. Ribosomal RNA does
not bear such a relationship to DNA (11). It has since been shown
that this newly fornied RNA is linked to ribosomes, which form
phage proteins, but is not the ribosomal RNA itself (12, 13).
Hall and Spiegelman (14) have performed a critical test of the
source of this RNA by demonstrating that it can combine phys-
ically by hydrogen bonding with phage DNA, which directed its
formation, but not with any other DNA. Thus, one concludes

Virus Action and Replication 125

that when DNA of an intemperate phage gets inside the ceU, the
cellular DNA is destroyed and can no longer serve as a source of
information. The phage DNA then assumes command of the
synthetic machinery in the cell via messenger RNA copied from
itself by hydrogen bonding between appropriate base pairs. This
results in formation by the cells' own ribosomes of phage-controlled
proteins only.

The next point of interest is: Wliat proteins does the virus
instruct the cell to make? The DNA of the T-even phages is
chemically peculiar in that it contains the base 5-hydroxymethyl-
cytosine (HMC) in place of the normally occurring" cytosine (15),
and in that some of the HMC residues hav^e glucose attached to
them after incorporation into the DNA chain (16). Thus, if
replication of the bacteriophage is to occur, a phage-infected cell
must be able to perform enzyme reactions not possible in an
uninfected cell. In the laboratories of Dr. S. S. Cohen and Dr.
Arthur Kornberg it has been demonstrated that phage-infected
cells do indeed acquire a large number of new enzyme activities
within minutes after infection (17, 18, 19). These are the first
proteins formed by the infected cell in order that phage DNA may
be replicated. Later in the course of infection, DNA synthesis and
structural phage protein synthesis begin.

Cell lysis, which occurs after several hundred new phages per
cell have been produced, is caused by the action inside the cell
of a lysozyme which is also formed under the genetic control of
the phage (20).

Many steps in the infectious cycle of the lytic phages are ob-
viously well understood on a molecular level. The intricacies of
the second type of response to phage infection, the lysogenic
response, have not yet been .so thoroughly elucidated. In a cell
which is infected by a temperate phage (one capable of inducing
lysogeny), as soon as the DNx^ enters the cell, differences from the
course of events in infection with a member of the T-series of
phages can be detected. Even if the pathway to the lytic response
is followed, the cell continues to synthesize cellular protein and
RNA (21), and can even be induced to foim new enzymes through-
out the latent period of the virus (22). If the cell is to become
lysogenized, then the synthesis of RNA, DNA, and protein stops.

126 Information Storage and Neural Control

In either case, the DNA of the cell is not destroyed. In the cell
undergoing the lysogenic response about a two-hour lag occurs
after infection before the cell resumes growth (23). The phage
genome has by this time become attached to the bacterial chromo-
some where it is carried in the form of prophage as a new genetic
character of the cell. According to current belief, phage genes in
a lysogenic cell do not function because a repressor is formed by
one phage gene which prevents the expression of the others (24).
Certain mutations (virulent) among temperate phages result in a
change in character of the phage so that it no longer can lysogenize
cells but can only lyse them. The mutation is presumably due to
a loss of the repressor. A strong piece of evidence in support of
this hypothesis is that separately arising, virulent mutants can
complement each other during mixed infections to produce
lysogenization (25, 26).

The basis for the profound difference in effect on the bacterial
chromosome between temperate and intemperate phages is ill
understood at present, but may be related in some way to the
close relationship between phage DNA and host DNA in the case
of the temperate phages. For instance, base ratios between tem-
perate phages and their hosts are similar (27). In addition, the
capacity of a bacterial cell to support multiplication of a temperate
phage is much more sensitive to inactivation by physical agents
which damage the nucleic acids than in the capacity to form lytic
phages (28). Also, unirradiated host cells are able to repair damage
to irradiated temperate phages, permitting growth of the phage.
It is therefore clear that during the replication of temperate phages
there is very close interrelationship between intact functioning
cellular DNA and phage DNA. It is widely presumed that this
affords opportunity for the attachment of the phage DNA to the
chromosome of the cell and for the establishment of lysogeny.

ANIMAL VIRUSES: EFFECT ON CELLULAR SYNTHESIS

With this brief summary of the possible interrelationships be-
tween bacteriophages and their hosts in mind, we will now discuss
the impact of viral infection on the synthetic processes in animal
cells. In the past ten years, techniques for the study of many

Virus Action and Replication 127

different animal viruses in cell cultures have been developed. This
approach lias, for the first time, allowed animal virology to be
studied at the cellular level with homogenous populations of cells
which can be simultaneously infected (30).

It is apparent that animal viruses present a vastly less homo-
genous group of agents tlian bacteriophages. The host range in-
cludes virtually all animals from single celled organisms to verte-
brates. These viruses range in size froml50A to 3000A; they are
of many different shapes and show great variability in chemical
composition. Moreover, the host cell for an animal virus is con-
siderably more complex than a bacterial cell. It is, tlierefore, not
surprising that the interrelationships between animal viruses and
their host cells are quite varied and complex. This discussion will
center on one animal virus-cell system, the poliovirus infected
HeLa cell, which is one of the most thoroughly studied animal
virus systems.

First, a few of the chemical and physical properties of polio-
virus should be stated (31). It is a small (300 A in diameter)
so-called spherical virus which is composed of RNA, 25 per cent,
and protein, 75 per cent. The RNA is enclosed within a protein
shell made of subunits which are arranged in a symmetrical form
(icosahedral) on the particle surface (32, 33). There are no lipids
or cell-derived macromolecules attached to the virus particle (31).
The infectious-unit-to-particle ratio is low (of the order of 1 in 100)
in both crude and purified virus suspensions (31).

The initial event of infection with poliovirus in a normally
susceptible cell is adsorption, which is, at least to some extent,
dependent on the ionic strength of the medium and on the presence
of divalent cations (34, 35). After adsorption the infectious virus
disappears rapidly from the surface of the cell. It can be shown
with virus labeled by P'^" in its RNA that all particles in a purified
suspension can adsorb to the cell but that about 50 per cent of
these come back off the cell in a non-infectious and non-adsorbable
state (36). The extracted RNA in these particles is still as infec-
tious as in an unexposed suspension, however. The RNA of most
of the particles which do remain attached to the cell is degraded
to small pieces. About 10 per cent of the attached particles remain
unchanged and about 10 per cent are changed in such a way that

128 Information Storage and Neural Control

the viral RNA has become susceptible to added RNAse but is
still in large molecular weight form. It is these latter particles
which could be expected to function as units responsible for virus
replication. There is still a great excess of particles (about 10 fold)
in this state over the number of actual infectious units, a fact for
which an adequate explanation is lacking at present.

The HeLa cell which is growing exponentially at the time of
poliovirus infection is drastically altered soon after infection.
Salzman et al. (37) have shown that net increases in protein, DNA
and RNA all cease upon infection. On the other hand, amino
acid incorporation continues after infection, but at a decreasing
rate (38), and incorporation of radioactive nucleic acid precursors
into RNA goes on at approximately the same rate but with a
different pattern. This changed pattern, however, is nonspecific
in the sense that halting growth by amino acid deprivation results
in the same change.

It is important to remember in considering experiments of this
nature that a polio infected cell makes at most only .5 per cent of
its dry weight into virus and that these substantial amounts of
incorporation represent more RNA and protein than eventually
appear as virus material. Whether this indicates the formation
of new material under the direction of the virus or the turnover
of pre-existing cellular components is unknown.

Late in the course of infection, cellular RNA is degraded and
lost into the medium as is a substantial amount of cellular protein
(37). This may represent a specific kind of loss. RNA is lost before
protein and both are lost prior to the liberation of virus into the
medium, a process which occurs as a burst and may be akin to
lysis of a bacterial cell after bacteriophage production (39, 40).

Poliovirus is an extreme example of a virus with a destructive
action on its host cell. Other viruses have been shown to possess
lethal capacity for host cells without the almost complete destruc-
tion which is accompanied by virus release. Examples of this type
of interaction are found in adenovirus (41), herpes simplex (42),
and vaccinia (43) infections. All of these are held intracellularly
to the extent of about 90 per cent. In each of these cases the virus
apparently contains DNA. In adenovirus and herpes infections
there is stimulation of DNA synthesis to greater than normal

Virus Action and Replication 129

levels (44, 45, 46) during the latent period. Another gradation of
the disruptive effect of a mukiplying virus on its host cell is found
in the case of the myxoviruses, influenza and Newcastle disease
virus. It has been shown clearly that, although cell death may be
the eventual outcome of encounters between these viruses and
HeLa cells (47), infected cells can definitely divide even after
production of viral protein has started (48).

One of the most interesting and perhaps most important kinds
of relationship in animal virology is the tumor virus-cell inter-
action. Of the tumor viruses which have been adapted to study
in cell culture, two, polyoma virus and Rous sarcoma virus, have
been most useful in following the outcome of individual cells after
infection (49, 50, 51). The influence of infection with these viruses
on the overall synthetic capacities of cells is not yet known. The
DNA-containing" polyoma virus has been shown to have, initially,
a lytic eff"ect on mouse and hamster cells which, except for being
slower, is similar in general pattern to the action of a virus like
polio (52).

Eventually, cultures which have been infected with polyoma
undergo a microscopic change and simultaneous biologic transition
to cultures which no longer produce virus (or do so at a very
low rate) but which have acquired the capacity to cause tumors
in animals. In an attempt to elucidate the role of the polyoma
virus in this transition, Vogt and Dulbecco have analyzed single
cells and have found that transformed cells which continue to
divide indefinitely do not produce virus and have an increased
resistance to infection by polyoma (53). They were unable to
obtain any evidence that the viral genome was present in the
cell (54). Thus, the vital question of whether the virus has inte-
grated with the cell to cause the change to a neoplastic unit or
whether the virus, by creating a selective pressure or by invoking
a developmental change, has aided in the establishing of a line
of cells with neoplastic capacity and increased virus resistance,
remains unanswered.

The other tumor virus which has been extensively studied in
vitro, using quantitative techniques, is the Rous sarcoma virus.
Rubin and Temin have analyzed the tumor cell transition in
chicken fibroblasts brought about by this RNA-containing virus

1 30 Information Storage and Neural Control

(55, 56). Under the usual experimental conditions Rous infected
cells do not lyse but become transformed after one to three days
into tumor cells which grow as changed clones of cells. Each
transformed cell is capable of liberating" virus at a very slow rate
while continuing to grow. This kind of integration between virus
and cell has no counterpart in bacteriophage systems.

In none of the animal virus systems studied is there any detailed
knowledge of how the damaging effect of the invading virus is
mediated, nor is there any understanding of how the tumor
viruses become integrated with the cell.

ANIMAL VIRUSES: FORMATION OF VIRAL
PRECURSOR MOLECULES

It will be recalled from the phage work that two classes of
macromolecules are formed after phage infection: 1) messenger-
RNA and "early" enzyme proteins in which the ultimate function
is to allow phage replication, and 2) the phage precursor DNA
and proteins which eventually form the new particles. In the case
of animal viruses this first class of new products has not been
proved definitely to exist. There are several reported instances
of materials which are apparently formed by infected cells after
virus infection, but whether these are genetically specified by the
infecting virus is unknown. This group of materials includes
interferon, which is produced by a variety of cells after infection
by a variety of viruses (57), a cell detachment factor produced
by adenovirus infected HeLa cells, which is serologically distinct
from the virus (58) and arginase, which is increased in cells
infected with papilloma virus (59). There is no apparent con-
nection between the formation of any of these substances and the
replication of the virus concerned.

Studies on newly formed products in infected cells have, there-
fore, been limited largely to the study of virus precursor molecules.
The techniques that have been most useful in identifying the time
and place of both synthesis of viral precursor molecules and
maturation of whole virus particles are: electron microscopy,
flourescent antibody staining and other types of immunologic
identification, extraction and measurement of infectious nucleic
acid, and radioisotopic labeling and purification of virus.

Virus Action and Replication 131

Two kinds of viruses, both containing RNA, have been studied
most extensively in this way— myxoviruses (specifically influenza
and fowl plague virus) and small spherical viruses (poliovirus,
encephalomyocarditis virus and Western equine encephalitis virus) .
I shall describe briefly the sequence of events in the formation of
fowl plague virus and poliovirus.

Fowl Plague Virus

Work in Schafer's laboratory in Tubingen has established that
fowl plague virus consists of at least two proteins plus a lipid and
RNA (60). One protein, which is associated with the RNA in
the S antigen and which can be released from the whole virus
particle by ether treatment, still in association with the RNA,
first becomes detectable by fluorescent antibody staining in the
nucleus of cells three hours after infection. By four hours after
infection it is also found in the cytoplasm. The RNA which is
enclosed by this protein is also presumed to be formed in the
nucleus. The hemagglutinating antigen, on the other hand, is
formed in the cytoplasm. By the use of 5-fluorophenylalanine
(FPA) as an inhibitor of viral formation, the sequence of forma-
tion of proteins has been determined. Infected cells exposed to
FPA before one hour after infection form no viral antigens. Since
the virus apparently penetrates the cell, this may be an indication
that a new protein other than viral precursor protein must be
formed to allow the subsequent steps in virus production to occur.
If two hours elapse before treatment, S antigen appears to be
formed normally, but neither the hemagglutinating antigen nor
the infectious virus is foimed. By three hours after infection the
production of hemagglutinating activity and infectivity have begun
to escape inhibition and by six to seven hours after infection FPA
has no effect. The formation of infectious particles is, however,
more sensitive to FPA than the formation of hemagglutinating
antigen, an indication that these are separate processes (61).

The virus particle thus has its origins in different parts of the
cell; then, by a transport process which also involves protein
synthesis, the whole particle is brought together at the cell surface
and completed. Nothing more is known about events within the
first hour of infection which are necessary for the initiation of
replication of viral precursor molecules.

132 Information Storage and Neural Control

Poliovirus Biosynthesis: Source and Time Course of Synthesis
of Viral Constituents

I will now describe the source and time course of synthesis of
the RNA and protein of poliovirus and then discuss some new
evidence relating to the questions of 1) the necessity for "early"
protein formation prior to actual virus replication, and 2) the role
of poliovirus RNA as a "messenger" RNA.

By differentially labeling the macromolecules (protein or RNA)
and the acid-soluble pool (amino acids or nucleotides) of HeLa
cells, and observing the production of poliovirus under these con-
ditions, it was shown that viral macromolecules were constructed
de novo in the infected cell from the acid-soluble pool. This was
true for both viral RNA and viral piotein (38, 62).

To determine the time at which viral macromolecules were
synthesized relative to the maturation cycle, radioactive precursors
of either protein or RNA were added to the medium of infected
cells at various times after infection and virus purified at the end
of maturation (38, 63). From the isotope content of the purified
virus could be determined how much of the virus had been syn-
thesized at the time of addition of the radioisotope. Virus protein
and virus RNA were shown to be formed between two and one-
half and six hours after infection and there was very little lag
between the onset of formation of viral macromolecules and of
the whole virus. An independent confirmation of this last state-
ment was obtained by determining the times of formation of
infectious RNA (ribonuclease sensitive plaque-forming activity)
and of whole virus. Here the very earliest increases of infectious
material could be determined (the first 0.1 per cent of new virus
or infectious RNA). It was found that infectious RNA began to
increase at about 2-2.5 hours while an increase in whole virus
began approximately thirty minutes later. It would appear, then,
that for the first 2-2.5 hours of the infectious cycle the cell does
not make virus precursor molecules.

The essential findings of the above experiments are borne out
in electron microscopic investigations of infected cells. Home and
Nagington (33) found evidence in electron photomicrographs of
circumscribed areas of apparent multiplication of protein sub-
units in the cytoplasm of HeLa cells, beginning about three hours

Virus Action and Replication 133

after infection. Fogh and Stuart (64) published beautiful pictures
of crystalline arrays of whole virus particles in the cytoplasm of
several kinds of cells. These crystals first appeared five to six
hours after infection.

In an effort to determine whether protein synthesis is required
for any steps in poliovirus multiplication prior to the foimation
of virus precursor molecules, experiments using inhibitors of pro-
tein synthesis were performed during that time period. The effect
of the inhibitors on whole virus multiplication and on infectious
RNA formation was measured (65).

The amino acid analog" 5-fluorophenylalanine, which had
originally been shown by Ackerman et al. (66) to inhibit polio-
virus multiplication, was found to be effective at a concentration
of about 0.05 mM in completely preventing whole virus synthesis
while affecting synthesis of infectious RNA only slightly, if at all.
If FPA was left in a culture past the usual time for the onset of
maturation and then the effects of the drug were reversed by
addition of a large excess of L-phenylalanine, maturation and
virus-protein synthesis began within an hour, indicating that all
possible steps except the formation of viral coat protein had
occurred in a normal fashion in the presence of FPA. Puromycin, a
drug which inhibits protein synthesis specifically in several systems
in a rather different manner from that of FPA (67, 68, 69), was
found to inhibit both infectious RNA and whole virus synthesis
when added prior to two hours after infection. If the puromycin
was added at 2.5 hours, then about 15 per cent as much infectious
RNA was formed as in controls, without the formation of any
wliole virus; and if puromycin was added at three hours, the
cells produced 30 to 100 per cent of the normal amounts of infec-
tious RNA but only about 5 to 10 per cent of the normal yield
of whole virus. Addition of puromycin at the outset of infection
and removal 1.5 hours later resulted in a corresponding delay in
the beginning of infectious RNA synthesis.

All these experiments taken together provide suggestive evidence,
but not proof, that the synthesis of some material, probably protein
in nature, is necessary for initiating the replication of poliovirus
RNA. This step in virus formation is not sufficiently complete
by two hours after infection to allow RNA replication.

134 Information Storage and Neural Control

The discovery by Nirenberg and Matthaei (70) of an in vitro
system for synthesizing protein which is dependent on the addition
of a messenger-RNA offers the opportunity to attack this problem
directly. In this system which is derived from Escherichia coli cells,
these workers found that tobacco mosaic virus RNA stimulated
the formation of TMV coat protein, indicating that the virus RNA
was itself the active messenger in protein synthesis. We have found
recently that this system also responds to the addition of poliovirus
RNA by producing material which will specifically precipitate
with poliovirus antiserum. Thus, if the poliovirus RNA specifies
the formation of other proteins, these should also be formed in
this in vitro system.

We can speculate as to the type of protein tiiat poliovirus might
require to assure its own synthesis in HeLa cells. The virus con-
tains no nucleic acid bases (31) or amino acids (38) foreign to the
cell. Moreover, with tiie possible exception of guanine and cytosine
nucleotides, the cell contains more than enough acid-soluble
material to provide for the synthesis of all viral material formed.
Tlius some enzyme protein of a type the cell already possesses,
but which is either under cellular control or is located at a position
in the cell which is inaccessible to the virus, might be used by the
virus as a mechanism for escaping cellular control.

REFERENCES

1. Kozloff, L. M., Lute, M., and Henderson, K.: Viral Inv^asion I,

II, III. J. Bio. Chem., 225.-51 1-546, 1957.

2. Cohen, S. S.: Growth requirements of bacterial viruses. Bad. Rev.,

7J.-1-24, 1949. Cohen. S. S.: Studies on Controlling Mechanisms
in the Metabolism of Virus-Infected Bacteria. CSH Symp. Qjtant.
Biol., 7<§.-221-235, 1953.

3. Luria, S. E., and Human, M. L.: Chromatin staining of bactei'ia

during bacteriophage infection. J. Bact., 59.-551-560, 1950.

4. Kozloff, L. M.: Origin and Fate of Bacteriophage Material. CSH

Symp. Quant. Biol, 75;209-220, 1953.

5. Hershey, A. D.: Bacteriophages as genetic and biochemical systems.

Advance Virus Res., 4:25-6\, 1957.

6. Cold Spring Harbor Symposia on Quantitative Biology, vol. 26,

1961, in press.

Virus Action and Replication 135

7. McQuillen, K., Roberts, R. B., and Britten, R. J.: Synthesis of

nascent protein by ribosomes in E. coli. Proc. Nat. Acad. Sci. USA,
45:\437, 1959.

8. Tissieres, A., Schlessinger, D., and Gros, F.: Amino acid incorpora-

tion into proteins by E. coli ribosomes. Proc. Nat. Acad. Sci. USA,
4(5.- 1450-1 463, 1960.

9. Tissieres, A., Watson, J. D., Schlessinger, D., and Hollingworth,

B. R.: Ribonucleoprotein particles from E. coli. J. Molec. Biol.,
7.-221-233, 1959.

10. Volkin, E. and Astrachan, L.: RNA Metabohsm in T-2-Infected

E. coli. In McElroy and Glass, The Chemical Basis of Heredity.
Baltimore, Johns Hopkins Press, 1957.

11. Spahr, P. F., and Tissieres, A.: Nucleotide composition of ribo-

nucleoprotein particles from E. coli. J. Molec. Biol., 7:237-239, 1959.

12. Gros, F., Hiatt, H., Gilbert, W., Kurland, C. G., Risebrough, R. W.,

and Watson, J. D.: Unstable ribonucleic acid revealed by pulse
labelling of E. coli. Nature, 790:581-584, 1961.

13. Brenner, S., Jacob, F., and Meselson, M.: An unstable intermediate

carrying information from genes to ribosomes for protein synthesis.
Nature, 190:576-580, 1961.

14. Hall, B. D., and Spiegelman, S.: Sequence complementarity of

T2-DNA and T-2 specific RNA. Proc. Nat. Acad. Sci. USA, 47:
137-146, 1961.

15. Wyatt, G. R., and Cohen, S. S.: A new pyrimidine base from bac-

teriophage nucleic acids. Nature, 770:1072-1073, 1952.

16. Sinsheimer, R. L.: The glucose content of the deoxyribonucleic acids

of certain bacteriophages. Proc. Nat. Acad. Sci. USA, 42:502, 1956.

17. Flaks, J. G., Cohen, S. S., and Lichtenstein, J.: Virus-induced

acquisition of metabolic function I, H. J. Biol. Chem., 2J4:1501-
1511, 1959.

18. Kornberg, A., Zimmerman, J. B., Kornberg, S. R., and Josse, J.:

Enzymatic synthesis of DNA, influence of bacteriophage T2 on
the synthetic pathway in host cells. Proc. Nat. Acad. Sci. USA,
45:772-785, 1959.

19. Aposhian, H. V., and Kornberg, A.: The DNA polymerase formed

after bacteriophage infection of E. coli — A new enzyme. Fed. Proc,
20:361, 1961.

20. Streisinger, G.: CSH Symp. on Quant. Biol., vol. 28, 1961. In press.

21. Siminovitch, L.: Biochemical modifications of the bacterial host

during bacteriophage development. Ann. Inst. Pasteur, 84:265-212,
1953.

136 Information Storage and Neural Control

22. Siminovitch, L. and Jacob, F.: Biosynthese induite d\m enzyme

pendant le developpement des bacteriophages chez E. coli K12.
Ann. Inst. Pasteur, 83:1 AS, 1952.

23. Frey, B. A., and Gros, F.: The metaboUc activities of E. coli during

the establishment of lysogeny. J. Gen. Microbiol., 27.-655-692, 1959.

24. Jacob, F.: Genetic Control of Viral Functions, in Harvey Lectures.

1958-1959, New York, Academic Press, Inc., 1960.

25. Kaiser, A. D.: Mutations in a temperate phage P22 and lysogeny in

salmonella. Virology, 3; 42-61, 1957.

26. Levine, M.: Mutations in the temperate phage P22 and lysogeny

in salmonella. Virology, 3:22-4\, 1957.

27. Luria, S. E.: Control of Bacterial Properties by Bacteriophage Genes.

Symposium on Virus-Cell Relationship, Oholo, Israel, 1961.

28. Garen, A., and Zinder, N. D.: Radiological evidence for partial

genetic homology between bacteriophage and host bacteria.
Virology, h'iAl-^ie, 1955.

29. Luria, S. E.: The multiplication of viruses, Protoplasmatologia.

Handbuch der Protoplasmqforschung, 4:1-63, 1958.

30. Dulbecco, R.: Interactions of viruses and animal cells. Physiol. Rev

J5.-301-335, 1955.

31. Schaffer, F. L., and Schwerdt, C. E.: Purification and properties

of poliovirus. Advance Virus Res., 6;159-204, 1959.

32. Fmch, J. T., and Klug, A.: Structure of poliomyelitis virus. Nature,

7.5J.-1709-1714, 1959.

33. Home, R. W., and Nagington, J.: Electron microscope studies of

the development and structure of poliomyelitis virus. J. Molec.
Biol., 7.-333-338, 1959.

34. Bachtold, J. G., Bubel, H. C, and Gebhardt, L. P.: The primary

mteraction of poliomyelitis virus with host cells of tissue culture
origin. Virology, 4.- 582-589, 1957.

35. Holland, J. J., and McLaren, L. C: The mammalian cell-virus

relationship II. Adsorption, reception and eclipse of poliovirus by
HeLa cells. J. Exp. Med., 109:487, 1959.

36. Joklik, W. K., and Darnell, J. E.: The adsorption and early fate of

purified poliovirus in HeLa cells. Virology, 13:439-447, 1961.

37. Salzman, N. P., Lockart, R. Z., and Sebring, E. D.: Alterations in

HeLa cell metabolism resulting from poliovirus infection. Virology.
P.-244-259, 1959.

38. Darnell, J. E., and Levintow, L.: Poliovirus protein: Source of amino

acids and time course of synthesis. J. Biol. Chem., 235:74-77, 1960.

Virus Action and Replication 1 37

39. LwofF, A., Dulfiecco, R., Vogt, M., and Lwoff, M.: Kinetics of the

release of poliovirus from single cells. Virology, /.•128-139, 1955.

40. Howes, D. W.: The growth cycle of poliovirus in cultured cells III.

The asynchronous response of HeLa cells multiply infected with
Type I poliovirus. Virology. 9.'1 10-126, 1959.

41. Boyer, G. S., Leuchtenberger, C, and Ginsberg, H. S.: Cytological

and cytochemical studies of HeLa cells infected with adenovirus.
J. Exp. Med., 7(95.- 195-2 16, 1957.

42. Stoker, M. G. P., Smith, K. M., and Ross, R. W.: Electron micro-

scopic studies of HeLa cells infected with herpes vn-us. J. Gen.
Microbiol., 79.-244-249, 1958.

43. Salzman, N. P.: The rate of formation of vaccinia DNA and vaccinia

virus. Virology, 70.T50-152, 1959.

44. Ginsberg, H. S., and Dixon, M. K.: Deoxyribonucleic acid and

protein alterations in HeLa cells infected with Type 4 adenovirus.
J. E.xp. Med., 109:407-422, 1959.

45. Green, M., and Daesch, G. E.: Biochemical studies on adenovirus

multiplication H. Kinetics of nucleic acid and protein synthesis
in suspension cultures. Virology, /J.T69-176, 1961.

46. Newton, A., and Stoker, M. G. P.: Changes in nucleic acid content

of HeLa cells infected with herpes virus. Virology, 5.-549-560, 1958.

47. Marcus, P. L, and Puck, T. T.: Host-cell interaction of animal

viruses L Titration of cell-killing by viruses. Virology, 6.- 405 423,
1958.

48. Wheelock, E. F., and Tamm, L: Mitosis and division in HeLa cells

infected with influenza or Newcastle disease virus. Virology, 8:
532-536, 1959.

49. Temin, H. M., and Rubin, H.: Characteristics of an assay for Rous

sarcoma virus and Rous sarcoma cells in tissue culture. Virology,
6.-669-688, 1958.

50. Winocour, E., and Sachs, L.: A plaque assay for the polyoma virus.

Virology, <?.-397-400, 1959.

51. Dulbecco, R. and Freeman, G.: Plaque production t^y the polyoma

virus. Virology, 5.-396-397, 1959.

52. Vogt, M., and Dulbecco, R.: Virus-cell interaction with a tumor-

producing virus. Proc. Nat. Acad. Sci. USA, 46:365-370, 1960.

53. Dulbecco, R., and Vogt, M.: Significance of continued virus pro-

duction in tissue cultures rendered neoplastic by polyoma virus.
Proc. Nat. Acad. Sa. USA, 46:\6\7-\623, 1960.

54. Vogt, M. and Dulbecco, R.: Studies on cells rendered neoplastic

by polyoma virus. Virology, 16:41-51, 1962.

1 38 Information Storage and Neural Control

55. Temin, H. M., and Rubin, H.: A kinetic study of infection of chick

embryo cells in vitro by Rous sarcoma virus. Virology, 5;209-222,
1959

56. Rubin, H., and Temin, H. M.: A radiological study of cell-virus

interaction in the Rous sarcoma. Virology, 7:75-9\, 1959.

57. Isaacs, A.: The Nature and Function of Interferon. Perspectives in

Virology, New York, Wiley, 1960, vol. 2.

58. Rose, W. R., Hartley, J. W., Roizman, B., and Levy, H. B.: Charac-

terization of a factor formed in the course of adenovirus infection
of tissue cultures causing detachment of cells from glass. J. Exp.
Med., 108:713-729, 1958.

59. Rogers, S.: Induction of arginase in rabbit epithelium by the Shope

rabbit papilloma virus. Nature, 75J.T815-1816, 1959.

60. Schafer, VV.: Some Observations Concerning the Reproduction of

RNA-Containing Animal Viruses. In Virus Growth and Variation,
New York, Cambridge University Press, 1959, p. 61.

61. Zimmerman, T., and Schafer, W.: Effect of fluorophenylalanine on

fowl plague virus multiplication. Virology, 7/.-676-698, 1960.

62. Saizman, N. P., and Sevring, E. D.: The source of poliovirus ribo-

nucleic acid. Virology, 7J.-258-260, 1961.

63. Darnell, J. E., Levintow, L., Thoren, M., and Hooper, L.: The

time of synthesis of poliovirus RNA. J^irology, 13:271-279, 1961.

64. Fogh, J., and Stuart, D. C, Jr.: Intracellular crystals of polioviruses

in HeLa cells. Virology, 77.-308-311, 1960.

65. Levintow, L., Thoren, M., Darnell, J. E., and Hooper, L.: The

Effect of 5-fluorophenylalanine and Puromycin on the Replication
of Poliovirus (in press, 1961).

66. Ackerman, W., Rabson, A., and Kurtz, H.: Growth characteristics

of poliomyelitis virus in HeLa cell cultures: Lack of parallelism in
cellular injury and virus increase. J. Exp. Med., 7(9O.-437-450, 1954.

67. Cohen, G. N., and Munier, R.: Effects des Analogues structuraux

d'amino acides sur la crossance, la synthese des proteines et la
synthese d'enzymes chez E. coli. Biochim. Biophys. Acta., 31:347-
356, 1959.

68. Nathans, D., and Lipmann, F.: Amino acid transfer from amino-

acyl-ribonucleic acids to protein on ribosomes of E. coli. Proc.
Nat. Acad. Sa. USA, 45.T721-1729, 1961.

69. Yarmolinsky, M. B., and De La Haba, G. L.: Inhibition by puro-

mycin of amino acid incorporation into protein. Proc. Nat. Acad.
Sci. USA, 45:1721-1729, 1959.

Virus Action and Replication 1 39

70. Nirenberg, M., and Matthaei, J. H.: The dependence of cell-free
protein synthesis in E. coli upon naturally occurring or synthetic
polyriljonucleotides. Proc. Nat. Acad. Sci. USA, 47. ■ \ 588 -\ 602, 1961.

DISCUSSION OF CHAPTER VI

Saul Kit (Houston, Texas): I sincerely congratulate Dr. Darnell
on his very interesting and informative paper. He may have
already settled this point, but it is one that is extremely important
to us. It has been postulated that the messenger-RNA is destroyed
at the time the proteins are synthesized and that, as a result of this,
there is a need for continual synthesis of messenger-RNA. Dr.
Darnell's system would seem to be an excellent one for testing
this possibility, since after synthesis of the polio proteins in the
reconstructed ribosome system, one could re-extract the polio RNA
and test for infectivity. I wonder whether he has already done this.

James E. Darnell, Jr. (Cambridge, Massachusetts): It turns
out not to be necessary to try to extract the polio RNA and to
look for any infectivity. Rather, what happens is this: The Niren-
berg system is made from E. coli, and E. coli ribosomes contain quite a
large amount of ribonuclease. It has been shown in Watson's
laboratory (J. D. Watson, personal communication) that natural
messenger-RNA from E. coli, which can be attached in vitro to the
ribosomes, is broken down completely during protein synthesis.
This is the case with polio also. However, we cannot say definitely
that this is linked to protein synthesis because the breakdown goes
on if the system is incubated in the absence of an ATP generating"
system. One possible way to get around this is to use ribosomes
from cells which do not contain ribonuclease, such as Bacillus
megaterium. An additional point of interest in this general area
is, of course, whether messenger-RNA from animal cells is handled
difTerently from bacterial messenger-RNx'\.

Kit: Are the E. coli ribosomes not stabilized sufficiently by the
amount of magnesium used in your medium so that there is no
release of ribonuclease activity?

Darnell: Not completely. Incubation of polio RNA in the
synthesizing system without an energy source, and, therefore, with
no resulting protein synthesis, still results in degradation of the
viral RNA.

CHAPTER
VII

THE INFORMATION CONCEPT IN ECOLOGY:
SOME ASPECTS OF INFORMATION -GATHER-
ING BEHAVIOR IN PLANKTON**

Bernard C. Patten, Ph.D.

INTRODUCTION

.HE subject of community energetics concerns the processes
by which ecological coinmunities achieve a favorable balance be-
tween energy gains and losses. Referred to as the study of pro-
ductivity, or trophodynamics, this is one of the most active areas
of investigation in modern ecology. A central concept in this work
is that of the food chain (1), or food web (2), beginning with
photosynthesizing plants and proceeding through various trophic
levels toward a terminal consumer, or consumers. Such a network
is illustrated in Figure 1, where the Si represent "species": Si
being the sun, So and Ss producers (plants), ^'4-^7 primary con-
sumers (herbivores), and .^7-^^11 secondary consumers (carnivores).
Note that ^^7 is an omnivore since it consumes both plant and
animal material. For simplicity, decomposers are not shown. The qj
denote pathways of energy flux: for example qs signifies that energy
is gained by Si through eating S2.

In the mid-1 940's when energy ecologists were involved in
working out the intricacies of food chain relationships on a who-
eats-whom-and-how-much basis, a physicist, E. Schrodinger, as-
serted that living organisms feed upon "negative entropy" (3).

*Contribution No. 120 from the Virginia Institute of Marine Science.

f The following biochemical abbreviations are used in this paper.
ADP = adenosine diphosphate; ATP = adenosine triphosphate; DPN = diphospho-
pyridine nucleotide; FMN = flavin mononucleotide; PN = pyridine nucleotide; PNH2
= reduced pyridine nucleotide; TPN = triphosphopyridine nucleotide.

140

Information Concept in Ecology

141

Since everyone in ecology understood that organisms ate food and
that they did so for the energy it contained, Schrodinger's sug-
gestion was ignored. In 1949, however, another physicist, Brillouin
(4), elaborated further in a manner more harmonious with the
climate of trophodynamic thought: "The earth ... is constantly
receiving energy and negative entropy from outside . . . life feeds
on high grade energy or 'negative entropy' ... all experimental
measures show that the entropy of the refuse is larger than that
of the food." This established a definite connection with ecological
thinking, and further rapport developed in 1953 with the appear-
ance of papers by Branson (5) and Linschitz (6) which amplified
the theme along thermochemical lines. This work was soon fol-
lowed by several publications in ecological journals which at-
tempted to relate information theory to community trophociy-
namics (7, 8).

In the present paper some aspects of the organization and
behavior of plankton communities are discussed in an informational
and trophodynamic context. In the development, such communities
will come to take the logical form of a rational utility-seeker (9),

'7 "'8 ~9 ^10 ^11

Fig. 1. Schematic diagram of a hypothetical food chain.

142 hiformation Storage and Neural Control

where utility is understood to have a hedonistic value in either
1) reducing" the community's uncertainty about nature, or 2) per-
mitting purchase of a measure of certainty. In the latter connection,
energy will be identified as a universal currency. We begin with
a result from communication theory.

SHANNON'S THEOREM 10

Consider, following Shannon (10), a discrete communication
channel fed by an information source. If H{x) is the input entropy
and H{y) tnat of the output, H{x,j) is the joint entropy of input
and output, and H{y\x) and H{x\y) are conditional entropies, then

[1] H{x,y) = H{x) + H{,j\x) = H{y) + H{x\ij).

For such a system SJiannon proved his Theorem 10: If a correction
channel has a capacity H{x\y), correction data can be encoded in
such a manner that all but an arbitrarily small fraction of errors
induced by noise can be corrected. This is not possible if the
channel capacity is less than //(.vjj), which represents the amount
of information which must be supplied to correct the message.

This theorem has been exploited by Ashby (11, 12) as the basis
for a cybernetic theory of biological homeostasis. Roughly, the
organism is regarded as bomJDarded by information from an
environment which tends to drive the organism into states outside
the limits which permit survival. To achieve stability, therefore,
it becomes necessary in light of Shannon's theorem for the organism
to provide information to a "regulator" (analogue of correction
channel) in amounts at least as great as the disturbances. Ashby
calls this the law of requisite variety. It implies that an organism
must continually be concerned with having sufficient information
available (accumulated against the gradient imposed by the
second law of thermodynamics) to meet particular environmental
threats. The compatibility of this theory with the Schrodinger-
Brillouin thesis is evident. Also apparent is the fact that its basic
applicability is unaltered by a conversion from the scale of organism
to that of ecological community: both units metabolize, both are
subject to the same "heat death" and, consequently, both have
similar problems of homeostasis.

Information Concept in Ecology 143

COMMUNITY STABILITY

It is almost axiomatic in ecology that structurally complex
communities are intrinsically more stable than simpler ones. The
standard illustration is to contrast the highly stable biota of
tropical rain forests with the comparatively unstable assemblages
of the Arctic tundra. Community stability and complexity can be
related in the following manner.

At any specified stage in its development, a community con-
tains m species, s,, of plants and animals with frequencies A'',- such
that

[21 Y.Ni = N. {i=\,2,...,m)

1=1

The uncertainty per individual of selecting the i ' species is

where P is probability. The total uncertainty, N < D > , referred
to as community diversity, is

f4] D = -XlAMogPCs,).

(=1

Although the diversity problem is not of specific concern here, it
should be mentioned that there has been considerable develop-
ment of this subject along informational lines (13, 14, 15), repre-
senting the most extensive application of information theory which
has so far been made to an ecological problem.

MacArthur (16) has proposed that community stability be
equated to the complexity of the food web as given by an entr'opy
measure:

[5J S= -Z^^(7.)logP(r/,),

where S is stability and Piqj) the probability of energy traversing
a particular path qj. The rationale of this suggestion is that removal
of a species and consequent destruction of the pathways leading
to and from it would be less disruptive to a community with a
high value for S than to one with a lower value. Since the P{qj)'s
are obviously functions of the A^,'s, it follows that stability and
diversity are related, the more diverse systems being the more
stable. Since greater stability implies greater success in meeting"

144 Information Storage and Neural Control

the imperative of Shannon's Theorem 10, and since stabihty is a
function of compositional complexity, it follows that a natural
tendency of ecological communities should be to develop to maxi-
mum proportions within the limitations imposed by particular
environments. This conclusion is consistent with empirical ob-
servations.

One measure of the extent to which a given community has
expanded to fill a physical space is the total quantity of organic
matter contained in that space. This variable will be referred to
here as the community's biomass. Because community ontogeny
(ecological succession) proceeds by means of niche (17) prolifera-
tion (more species make more species possible), a reasonable way
to assess, in a quantitative sense, the extent of organization of a
community might be to oxidize a suitable sample in a calorimeter
and to equate heat evolution with intrinsic complexity. Though
admittedly crude, such an approach would not be entirely without
basis since all information, even that which is abstract, is under-
stood to be physically based and is therefore referable to thermo-
dynamic negative entropy (18, 19). This broaches the problem of
the relationship between information and energy — the reason why
information theory is of interest to energy ecologists.

ENERGY AS CURRENCY

The connection between energy and information has been
well established in the context of macroscopic thermodynamics
(19) where adiabatically accessible system states are generally
regarded as informationally equivalent, while those attainable
only non-adiabatically are not (20). In the usual Boltzmann-
Gibbs treatments, the role of matter in determining a system's
entropy is obscure; however, the recently introduced formalism
of Jaynes (21) and Tribus (22) offers considerable clarification,
as follows.

Consider a system of /?«, rib, ■ ■ ■ particles of matter of kinds
a, b, . . . in a phase space with coordinates Xi, X2, .... When the
coordinates are prescribed and the number of particles known,
the system consists of a finite number of discrete quantum states,
J, with energies e^.-

Injormation Concept in Ecology 145

|6] ey = eO'; Tia, Ub, . . .;Xi, X2, . . .).

If/?, is the probability for a particular small subsystem to be in
state I, then,

[7] Z V, = 1

i
[8] 11V^^^ = <f>

[y] Y^V^na.i = <>L>, (a,l>,c, . . .)

and

[10] *S:= -kY^P^np,,

where S is the entropy of the system, and < > denotes expected
values. The maximum uncertainty of selecting" a subsystem in state
I is obtained when the p^s are all ecjual (10). Maximizing [10]

[11] "T=? ^^''^'^ l)dp, = 0;

difTerentiating [7], [8] and [9] and introducing the undetermined
Lagrangian multipliers ««, «&, • . ■ , 1^, ^a, we obtain

[12] (Qo- l)T.dp. = 0

[13] (3Y.e.dp,=0

[14] a^X) na.,dpi = 0. {a,h,c, . . .)

Adding" [11-14] and collecting" terms:

[15] XI Oil P' + ^^0 -\- ^U + a,, Ha., + abni,, + . . .) fZ/J/ = 0,

from which

[16] p, = exp ( — Qo — (Se,- — aa nn.i — ab Hb.i —...).

Thus, a system's entropy is maximal when the e,'s, na,i's, «6, /s,
etc., of all its subsystems are identical, signifying a homogeneous
distribution of matter and energy throughout the system. It can
be shown (22) that the rate of entropy change is

146 Information Storage and Neural Control

dS = k (J3d <e> + aa d <Ha> + "& d <nb> + . . .

If two systems with different values of /3, «„, a^, . . . , and witli
total energy and matter constant between them, are allowed to
interact irreversibly, then the energy gain of one must be equiva-
lent to the loss of the other, and the gain in n,j, n,^^ . . . by one
corresponds to that lost by the other. In the language of game
theory (23) such a relationship is zero-sum. \idXi = dX-i = . . . =0,
then the connected system's entropy change is, from [17],

[18] dS - k [(j3 - 13') d <€> + {aa - aa) d <Ha>

+ (ab — ab') d <nb> + . . .]

Hence, it is possible for one of the systems (call it community)
to decrease its entropy at the expense of the other (environment)
since the only requirenient is that dS > 0 overall.

Details of energy-matter exchange between such systems are
very complex because the parameters 3, aa, at, ■ . . may become
reciprocally coupled within a system

d<e> _ _ a"Qo

[19] ) {a,b,c, . . .)

d<na> _

6/3 ~ ' daa dl3l
SO that

[20] d<e> = -^ dl3 - -^ daa (a,b,C, ...)

and

d^- " dl3 daa

d''QiO ,,, 3"Qo

[21] d<na> = - T ~dl3 - ~r—;daa {a,h,c, . . .).

daa dp daa"

The important point to distinguish for our present purpose is that
for one system to diminish its entropy with respect to another
with which it is coupled in communication, it must establish and
maintain physical barriers to the free exchange of energy and
matter. In short, it must proliferate structural heterogeneity by
maximizing the inequality of the /^,'s in [10].

At the community level, such heterogeneities are maintained
by a graded series of discrete, functional barriers ranging from, at

Information Concept in Ecology 147

the lower end of the scale, quantum states, atoms, molecules,
membranes, cells and their ultrastructural components, tissues and
organs, to, at the upper end, individual organisms, species, popu-
lations, multi-specific evolutionary units (supraorganisms) (24)
and finally the community itself. The construction, maintenance
and operation of such barriers (with all the morphology and
physiology that this implies) are achieved by physical and chem-
ical processes which, in net, are endergonic. Without, therefore,
a continuous input of energy, the barricades would fail to function
and would ultimately be eroded away, with the result that com-
munity and environment would become one.

This is a trivial conclusion, of course. After all, it is one of the most
obvious statements which could be made regarding bio-systems.
Yet its articulation seeins necessary to provide a basis for the follow-
ing restatement of the Schrodinger-Brillouin proposition: Energy
may be regarded as a universal currency with which organisms pur-
chase utility, as negative entropy, from the environment.

COMMUNITY BIOENERGETICS

In view of this proposition, the ultimate source of negativt
entropy to an ecological community may be regarded to be
photons. When a photon strikes an atom an electron is lifted from
ground state to a higher empty orbital (vertical arrow ^~ -^^* in
Fig. 2). For most molecules excited electrons usually drop back
to ground state immediately, dissipating the excess energy as
electromagnetic radiation (broken arrow, Fig. 2). Living systems
to paraphrase Szent-Gyorgi (25), have shoved themselves between
these two processes by shunting the excited electrons into different
downhill pathways in which their energy can be released slowly
and put to useful work. The first step in the process is excitation
(by photons, or indirectly via accessory plant pigments) of pi
electrons in the conjugated portion of chlorophyll a. In cyclic
photosynthetic phosphorylation (26) the chlorophyll provides these
electrons directly, thereby acting both as electron donor and
acceptor (Fig. 2). In noncyclic photophosphorylation the electrons
come from H2O, which the excitation energy decomposes to
oxygen, freed as O2, and H atoms. The hydrogen electrons sub-

148

Information Storage and Neural Control

Fig. 2. Schematic diagram of the energy cycle of an ecosystem, modified and

expanded after Szent-Gyorgi (25) and Arnon (26). Anaerobic and chemosyn-

thetic processes are not indicated.

sequently reduce one of two pyridine nucleotides {PN -^ PNHi,
Fig. 2). Concurrently, ATP is synthesized, incorporating into its
terminal "high energy" phosphate bond some of the original
photon energy. Neither ATP, DPN, nor TPN is stable enough
to function in energy storage. This is accomplished by reducing
CO 2 to carbohydrates and water, then to lipids (Fig. 2).

Energy so stored may be utiHzed directly by the primary pro-
ducer, or it may be transmitted to other organisms in the food
chain. The retrieval of energy from storage is accomplished by
transferring electrons (in H atoms) to PN, releasing the carbon
as CO 2. The PNHi then transfers electrons to flavin mononucleo-
tide (FMN), whence they cascade down the oxidative chain of
cytochromes, generating heat at every step. Most of the energy
remaining is converted (in oxidative phosphorylation) to .4 TP,
in which form it is available for the performance of cellular work.
Finally, the electrons are transferred to Oo which then binds
protons to form HoO. Water represents ground state, where the
cycle e~ —> e* -^ e^ is completed. If, at a specified time, the
system contains more free energy than it did at a prior time, we
say that a favorable balance between inputs and expenditures has

Injormation Concept in Ecology 149

been achieved. This enables the system to maintain or further
diminish its entropy. If the system possesses less free energy after
a passage of time, we say the balance is unfavorable and the system
is less able to forestall an entropy gain. These relationships can
be summarized by a simple transfer function which will be termed
cost. This variable represents the amount of energy which must
be expended to gain a unit of energy from the environment:

P7r~ < 1 (biomass gain)

[22] piv~^ = 1 (steady state)

P7r~^ > 1 (biomass loss)

where tt denotes total energy gained by the community and p
represents total energy lost.

Let us now consider some specific behavioral and organiza-
tional attributes of planktonic systems which exemplify goal-
adaptability, the goal being biomass maximization.

PROCEDURES

The plankton communities under consideration occupied the
York River, Virginia, during the summer of 1960. For ten con-
secutive weeks, from June 23 to August 25, in situ dark and light
bottle differential oxygen studies (27) were performed weekly to
assess energy flux through the community. The sampling station
was located about 300 yds. off the end of the Virginia Institute of
Marine Science pier where the approximate depth of mean low
water was thirty feet.

Hydrographic determinations included vertical profiles of chlor-
inity, temperature, dissolved oxygen, total dissolved phosphorus
and total nitrate. Temperature was recorded with a thermistor
unit. Chlorinity was titrated with silver nitrate. Dissolved oxygen
was measured by the unmodified Winkler method. Dissolved
organic and inorganic phosphorus were obtained as follows:
Fractionation into dissolved and adsorbed inorganic and dissolved
and particulate organic components was achieved by Millipore
(type HA) filtration. Inorganic fractions were assayed directly;
organic fractions were obtained by digesting samples for twelve
hours at 20 psi; the molybdate method (corrected for salt interfer-
ence) was employed to estimate the orthophosphate in both cases.

1 50 Information Storage and Neural Control

For energy flux determinations, paired dark and liglit bottles
containing water samples from two, six and ten feet were sus-
pended at various depths in the water column for twenty-four
hours (beginning 0730 EST), and then fixed for Winkler titration.
The suspension depths included all combinations of the collection
depths: (2,2), (2,6), (2,10); (6,2), (6,6), (6,10); (l0,2), (l0,6),
(10,10), where the left member of each pair designates collection
depth and the right member suspension depth. Additional dark
bottles for (l4,14) and (l8,18) were also included. Production
variables were detei mined from the initial and final dissolved
oxygen concentrations in the bottles:

TT = I — d (photosynthesis)

[23] p = i — d (respiration)

IT — ,0 = I — i (net production)

where /, d and / are, respectively, light bottle, dark bottle, and
initial oxygen concentrations. The differential oxygen concen-
trations were converted to gram calories (gcal) using suitable
conversion factors derived from the stoichiometry of the photo-
synthesis and respiration reactions.

Incident solar radiation at the water surface was measured in
gcal cm~" by an Eppley 10-junction pyrheliometer installed a few
hundred yards from the station, the output of the thermopile
being electronically integrated and automatically printed-out
every thirty minutes. Extinction coefficients for "white" light
were detei mined on samples obtained from the various depths
at the beginning and end of each experiment. The optical densities
were measured with a Klett-Summerson colorimeter, using a
neutral filter. From these values a mean was obtained for the
upper ten feet and was employed to estimate the light intensity
at any depth.

Total chlorophyll was assayed by Millipore-filtering samples
from different depths, grinding filters and residues with sand and
extracting the pigment in 90 per cent acetone (A/^COs-saturated),
then Seitz-filtering to remove sand and undissolved millipore frag-
ments. Absorbancies were determined with a red filter, and were
converted to chlorophyll concentrations by comparison with a
standard curve prepared from chlorophyll a.

Information Concept in Ecology

151

Biomass was estimated as ash-free dry weight of suspended
soHds. The inethod involved filtering water through tared Millipore
filters (type HA), desiccating" filters plus residues, weighing for
total solids, then ashing at 600 °C., rehydrating the ash, desic-
cating, and weighing again to obtain the ash weight.

Counts of phytoplankton units (chains, colonies or individual
cells) were made from Sedgwick-Rafter mounts of fresh samples
obtained from the various depths. All flagellates and diatoms were
counted; ciliates and other animals, when present, were excluded.

COMMUNITY ADAPTATIONS FOR MAXIMUM BIOMASS

The York River water column at the station sampled was
comparatively unstratified from surface to bottom during the
summer of 1960. This is illustrated by the graphs in Figure 3.

MAN DIUOLVIO OXTMII

MEM eitMLVto rHOtrORU*

MCAN NITRATE

Fig. 3. Mean vertical distribution of chlorinity, temperature, and the dissolved
substances oxygen, phosphorus and nitrate.

152

Information Storage and Neural Control

Chlorinity varied from 8.54 to 12.60 parts per thousand, with the
surface water generally a little less saline than that near the
bottom. The mean gradient for the ten experiments was only
0.75 parts per thousand. Temperature ranged from 24°C. in June
to over 27 °C. in mid-August, the surface waters being somewhat
warmer than the lower strata. The mean temperature gradient
was but 0.29 °C. These two variables, clilorinity and temperature,
are determinants of water density. In this case, they indicate a
very small gradient of increasing density with depth, thus assuring
a fair amount of vertical mixing in the water column. This con-
clusion is underscored by the vertical distribution patterns of
other dissolved substances for which data were obtained — dissolved

SURFACE

2 -

DEPTH 10 _
(ft)

14-

18-

BOTTOM

4000

6000

MEAN NUMBER OF CELLS
( per ml )

Fig. 4. Mean concentrations of living phytoplankters, in counting units ml,

at various depths.

Information Concept in Ecology

153

oxygen diminished only slightly with depth, and dissolved phos-
phorus and nitrate increased slightly (Fig. 3).

In sharp contrast to these physical relationships, the living
organisms of the phytoplankton were markedly stratified in the
upper layers (Fig. 4). To account for this we note that the domi-
nant organisms of the summer flora were motile flagellates closely
related to forms which are known to be positively phototactic.
Thus, swimming is the probable primary mechanism involved.
Other factors of possible influence include rapid cell division in
the lighted surface layers, and manufacture of low specific gravity
(lipid) storage products.

The mean daily vertical distribution of light in the ten experi-
ments is graphed in Figure 5, and shows typical exponential
extinction with virtually complete absence of light at the bottom.

SURFACE

DEPTH
(ft)

10 -

MEAN EXTINCTION

COEFFICIENT = 0.97

BOTTOM

I I I I

0 200 400 600

MEAN SUBMARINE ILLUMINATION
( gcal cm-2day-' )
Fig. 5. Mean vertical distribution of light in the ten experiments.

154

Information Storage and Neural Control

PHOTOSYNTHESIS
( gcol cm-^doy-' )

DEPTH
(ft)

RESPIRATION
( jcQl cm-*do>-' )

Fig. 6. Mean photosynthesis and mean respiration in the water column.

Mean photosynthesis and respiration are depicted in Figure 6.
As expected, production was highest near the surface and atten-
uated in exponential fashion with depth. Respiration was about
equal throughout the upper ten feet, but was only half as great
below this level.

MEAN ASH-FREE SOLIDS ("BIOMASS")
( mg cm-2 )

DEPTH ,0-
(ft)

MEAN TOTAL CHLOROPHYLL
( ug cm-2 J

Fig. 7. Mean ash-free solids and mean total chlorophyll concentration at various

depths.

Information Concept in Ecology 155

The concentration of ash-free solids (Fig. 7) was observed to
increase markedly with depth. This variable may be equated to
community biomass since even the non-living detrital material
which it includes represents a source of energy to certain hetero-
trophic components of the living plankton. The inverse relationship
between the vertical distribution of these ash-free solids and that
of living cells is a consequence of the detrital rain from the zone
of production at the top of the water column, and also of the
upwelling of bottom materials. Since even dead organic material
of this type has an oxygen demand, a significant (though un-
specifiable) fraction of what was represented in Figure 6 as com-
munity "respiration" is a product of non-biological oxidations
attending decomposition. Since such oxidations cost the com-
munity biomass energy, it is proper that they be included in
determinations of energy loss.

As in the case of ash-free seston, the vertical distribution of
total chlorophyll was different from that which would be antici-
pated on the basis of the cell-count data (Fig. 7). Chlorophyll
concentration increased gradually with depth. The explanation
is that large quantities of chlorophyll- and its degradation products
(many of which would be included in this assay) are associated
with non-living detritus (28, 29) and sediments (30, 31, 32). The
two curves of Figure 7 strengthen the conclusion that we are
dealing with a fairly well-mixed water mass since a certain amount
of upwelling is indicated.

Let us now consider some of the photosynthetic characteristics
of the plankton community at various depths.

Recall from the description of procedures that water samples
for the measurement of photosynthesis were collected at depths
of 2, 6 and 10 ft. and were resuspended so that data for all com-
binations of collection and suspension depths could be obtained.
The graphs in Figure 6 are for results from the particular com-
binations (2,2), (6,6) and (lO,10). In Figure 8, ail of the combina-
tions are graphed in 3-space with coordinates (collection depth, sus-
pension depth, mean photosynthesis). The surface depicted is
concave upward, slopes downward toward the viewer, and curves
markedly upward on the left. Consider, first, photosynthesis as
a function of suspension depth, by looking at the surface from back

156 Information Storage and Neural Control

to front. The downward sloping represents the attenuation of
photosynthesis with increased depth of suspension due to decreased
illumination. The curve of Figure 6 is the locus obtained on this
surface by connecting the points (2,2,4.61), (6,6,1.46), and (lO,10,
0.85). Now view the surface from left to right. This gives photosyn-
thesis as a function of collection depth. Regardless of the depth of
suspension, the populations collected at 2 ft. always photosyn-
thesized more than those obtained from 6 and 10 ft.; the latter
samples appear to give very similar results. These relationships
indicate that the organisms taken from deeper layers of the water
column have less capacity for photosynthesis than those which
normally occupy the surface waters. This may be a reflection of
the fact that the deeper plankters are senescent and sinking;
microscopic examination usually revealed the surface organisms
to be far more active in swimming than their counterparts from
below.

Consider now the thermodynamic efficiency of photosynthesis
as reflected by the ratio of mean photosynthesis per mean illumi-
nance at each depth. These data are presented in Figure 9. The
surface generated is concave "upward, slants upward approaching
the viewer and toward the left, and rises sharply on the right in
front. Studying from back to front first, we observe that photo-
synthetic efficiency increases with depth of suspension, hence with
diminished light intensity. This result is in accord with the photo-
synthesis literature (33). Now studying the surface from left to
right, we observe that plankters living nearer the surface are
generally more efficient in light utilization when compared at the
same suspension depths with those from deeper layers, except that
organisms collected from 10 ft. appear to be almost as efficient
as those from 2 ft. when both are suspended at the deeper level.
In general, then, the relationships of Figure 9 are consistent with
those of Figure 8 in denoting greater productive capacity of surface
populations compared to those from farther down. The observation
that efficiency increases as light decreases can be interpreted to
be adaptively significant in respect to the goal of biomass maxi-
mization. The extent of this dark-adaptability under natural
conditions is emphasized by comparing efficiencies of the popu-
lations at the depths they naturally occupy. Thus the points (2,2,

Information Concept in Ecology

157

MEAN
PHOTOSYNTHESIS

(gcal cm"2day"')

5-1

(10,2,257)

(2,10,1.06)

COLLECTION DEPTH (ft)

Fig. 8. Photosynthesis as a function of sample collection and_suspension depths
(means for ten experiments).

18.5), (6,6,19.5), and (10,10,44.7) in Figure 9 indicate a 2.4-fold
efficiency increase at 10 ft. compared to 2 ft.

Two phenomena may be involved in this increased efficiency
of the deeper populations: 1) the purely numerical "swamping"
effect of more photons in the upper layers of the water column
than can possibly be absorbed by the plant pigments (34), and
2) actual increase in the thermodynamic efficiency of chlorophyll
with depth. The latter is illustrated in Figure 10 in which mean
photosynthesis per unit mean illumination per unit mean initial
concentration of total chlorophyll in the ten experiments is graphed.

158

Information Storage and Neural Control

MEAN

PHOTOSYNTHESIS

PER UNIT

ILLUMINATION

(gcal kcal'i]

10,53.1)

•50

-28

(2,2,18.5)

--I0
6 10

COLLECTION DEPTH (ft)
Fig, 9. Photosynthesis per unit illumination as a function of sample collection
and suspension deptlis (means for ten experiments).

This graph represents chlorophyll efficiency. Although the surface
shown is fairly similar to that of Figure 9, the greatest similarities
are on the left (2 ft. collection depth) and in the rear (2 ft. sus-
pension depth). The forward part of the Figure 10 surface (10 ft.
suspension depth) and the rigiit-hand side (10 ft. collection depth),
however, are considerably more elevated than those of Figure 9.
These relationships appear at a glance by noting that much more
of the underside of the Figure 10 surface is visible than that of
Figure 9.

Information Concept in Ecology

159

MEAN

PHOTOSYNTHESIS

PER UNIT

ILLUMINATION 8
CHLOROPHYLL
( gcal kcal"' ug"')

(2,10,10.9)

f-IO

-5

(2,2,3.9) (10,10,9.2)

( 10,6,6.1)

10,2,2.6)

COLLECTION DEPTH (ft)

Fig. 10. Photosyntliesis per unit illumination and chlorophyll as a function of
sample collection and suspension depths (means for ten experiments).

To test the significance of these relationships, the vertical co-
ordinate of the Figure 10 points was divided into that of the
Figure 9 points to obtain the ixiean chlorophyll concentrations
required to give unit efficiency: 4.86, 4.41 and 4.05 /xg", respec-
tively, for samples collected at 2, 6 and 10 ft. These values indicate
less chlorophyll to be required as sample depth is increased. Only
the first two means were significantly difTerent, however, estab-
lishing that the chlorophyll at 2 ft. was less efficient that that at
6 ft. Because of the high variance associated with the 10 ft. samples,

160 Information Storage and Neural Control

the 2 — 10 ft. and 6 — 10 ft. means could not be distinguished. There-
fore, the tendency toward increased chlorophyll efficiency with
depth of collection cannot be formally accepted as a generalization.
We may accept it on intuitive grounds, however, noting the high
likelihood for a Type II (35) biometrical error due to small sample
size, i.e., an error such that the null hypothesis is accepted when
in fact it is false.

Although physiological mechanisms {e.g., photoinhibition) are
doubtlessly involved in the observed increase of chlorophyll
efficiency with depth, ecological factors are also implicated. One
of the striking features about the vertical organization of summer
estuarine plankton communities is variability in species com-
position and in cell concentrations. The numerical stratification
of the York River phytoplankton has already been described
(Fig. 4). Table I is provided to illustrate the nature of species
changes with depth. It is a list of phytoplankton species and their
concentrations obtained from the third experiment (July 6) of the
series under consideration. There is nothing especially atypical
about this particular list; it is fairly representative.

The table shows that two flagellates (in decreasing order of
importance: Massartia, Chilojrtorms) were dominant at the surface.
Two feet below, three species dominated in a diff'erent order of
abundance {Alassartio, Gjrodimum, Chilomonas). These forms are
all highly motile; Massartia and Gyrodinium are dinoflagellates,
Chilomonas is a yellow-green flagellate. Both of these groups typically
photosynthesize at maximal rates under conditions of high light
intensity (36). At 6 ft. the surface forms were no longer of sig-
nificance (dominants being Eutreptia, Gyrodinium), and at 10 ft.
they were entirely absent (dominants: Eutrepfia, Pyramimonas,
Leplocylindricus) . Eutreptia is a euglenoid, Pyramimonas a flagellated
green alga, and Leplocylindricus an immotile diatom. The latter
two groups, in estuaries, are generally adapted to photosynthesize
maximally under conditions of low or medium illumination (36).

It would seem from these few general observations that main-
tenance of a suitable vertical diversity structure might constitute
a significant segment of community strategy in implementing the
goal of biomass maximization. That a very definite vertical
diversity pattern is maintained in summer is illustrated in Figure 1 1

Information Concept in Ecology

161

5 ^

[2 w

z >■

OP
Z H

§1

§0

< s

CN o CN o o
I o I r-- m r~- r-

■--,

m cn 00 CN
o m ■* CO
\o -^ en ■r-

r^ \o c?\ CN
I ^ in o o 1

■^ un (N CM

00 00 so -^ M c^
O CM t-^ O CO CO
in o CM -^ M CM

t~- I 1 -- I I I

I I r~- I r-- I I o I 1

rv] CM \o <^] \o

ro CO ^-c •^ ^-'
CM CM T-H ,-.

I I i

00 00 00 00 CN) CM CM V3 O C^] C^I C^l C^l CV)

r^cM'*-<^rOcOtOT-H.^T-.'rH.^.^T-< I

OOCncOcOCMCMCMt-ht-i

I I I I I I 1 T-H ^

^ —

br. t«

g _. o u

i-, ^ -o -

c tj:

U TO ft

w §:!

• — o
en -o g

- S3-? i«

«j O 1^ i-
— » -— u 03

J2 « ^ O

^ S

■a, C"':= 2 5

OCi

5 5^ c;

O h2 a.

^ W

-S a.

■~

'■p. o

-G -2

a —

■*^

-a

dT3

M U

■ S S

~ c

"> 5J

-a

-0

^:a

162

Information Storage and Neural Control
SURFACE

DEPTH 10-
(ft)

BOTTOM

5 10

MEAN COWMUNITY DIVERSITY
I bits X lO-yml )

Fig. 11. Mean community diversity, D = — ^ Nj log P(Si), at various depths.

in which mean community diversity for the ten experiments, as
defined in Equation [4], is plotted against depth. The figure shows
maximum diversity at the 2 ft. leveL

From the foregoing data (Figs. 4 and 11) it is concluded that
the living organisins of the York River summer plankton are
vertically stratified in excess of the extent attributable to cor-
responding heterogeneity of the physical environment (Fig. 3).
Maintenance of such a concentration gradient against the mixing
forces of the environment inust therefore be endergonic — the
organisms must expend biomass energy to reduce the entropy of
their distribution in space.

Injormatwn Concept in Ecology

163

In oceanography, there is a prominent and widely accepted
theory that energy accrual by a planktonic system cannot exceed
respiratory losses in a uniformly mixed water column (37). Vertical
stratification of the organisms is therefore necessary for positive
energy balance. This theory has never been rigorously developed,
however, and as a matter of fact has recently (38) been invalidated
by proof for a countertheorem: Vertically homogeneous plankton
communities are energetically feasible. Stratification is therefore
not essential to positive energy balance. This conclusion makes the
foregoing York River observations difficult to understand. Why
should a community expend energy to achieve and maintain pro-
nounced vertical stratification if it is not thermodynamically
essential for it to do so? Consider the following.

The important variable relating to community energy balance is
the cost as defined in Equation [22]. In Figure 12 mean cost data
are graphed as a function of collection and suspension depths;

(6,10,2.671

(10,10,2.99),

SUSPENSION DEPTH (ft)

Fig. 12. Cost, p TT-i as a function of collection and suspension deptlis (means

for ten experiments).

164 Information Storage and Neural Control

this particular grapii is rotated 90° clockwise around the vertical
axis (compared to previous figures of this type) to improve the
perspective in which the surface is viewed. Studying the surface
from back to front first, we see that cost increases in a generally
hyperbolic or logarithmic fashion with depth of collection; the
surface is saddle-shaped, being convex upward from back to front.
The fact that it rises toward the viewer supports the previous
conclusion that the deeper populations are less viable than those
nearer the surface — their cost of operation is higher. The ribbon-
shaped segment in the figure denotes the loci on the surface and
on the horizontal plane where the ratio p7r~^ is unity, i.e., where
an exact balance between energy inputs and expenditures is
achieved. For any specified collection depth, this ribbon indicates
the depth at which the sample must be suspended to achieve a
steady state between inputs and losses. This depth is seen to become
shallower as the collection depth increases — another indication of the
intrinsically higher vitality of populations found nearer the surface.

Viewing the surface of Figuie 12 from right to left, cost is shown
to increase as suspension depth is increased. In this direction the
surface is concave upward. Thus, despite the measure of dark-
adaptability demonstrated earlier, the price to a population of
inhabiting deeper layers in the water mass is unequivocally in-
creased cost of operation. This datum appears to provide an
economically logical reason for stratification. A well-known doc-
trine from marginal analysis in economics (39) states that the
scale of an activity should be expanded so long as marginal
profitability (increase in net utility gain) is a positive value, and
carried to a point where marginal yield is zero. This corresponds
to the procedure in calculus of maximizing a function by setting
its first derivative to vanish. Applied to the plankton, this law
demands, in view of observed depth-cost relationships, that the
community should invest biomass energy to concentrate its com-
ponent organisms near the surface up to the point where additional
return becomes zero. It would appear that the stratification
behavioi of the York River plankton is consistent with sound
economic policy.

The converse of the marginal profitability law would be: If
marginal gains are negative, the scale of an activity should be

Information Concept in Ecology 165

reduced at least until a point of no further loss (zero return) is
reached. Let us examine the behavior of the York plankton in
respect to this proposition. Referring to Figure 6, mean photo-
synthesis is observed to exceed mean respiration in the upper
water column {pir'~^ < 1) but not in the lower (ptt"^' > 1)- This
relationship is so typical in aquatic communities that the depth
at which the photosynthesis and respiration curves cross (ptt"^ = 1)
is a standard variable — the compensation depth. The mean depth
of compensation at the York sampling station during the summer
of 1960 was 6.5 ft.; this level is denoted by broken lines in Figures
3-7 and in Figure 1 1 . When phytoplankters drift beneath the
instantaneous compensation depth they experience, on the average,
a shift fi'om positive to negative energy balance. If a net positive
balance is to be achieved for the whole water columii it is necessary
that the community reduce energy losses in the lower part of the
column. This implies, by the mathematical nature of the cost
variable, increasing the rate of photosynthesis and /or depressing
the rate of respiration. Community behavior in accordance with
tlie former imperative has already been described as dark-adap-
tability. We consider now the attenuation of respiration.

The data which have been presented indicate that althougli
photosynthetic capacity of the plankters was irreversibly (in 24
hours) less at the 6 and 10 ft. levels than at 2 ft. (Fig. 9), vigorous
respiration equivalent to that of surface populations persisted
down to 10 ft. (Fig. 6). Below 10 ft., however, oxygen uptake was
sharply reduced. The extent of actual metabolic failure must be
even gi^eater than indicated by Figure 6 since the concentration
of oxidizable detritus increased with depth (Fig. 7) producing a
continually increasing oxygen demand (reflected in the oxygen
curve of Fig. 3). This underscores the conclusion that metabolism
is sharply curtailed soon after the organisms drift beneath the
compensation depth. This phenomenon constitutes a pei'fect
response on the part of tlie community to the converse marginal
profitability principle, and is an example of ''beneficial death" (24)
at the community level. Beneficial death is u.sually thought of in
connection with individuals {e.g., dead cells forming" the matrix
of a functional tissue, as in plant xylem or some insect wings) or
populations {e.g., annual plants, some social insect castes, genetic

166 Information Storage and Neural Control

lethals). That the comparatively loosely organized coinmunity
may also derive profit through death of its constituent organisms
at an appropriate time is an interesting speculation.

First of all, planktonic systems such as these have, of course,
evolved. One of the important taxonomic characteristics of the
algal phyla is the nature of food storage products. It would seem
that with such a capability already well developed generally in
these groups there could have evolved, in the time available,
species able to maintain robust metabolic activity right down to
the bottom if it were consistent with community design. This
would be especially adaptive in water masses where expectation
for return to the trophogenic zone (above the compensation depth)
through vertical turbulence would be good. Indeed, evolutionary
theory asserts that such foims would enjoy a selective advantage
over more labile ones. In phytoplankton, the smaller motile
species typically possess rapid dynamics and short generation
times, but lack the capacity for sustained yields characteristic of
larger forms with more conservative dynamics (15). Clearly, from
the standpoint of the York system in summer with its slight vertical
density gradient, the latter type of organism would not be nearly
so satisfactory a component as the former. Their production rates
per unit of biomass would be slower in the upper water column,
and their collective respiration higher in the depths; the result
might be a net energy loss to the conmiunity. Under winter con-
ditions when hydrography is such that vertical turbulence is
extreme and the water column thoroughly mixed, larger species
with longer generation times, lower light optima, and greater
capabilities for food storage niight be more serviceable con-
stituents. Perhaps, therefore, the seasonal replacement of summer
flagellate floras by diatomaceous communities in winter and spring
may be taken to reflect community adaptability in response to a
changing environment. In view of this, it does not seem unreason-
able that particular species may be selected for occupancy in a com-
munity under a specific environmental regime, not only for their
Darwinian competence in competition, but as well for their compati-
bility as functional components of a goal-adapted "machine" (40).

This possibility would seem to add another dimension to the
classical concept of ecological community because of its implicit

Information Concept in Ecology 167

demand that the success or failure of species be related to and
interpreted in a broader sociological context. Acceptance of such
a context carries with it the important advantage of making some
of the elegant formalisms (9, 23) developed in connection with the
study of situations of conflict available for ecological analysis. The
theory of games and decisions is, however, notoriously teleological
in basis: litigants come to odds through mutual impairment of
purposive behavior. This objection can be ameliorated to a very
large extent by regarding community goal-adapted behavior in
a teleonomic, not teleological, sense; i.e., the community is
"programmed" for goal achievement though possessing no "con-
scious" knowledge of the goal. This kind of thinking is widely
accepted in connection with the problem of DNA coding, and
it has been formalized in Bellman's (40) concept of information
pattern. In such a framework, the mechanism of natural selection
may still be construed to operate at an infraspecies level; for
example, by acknowledging" that the information pattern of a
species (a program containing the accumulated history of its past
and rules for decision making) can enable the latter to make, in a
completely mechanistic manner, a choice between alternative
strategies such as those embodied in a recent theorem (41) due
to Rashevsky: If two individuals work on the production of some
object of satisfaction (utility) and if their cooperative efforts result
in an increased overall productivity, then each individual will
have less of the object of satisfaction if each adopts a strategy of
maximizing his own satisfaction (egoism, competition) than if each
tries to maximize the sum of the satisfactions of both individuals
(altruisin, cooperation).

The importance of epistemological bearing in determining the
character of questions which one may ask of biosystems and,
consequently, that of the answers elicited can be illustrated as
follows. Consider a proposition of the form, "The organism
(species, community) is adapted to . . . ." This is completely
acceptable biological rhetoric. Constructed in the passive voice,
the statement carries the implication that it is the fortuitous
environment which does the selecting. If we go to the active form,
"The organism adapts to . . .," we provide the biological sub-
ject with a degree of initiative in the process. This is still quite

1 68 Information Storage and Neural Control

acceptable. If now we change the verb akogether and posit,
"The organism adopts a strategy for . . .," we pass for many
readers rather too abruptly into the realm of purpose. Thus, it
might be more suitable to say instead, "The organism is pro-
grammed for a strategy of . . .." The important point to im-
press here is that all of these statements mean essentially the
same thing mechanistically, though epistemologically they are
poles apart. Consequently, they give rise to very different ways of
asking questions, therefore to divergent investigational approaches,
and finally to quite different classes of answers.

To illustrate, if in the present instance the hrst-mentioned
point of view is adopted, then only the empirical sections of this
paper would have relevance, and its content might be summarized
by saying: The York River plankton community appears to be
eminently adapted to its environment as indicated by 1) stratifica-
tion of organisms near the surface where there is more light,
2) increase of chlorophyll efficiency with depth due to both
physiological and species compositional reasons, and 3) sharp
curtailment of respiration in the lower part of the water column
as the organisms die and sink, making possible a positive balance
between energy gains and losses in the community. This is a
descriptive approach, and it yields purely descriptive answers with
limited power to provide real insight into the marvel of organiza-
tion and behavior which is the community.

Contrast this with the summary which might result from
acceptance of the last point of view: Based on the above-mentioned
observations, the York River community appears to be pro-
grammed for a strategy of maximizing its biomass, therefore its
energy content, therefore its ability to purchase utility and increase
its information reserves, therefore its diversity or richness of form,
and therefore its stability in a variable environment. In the
process, the community, inchoate a biological system as it is,
meets some fundamental thermodynamic and economic impera-
tives, as well as the dictum of Shannon's Theorem 10.

The new level of abstraction so attained may or may not qualify
the community as a Wienerian (42) machina ratiocinatrix, but if
there is a distinction, it would seem to lie largely in the realm of
logic and semantics, not of biology.

Information Concept in Ecology 169

REFERENCES

1. Lindeman, R. L.: The trophic-dynamic aspect of ecology. Ecology,

2J.-399-418, 1942.

2. MacFadyen, A.: The meaning of productivity in biological systems.

J. Anim. EcoL, 77.-75-80, 1948.

3. Schrodinger, E.: What is Life? Cambridge, England, University

Press, 1945.

4. Brillouin, L.: Life, thermodynamics, and cybernetics. Sci. Amer., 37:

554-568, 1949.

5. Branson, H. R. : A definition of information from the thermodynamics

of irreversible processes and its application to chemical communi-
cation in biological systems, in Quastler, H. (ed.), Information
Theory in Biology. Urbana, Univ. Illinois Press, 1953, p. 25.

6. Linschitz, H. : The information content of a bacterial cell, in Quastler,

H (ed.), Information Theory in Biology. Urbana, Univ. Illinois Press,
1953, p. 251.

7. Patten, B. C: An introduction to the cybernetics of the ecosystem:

the trophic-dynamic aspect. Ecology, 40:22\-23\, 1959.

8. Patten, B. C: Negentropy flow in communities of plankton. Limnol.

Oceanogr., 6.-26-30, 1961.

9. Thrall, R. M., Coombs, C. H. and Davis, R. L. (eds.): Decision

Processes. New York, Wiley, 1954.

10. Shannon, C. E.: A mathematical theory of communication. Bell

Syst. Tech. J., 27.-379-423, 623-656, 1948.

11. Ashby, W. R.: An Introduction to Cybernetics. New York, Wiley, 1956.

12. Ashby, W. R.: Design for a Brain, ed. 2. New York, Wiley, 1960.

13. Margalef, D. R.: Informacion y diversidad especifica en las com-

munidades de organismos. Invest. Pesquera, 3.-99-106, 1956.

14. Margalef, D. R.: La teoria de la informacion en ecologia. Mem. R.

Acad. Cien. Artes, Barcelona, J2.-373-449, 1957.

15. Margalef, D. R.: Temporal succession and spatial heterogeneity in

phytoplankton, in Buzzati-Traverso, A. A. (ed.), Perspectives in
Marine Biology. Berkeley, LIniv. California Press, 1958, p. 323.

16. MacArthur, R.: Fluctuations of animal populations, and a measure

of community stability. Ecology, J(5." 533-536, 1955.

17. Hutchinson, G. E.: Concluding remarks. Cold Spring Harbor Symp.

Qimnt. BioL, 22:415-427, 1957.

18. Wiener, N.: The Human Use of Human Beings. New York, Doubleday,

1950.

19. Brillouin, L.: Science and Information Theory. New York, Academic

Press, 1956.

170 Information Storage and Neural Control

20. Rothstein, J.: Communication, Organization, and Science. Indian Hills,

Colorado, Falcon's Wing Press, 1958.

21. Jaynes, E. T.: Information theory and statistical mechanics. P/owVa/.

Rev., 7O6.-620-630; 7C5.-171-190, 1957.

22. Tribus, M.: Information theory as the basis for thermostatics and

thermodynamics. Gen. Syst., 6.-127-138, 1961.

23. Von Neumann, J. and Morgenstern, O.: Theory of Games and Economic

Behavior. Princeton, N. J., Princeton Univ. Press, 1947.

24. Allee, W. C, Emerson, A. E., Park, O., Park, T. and Schmidt, K. P.:

Principles of Animal Ecology. Philadelphia, Saunders, 1949.

25. Szent-Gyorgi, A.: Submolecular biology. Radiation Res., 1960, supp.

2, p. 4.

26. Arnon, D. I.: The role of light in photosynthesis. Sci. Amer., 203:

105-118, 1960.

27. Gaarder, T. and Gran, H. H.: Investigation on the production of

plankton in the Oslo Fjord. Rapp. et Proc. — Verb., Cons. Internal.
Explor. Mer., ^i.-l-48, 1927.

28. Gilli)richt, M.: Untersuchungen zur Productions Biologic des Plank-

tons in der Kieler Bucht. I. h'ieler Meeresf., <§.• 173-1 91, 1951.

29. Krey, J.: Untersuchungen zum Sestongehalt des Meerwassere 1:

Der Sestongehalt in der westlichen Ostsee und unter Helgoland.
Ber. Deutsch Komm. Meeresf., 72:431-456, 1952.

30. Vallentyne, J. R.: Sedimentary chlorophyll determination as a

paleobotanical method. Canadian J. Bot., Ji.-304-313, 1955.

31. Vallentyne, J. R., and Bidwell, R. G. S.: The relation between free

sugars and sedimentary chlorophyll in lake muds. Ecology, 37:
495-500, 1956.

32. Vallentyne, J. R., and Craston, D. F.: Sedimentary chlorophyll

degradation products in surface muds from Connecticut lakes.
Canadian J. Bot., 35:35-42, 1957.

33. Rabinowitch, E. I.: Photosynthesis and Related Processes, New York,

Interscience, 1951, vol. 2, pt. 1, p. 603.

34. Odum, H. T., McConnell, W., and Abbott, VV.: The chlorophyll

"A" of communities. Publ. Inst. Mar. Sci., Univ. Texas, 5.-65-96, 1958.

35. Snedecor, G. W.: Statistical Methods Applied to Experiments in Agri-

culture and Biology, ed. 5, Ames, Iowa, Iowa State College Press,
1956.

36. Ryther, J. H.: Photosynthesis in the ocean as a function of light

intensity. Limnol. Oceanogr., 7.-61-70, 1956.

Information Concept in Ecology 171

37. Sverdrup, H. U.: On conditions for the vernal blooming of phyto-

plankton. J. dii Conseil, 7<§;287-295, 1953.

38. Patten, B. C: Energy-depth relationships in plankton. In manu-

script.

39. Baumol, VV. J.: Economic Theory and Operations Analysis. Englewood

Chffs, N. J., Prentice-Hall, 1961.

40. Bellman, R.: Adaptive Control Processes: A Guided Tour. Princeton, N. J.,

Princeton University Press, 1961.

41. Rashevsky, N.: A contribution to the theory of egoistic and altruistic

interactions. Bull. Math. Biophys., 2J.-115-134, 1961.

42. Wiener, N.: Cybernetics. New York, Wiley, 1948.

DISCUSSION OF CHAPTER VII

Walter Abbott (Houston, Texas) : You are undoubtedly aware
of the criticisms of the light-dark bottle technique, mainly because
of the reduction in turbulence. Does this mean that your data
represent minimum estimates?

Bernard C. Patten (Gloucester Point, Virginia): I do not
know what it means, actually. There are four or five classes of
criticism of the light and dark bottle method. Some of them work
in opposition, i.e., an error in one direction may be cancelled or
partially mollified by another error in the reverse direction. I
think the technique is marginal, at best, for obtaining absolute
measures of energy flux, but quite adequate for relative com-
parisons of the activities of different populations, which is how we
used it. If you perform enough experiments and observe that a
fairly consistent pattern emerges, you can begin to feel confident
of the reality of the pattern even though the data may represent
minimal estimates.

Heather D. Mayor (Houston, Texas): Would you have to
allow for extra energy gain to your system, brought about by the
process of measurement? Is there, for example, additional "noise"
added to the respiration term because of the measurements?

Patten: I am not quite certain what you mean, but we do make
corrections of the type you suggest. We make a correction for the
fact that the photosynthetic quotient is generally greater than
unity in marine phytoplankton, but I do not believe that this is
the kind of thing to which you are referring.

1 72 Injormatwn Storage and Neural Control

Mayor: Using a quantum analogy, I know you would be
introducing an additional perturbation by measuring your param-
eters. I was wondering" whether you have considered this problem,
or whether you are working at a level where this type correction
is not necessary.

Patten: If there is an analogous problem here, I am not aware
of it. I believe we may be thinking as well as working at different
levels.

CHAPTER
VIII

EXCHANGE OF INFORMATION ABOUT
PATTERNS OF HUMAN BEHAVIOR

Gregory Bateson, M.A,

.T THE outset I wish to make two acknowledgments. First,
I would like to credit Attneave's work (1) in which he points
out the synonymy between what in information theory is called
"redundancy" and what in popular parlance is called "pattern."
You will see, as I develop what I have to say, that this synonymy
is basic. Second, I want to acknowledge a less definable debt to
conversations with Alex Bavelas about his experiments involving
varieties of contingency in learning contexts. I had hoped that
the outcome of these conversations would be a paper in which
his name would be included as joint author. Since our diverse
professional commitments have prevented our getting together on
this, I must take responsibility for the thoughts which his work
has stimulated in me. A major part of this paper will be devoted
to defining that order of information which I regard as "infor-
mation about patterns of human behavior." This involves a
restructuring of learning theory.

Let us assume that all receipt of information is "learning." This
will bring within a single theoretical spectrum the whole range of
phenomena, beginning with the receipt of a pip by a receiving
machine at the end of a wire, up to and including such complex
phenomena as the development of neurosis or psychosis under
environmental stress. Notice first of all that the receipt of a bit,
a yes or no answer to a question, is not usually called "learning"
if the receiver already knows to what question the bit is an answer.
Psychologists who perform what are usually called learning experi-

173

1 74 Information Storage and Neural Control

ments generally ignore phenomena of this order. Their experiments
are concentrated upon a change in the way the receiving entity
responds to what is supposed to be the same bit when this bit is
presented on successive occasions.

"Learning," as the word is used by psychologists, denotes the
receipt of a meta-bit, i.e., a piece of information which will change
the subject's response to some bit. Over and over the psychological
experimenter presents the stimulus, a buzzer, followed by meat
powder. He observes that, after a number of trials, the animal
which formerly did not salivate when it heard the buzzer now
does salivate. This change is called "learning." But when this
process has approached completion, if the psychologist again
presents the buzzer and the animal salivates, this receipt of infor-
mation— the receipt of the sound of the buzzer as a yes or no
answer to a question which the animal can now identify — is not
usually regarded as learning. By including this simplest phenom-
enon, i.e., the receipt of the single bit, within the spectrum of
learning, the question as to whether a computer is or is not learning
when it receives appropriate input is answered out of hand. This
is learning of the simplest order.

Second order learning arises when the subject changes his ability
to receive the yes or no answer to a question. This is, in fact, the
phenomenon which psychologists have studied maximally in learn-
ing experiments; the dog learns that the buzzer means future
meat powder.

But beyond this, there is obviously a third order of learning
called the acquisition of "test wisdom," or "set learning." Here
the subject learns that he is to be on the lookout for sequences
of a certain sort in his universe, which include both external
events and his own behaviors. For example, he learns to behave
instrumentally in order to solve the problems presented by stimuli.
If the laboratory is Pavlovian, he learns to expect the stimuli to
be direct predictions of future reinforcements which will come
regardless of his action. I shall speak of this as third order learning,
referring to those changes whereby the subject who encounters
and solves repeated problems of a certain sort comes to expect
his universe to be structured in ways related to the formal struc-
turing of these previous problems.

Patterns of Human Behavior 175

To this formal structuring of contexts, we can apply the language
which invokes contingency. We shall then say, for example, that
the animal which has undergone recurrent classical Pavlovian
experimentation will expect his universe to be so structured that
reinforcements are contingent only upon stimuli, not upon his re-
sponses. If his universe is totally structured in this way, all he can
do is to prepare for the coming reinforcement, e.g., by autonomic
measures such as salivation. He can predict but he cannot control.

Note that a subject, acting in terms of this philosophy or in
terms of any philosophy of this order, will, in general, have such
experience of his universe as will validate his philosophy. If he
does not believe it is worthwhile to behave instrumentally, he
will never engage in behavior which would disprove or test the
philosophy. And, conversely, if he has had past experience only
of instrumental contexts, he will have learned to behave instru-
mentally and will encounter, as it seems to him, a universe in
which instrumental behavior is appropriate. Attempting to make
a reinforcement come, he will try out various courses of action;
and when the reinforcement does come, he will believe that the
action which immediately preceded it was an effective instru-
mental action. His experience of his universe will validate his
theory of instrumental magic, even though the causal contin-
gencies assumed by this magic may be mythological or delusory.

Let me now extend what I have said about individual learning
to what would superficially seem to be much more complex
phenomena— those of interpersonal exchange. To do this, we
have only to personify the experimenter as well as the learning
subject and to see the learning experiment as a small segment
of an interchange between two persons: A, the experimenter,
provides the stimulus; B, the subject, responds to the stimulus;
and A follows B's response with a reinforcement.

Notice that these categories {stimulus, response, and reiti for cement)
which we are putting upon the behaviors cannot be empty. If
the experimenter does not provide a reinforcement, this in itself
is a reinforcement; and, if the subject does not respond to the
stimulus, this failure to respond represents the subject's response
to the stimulus. Notice also that if there were no stimulus, this in
itself would be the stimulus to which the subject responded. In

1 76 Information Storage and Neural Control

the world of communication, a message does not have to be an
event or an object in order to be a message. As my friend Ray
Birdwhistell says, "Nothing never iiappens."

If we look at an on-going interchange between persons who
behave alternatively, they can never "not behave." The inter-
change has been going: . . . A, B, A, B, A, B, . . . From this
we cut out, for our analysis, any triad: a sequence A, B, A, or,
if you like, a sequence B, A, B. Within any such triad, we can now
recognize that the third item is necessarily a reinforcement be-
cause, in this triad, if the third item had been something other
than what it was, or if it had been something, for example, which
made the second item inappropriate, it would obviously have
been a negative reinforcement. So if the third item is appropriate,
it is, in fact, a positive reinforcement of the second item. By the
same token, the second item can always be regarded as a "response"
since it follows a first item and is reinforced by a third. Cor-
respondingly, the first item is necessarily a stimulus since it precedes
the second, which is reinforced by the third. These are purely
formal relations between items and must necessarily obtain in any
triad of an interchange between learning entities.

It follows that, in a long interchange of this kind, any behavior
of B is necessarily simultaneously a stimulus, a response, and a
reinforcement, according to how we slide our identification of the
triad up and down the series. The same is true for any behavior
of A. Such a scheme has the advantage of presenting to the scien-
tist all the possibilities for punctuating a sequence of interchange
at the level of complexity of the triad. It is, however, arbitrary in
that it excludes the simpler (dyadic) units of interchange and also
the more complex (polyadic) units.

The arbitrary selection of the trigram, however, does raise a
number of interesting problems. Note that each item in any tri-
gram is also a member of two other trigrams. Clonsider such a
sequence as the following:

A . . . 23 25 27 29 . . .

B ... 22 24 26 28 30 . . .

In the sequence, the odd numbers represent items of A's behavior
while the even numbers represent those of B. The sequence is

Patterns of Human Behavior \11

deliberately imagined to be far from the beginning and from the
end of the total interchange. It will be observed that B's item 26
is a response in the trigram 25-26-27, but it is also a reinforce-
ment in the trigram 24-25-26 and a stimulus in the trigram
26-27-28. The formal truth, however, may not represent the
natural history of the relationship as it is perceived by the par-
ticipants. They are busy putting their labels, imposing their
Gestalten, on the items and on the trigrams. It is perfectly possible,
for example, for A to punctuate this interchange in such a way
that he will see only the trigrams 23-24-25 and 27-28-29 and
ignore or brush off B's items 22 and 26, creating a picture in which
A always provides the stimuli and reinforcements while B provides
only the responses. If A succeeds in maintaining this system and
in making B see the relationship in the same way, we may say
that A is, in this particular sense, the dominant participant in
the relationship. On the other hand, B, by pulling his punches
on items 22 and 26, may succeed in forcing A to think that he
(A) has the initiative. It may then be difficult to decide who is
"dominant."

At this point it is not appropriate to go into all the possible
details of the punctuation of such sequences. However, a part of
this matter has been explored in earlier publications (2) in which
the formal resemblances and differences between dominance,
dependency, and spectatorship were discussed. It was pointed out
that these themes of relationship could be reduced to paradigms
of learning and that various types of "end-linkage"' could occur.
For example. A, in his relationsliip to B, could take the dominant
end of a dominance-submission relationship and the succoring end
of a succoring-dependence one. These patterns could also be
reversed, in which case A would combine dominance with de-
pendency. Very basic differences between cultures, e.g., between
the cultures of England and America, might be expressed as
contrasts of end-linkage in parent-child relationships.

But, if it is true of human natural history that people punctuate
their interchanges into sequences which are, in fact, contexts of
learning, it follows that in interpersonal interchange we must also
face at least the three levels of learning which have already been
defined in the learning experiments. That is, each person is

178 Information Storage and Neural Control

receiving bits of information, and these bits are already falling
into place as yes or no answers to questions of which the person
already has understanding. But the second order learning must
also be occurring, i.e., he must be changing his identification and
understanding of the questions to which the bits are answers; and
third order learning must also be going on, namely, he must be
learning the characteristic patterns of contingency in this re-
lationship.

The reality of these three levels of learning, especially the
reality of the third level and perhaps of higher levels, can only
be demonstrated convincingly from phenomena of pathology.
Wlien all is going smoothly, it is not possible to get a clear picture
of what orders of learning are operating. It is when certain orders
of learning are disturbed that it becomes possible to analyze and
recognize these orders.

For a long time psychologists have been performing various
experiments which amply demonstrate what I am trying to say.
Unfortunately, the conventional phrasings used in the psycho-
logical laboratories are not along the lines I am advocating here.
The experiments to which I refer are those called experiments in
"experimental neurosis." Traditionally, these are described with-
out invoking any theory of levels of learning. For example, we are
told that the dog starts to exhibit psychotic or other sympto-
matology when his "discrimination breaks down." Let me dissect
a typical experiment for you so that you may see that what happens
is not necessarily a matter of breakdown of discrimination but
can be seen as a matter of disruption of the learning process at
what I am calling the third level.

Classically, the animal is presented with an ellipse, which
means x, and with a circle, which means y. If the dog performs
X in response to the ellipse and y in response to the circle, it either
gets its reward or avoids its punishment. But, if the dog fails to
"discriminate" between these stimulus objects, it receives punish-
ment or fails to get a reward. Having taught the dog this dis-
crimination, the experimenter begins to fatten the ellipse and to
flatten the circle. The dog responds by exerting greater effort to
tell the difference between the symbols, and at first these eff"orts
will be successful. As a further stage is reached and the discrimina-

Patterns of Human Behavior 1 79

tion becomes more difficult, the psychologist makes a pencil mark
on the back of the ellipse in order to distinguish it from the "circle."
He also uses a coin or some other randomizing device to decide
which of the stimulus objects he is going to administer next. He
cannot afford to administer them in any patterned order which
the dog might learn. Finally, these two objects become indis-
tinguishable; i.e., from the point of view of the dog they are one
object or, rather, they would be one object if the dog had not
been told previously, "This is a context for discrimination." This
message was underlined during the period when discrimination was
difficult but still possible.

The message, "This is a context for discrimination," is carried
partly by the earlier training and partly by every circumstance
of the laboratory, the harness, the smell of the experimenter, and
so forth. All these ancillary stimuli are, in fact, indications to the
dog that he is now in a context for discrimination. At this point,
the dog starts to show grossly disturbed behavior; it may bite its
keeper, refuse food, become comatose, etc.

If the experiment is started with a naive dog and the preliminary
training in discrimination is omitted, the dog does not go crazy.
If you start with a dog untrained in discrimination and present
a single stimulus object (flipping a coin to decide what this object
shall mean), the dog has to guess and will do the appropriate
thing; it will gamble on the difference. The dog cannot toss a
coin, but it settles, in general, to approximately the probabilities
which it experiences. If the stimulus object means \ 70 per cent of the
times and )' 30 per cent of the times, the dog will settle to guessing
at .V 70 per cent of the time and guessing at y 30 per cent of the time.
This is not the ideal course which the sophisticated gambler would
follow; he, of course, would bet on x 100 per cent of the times be-
cause it gives more frequently the positive reinforcement.

What happens, it seems to me, in the pathogenic experiment is
that the experimenter succeeds in communicating to the dog a
message about the contingency patterns in which it is to find
itself, and this message happens to be an untrue message. The
dog is in a probabilistic situation, but the experimenter has con-
vinced the dog that it is in a discrimination situation, at which
point very severe pathological changes start to appear.

1 80 Information Storage and Neural Control

These are the situations which, in our work on schizophrenia,
have come to be called "double-binds." These may now be defined
very simply as pathological alterations of communication at the
third level.

Let me illustrate this pathogenic pattern, or perhaps I should
say broken pattern, rather briefly with an excerpt from a book
entitled Mary Poppins (3). This is an English children's book by
P. L. Travers about an English nanny Mary Poppins. She has
taken the two children to a little old gingerbread shop owned
by Mrs. Corry, a tiny old woman with two large "sad" daughters:

"I suppose you've come for some gingerbread?"

"That's right, Mrs. Corry," said Mary Poppins politely.

"Good. Have Fannie and Annie given you any?" She looked at
Jane and Michael as she said this.

"No, Mother," said Miss Fannie meekly.

"We were just going to, Mother — -" began Miss Annie in a
frightened whisper.

At that Mrs. Corry drew herself up to her full height and regarded
her gigantic daughters furiously. Then she said in a soft, fierce,
terrifying voice, "Just going to? Oh, indeed! That is very interesting.

And who, may I ask, Annie, gave you permission to give away my
gingerbread — ?"

"Nobody, Mother. And I didn't give it away. I only thought — "

"You only thought! That is very kind of you. But I will thank you
not to think. I can do all the thinking that is necessary here!'' said
Mrs. Corry in her soft, terrible voice. Then she burst into a harsh
cackle of laughter.

"Look at her! Just look at her! Cowardy-custard ! Cry-baby!" she
shrieked, pointing her knotty finger at her daughter.

Jane and Michael turned and saw a large tear coursing down
Miss Annie's huge, sad face, but they did not say anything, for,
in spite of her tininess, Mrs. Corry made them feel rather small
and frightened . . .

In this episode Mrs. Corry indicates that this is a context in
which to have given gingerbread to the children would be re-
warded and not to have given gingerbread might be punished.

Patterns of Human Behavior 181

The daughter Annie tries to alibi for not giving gingerbread, and
Mrs. Corry promptly punishes her. This is not, was not, that sort
of context at all; it was one in which the daughter had no right
to give away gingerbread and was wicked to even think of doing so.

The problem, then, for every individual in every interchange
is to maintain an up-to-the-minute grasp of understanding of the
state of the contingency patterns between himself and his vis a vis.
Consciously or unconsciously, he has to be able to recognize what
sorts of trigrams, or more complex sequences, should characterize
the relationship at every moment and to act in terms of these
recognitions. The individual has to predict from what occurred
previously which pattern is appropriate at the moment. This is
what we call understanding between persons. Without it or when
such understanding is traumatized or punished, very severe patho-
logical behavior may follow.

But such understanding is only possible because we are able to
predict, to guess correctly at a given moment, within what pattern
we are operating and within what pattern the other person is
operating. Prediction is the essence of the matter, and it is at this
point that double-bind theory links up with information theory.

Redundancy, as the term is technically used, is that charac-
teristic of the sequence of events that enables an observing subject
to make a better than probable guess at the next item in the
sequence, so that this next item, when it actually occurs, does
not provide 100 per cent new information. It is rather unfortunate
that the word redundancy has been used in this sense, because
coinfortable communication between people (we may even say
efficient communication between people) depends entirely upon
such ability to predict. It might have been happier to describe
the phenomenon of redundancy as a necessary condition of
efficiency rather than as a characteristic excess since it is economical
to deal with patterns rather than with multiple bits.

It is now appropriate to think for a moment about the place
in human natural history of patterns of this order. Bavelas (per-
sonal communication) has shown that these orders of learning are
singularly difficult to modify when erroneous learning has occurred.
The experimental material is somewhat as follows: The subject
is presented with a board on which there are a number of buttons

1 82 Information Storage and Neural Control

and is told to find the correct way to press these buttons. He is
told that when he presses them correctly, a bell will ring. The
subject proceeds to press buttons, and after he has pressed, say,
fifty buttons, the bell rings. The experimenter now asks him if
he knows how to do it and if he will do it again. The subject
again presses buttons, and after he has pressed about forty-five
buttons, the bell rings. He is again asked to repeat the task, and
this time after about forty pressings the bell rings. The subject
is doing better and better. When the subject has reduced the
number of pressings to about twenty, Bavelas stops the experi-
ment and tells him that there is no connection between the buttons
and the bell, that the bell is only geared probabilistically to a
hypothetical learning curve.

The subject will then look Bavelas firmly in the eye and tell
him he is lying. This, of course, is true except that the subject is
wrong as to which lie he is attributing to Bavelas. The truth is
that Bavelas was lying initially when he told the subject there
was a connection between the bell and the buttons, but he is
now telling the truth. The subject, however, cannot be convinced
of this and will reassert his theory of the interrelation between
the buttons, usually quite a complex theory with a lot of paren-
thetical cautions in it: "At this part of the sequence you should
not go too fast"; "If you go too fast, you can only correct it by
going back to the beginning of the sequence," etc. The subject
is perfectly certain that what he was doing was related to the
theory he built up and that his experience has validated this
theory. He has been, after all, well reinforced in this belief by his
steadily increasing success.

There is, I understand from Bavelas, only one way of dis-
illusioning the subject in regard to his theories about these buttons.
This is by asking him to perform the experiment upon a second sub-
ject. As he does this and sees the second subject develop analogous
but dissimilar illusions, he realizes the nature of the situation and
the process through which he has gone.

The point I want to make is that these impressions, illusions
at the third level, are held very deeply and are exceedingly difficult
to disturb; the same must be true of knowledge and wisdom at
the third level. I have mentioned that the subject trained in an

Patterns oj Human Behavior 183

instrumental philosophy will, of course, encounter a universe which
will seem to him to validate that philosophy and that a subject
trained in a Pavlovian universe will correspondingly, as it seems
to him, encounter a universe in which the Pavlovian philosophy
is appropriate.

It is a formal characteristic of this level that opinions about it
are, in general, self-validating, and, of course, a great deal of the
difficulty in psychotherapy occurs in wrestling with this particular
fact. The interchange between therapist and patient always seems
to the patient to validate those third level premises with which
he entered the therapy room. This is the phenomenon of "trans-
ference." The therapist's task is to endeavor to break up those
learnings at the third level for which the patient has been deeply
reinforced in the past, those learnings of which he is, in general,
almost unconscious and which necessarily have this characteristic
of being self validating ... no mean task.

At this point, we are approaching a fourth learning level: the
problem of changes at the third level. I have said that this is no
mean task for the therapist, and I think it is worth noting that
this is a task in which considerable meanness, in another sense of
the word, may be a necessary ingredient. To change one's basic
premises at this third level is always in some degree painful and
always difficult, and the therapist may be compared, if you will,
to Mrs. Clorry. He must, of necessity, put the patient in the wrong
at the third level. It is therefore essential that psychotherapy shall
be double-binding in the sense in which the word is defined here.
Mrs. Corry is pathogenic because she goes on doing this without
mercy. The therapist is curative insofar as he does it with wisdom
and with consistency. After all, Mrs. Corry, is inconsistent even
in her inconsistency and can, therefore, always surprise her victim;
whereas, the therapist must instruct his patient, albeit by implicit
methods, so that new expectations may replace the old and may
be rewarded.

This problem of fourth-level learning, of changes at the third
level, is a necessary part of human life. It obtains in courtship; it
obtains in initiation; it obtains in psychotherapy; it obtains, in
fact, wherever important reconstruction of relationship must occur.
We know very little about such phenomena, and I cannot tell

1 84 Information Storage and Neural Control

you much today. Certain aspects, however, are conspicuous
enough to be worth mentioning. First, it seems that such deep
clianges and the processes by which they occur are almost in-
variably cloaked with unconsciousness and with amnesia. The
ability of any couple to tell you what it really was that they went
through in courtship is approximately zero. They can tell you
dates, times, and places. They may be able to identify a single
striking episode, something that he did or she did which struck
the other with a moment's flash; but, in general, such processes are
not subject to recall and have not been investigated. Wliile there
is a great deal of fantasy about courtship, there is, as a matter of
fact, no recorded data regarding it in any culture of the world.
Similarly, the patient and the therapist are both virtually
unable to tell you what happened that led to psychotherapeutic
change. Theories are many; fantasies are many; recipes are many
and are always unsatisfactory. It is not too much to say that this
is a region of almost total scientific ignorance. I believe, however,
that it has to be analyzed, has to be studied, and will be studied
in the next twenty years, and that in this study, the branch of
information theory dealing with patterns of patterns, redundancies
about redundancies, will be a central tool.

REFERENCES

1. Attneave, Fred: Applications of Information Theory to Psychology, New

York, Henry Holt and Co., 1959.

2. Bateson, Gregory: Morale and national character, in, Civilian Morale,

2nd Yearbook of the Society for the Psychological Study of Social
Issues, edited by Goodwin Watson, New York, Houghton Mifflin
and Co., 1942, p. 71.

3. Travers, P. L.: Mary Poppins, New York, Reynal and Hitchcock,

1934, p. 121.

DISCUSSION OF CHAPTER VIII

Herman Blustein (Chicago, Illinois): Doesn't an adequate
communication system actually preclude the knowledge of the
rules of the game by both communicators and receptors of the
communication system?

Patterns of Human Behavior 1 85

Gregory Bateson (Palo Alto, California): I think there are
two questions combined here. One concerns the case where com-
munication is going along "smoothly," as I called it earlier. Is a
knowledge of the rules necessary? Obviously it is not. The rules
are provided; they are built in, and that is all we ask. To be able
to cough them up and inspect them is not necessary. So far I
think we are in agreement; however, behind this is the question
of "rules about rules" and "rules about rules about rules." I think
we always walk around wishing to be in the state of "things going
along smoothly," and wishing, therefore, not to turn over all this
disturbing stuff, i.e., unwilling to raise questions about the rules. We
may be forced to do this when things go wrong. We want some
of the rules to be steady. We hope we can operate on the common
assumptions of the culture which we share, and we hope to try
to get mutual understanding at that level. If we cannot, we may
be pushed into reexamining blemishes of the culture, but this will
be painful and always at an upper level which we do not want
to disturb.

Yasuhiko Taketomo (New York, New York): In the com-
ments on expectation in relationships, were you referring to
something like role-taking in psychiatric communication?

Bateson: I was doing so in a terribly loose context. I think the
evidence is going to come from such work as that of Birdwhistell,
studying expressive movement and expressive posture. This is not
a study of those movements which are quasi-linguistic, such as
thumbing a ride, but the study of those much less conscious and
much less voluntary elements in our movements. I think it is
going to appear that, while we talk with words, mathematical
equations, and other highly sophisticated devices, we are, in fact,
either leaning forward on the rostrum or scratching in our pockets
looking for a cigarette or some other object. All these movements
can be interpreted and handled, and are going to be interpreted
and handled, at this third level as sequence markers or signals
about the relationship. But when I lean forward or draw back
from you, these movements indicate to you whether I want you
to come forward and shoot me with questions or whether you
should beware of my defenses, and so on. I think the implementa-
tion is going to come from this area and from the field of micro-

1 86 Information Storage and Neural Control

linguistics in which modulations of loudness of voice, emphasis,
rasp, etc., are going to be the key signals.

Myron F. Weiner (Dallas, Texas) : Assuming that somebody
comes to you because he has had a breakdown of relationships
because, in turn, his metacommunications or metaconcepts, or
what he expects of the world, are somewhat different from what
he says he expects, do you think it would be of some value in
correcting his behavior to bring to his consciousness the fact that
his metaperception is quite different from what he thinks he
perceives?

Bateson: This is a problem of technique of psychotherapy.
Let nie reword Dr. Weiner's question: "Does it help to give him
insight?" I would not agree that insight is necessary and sufficient.
It may be sufficient, but I do not think it is necessary. I think that
experiences of effect in communication at these levels probably
are therapeutically necessary, but I do not think it is necessary
that these communications take the form of providing a guide to
conscious insight into the mechanics of these levels. Surely it
never happens. I do not know of any school of psychotherapy that,
as yet, has enough language for talking about these levels to even
attempt to give insight at these levels. We just do not have the
language to give that insight. I think we know that psychotherapy
occurs; but since it occurs in a culture which does not have sufficient
language to say what is happening, it follows that linguistic insight
is certainly not necessary.

W. R. Beavers (Dallas, Texas) : These remarks about the con-
text, or metalanguage, reminded me of Bion and his primitive
group concepts. He felt that in working with groups, he saw and
began to communicate with them, not about their intrapsychic
assumptions, but about the primitive group assumptions. As I
recall, there was an assumption of the fight-or-flight and of the
pairing group. This sounds very much like your ideas concerning
the basic mammalian assumptions underneath that which is con-
ventional conversation.

Bateson: I am slightly familiar with the ideas, but have not
worked with them.

PART III— NEUROPHYSIOLOGICAL ASPECTS OF
INFORMATION STORAGE AND TRANSFER

Moderator: Hebbel E. Hoff, M.D., Ph.D.

CHAPTER
IX

INFORMATION STORAGE IN NERVE CELLS

Frank Morrell, M.D.

►EHAVIORAL observations have generally supported the
notion that (aside from genetic information) there are two quali-
tatively different forms of information storage in the nervous
system. So-called "recent" memory is made of particularly labile
stuff. A cerebral concussion produces an amnesia not only for the
injury itself but also for the events immediately leading up to the
injury, a circumstance about which many lawyers are painfully
aware. The impact of experience requires time for fixation. If
neural activity is interfered with during this fixation or con-
solidation period by electro-shock (13, 14, 53, 54), trauma (50),
severe cold (44), or rapid induction of ether or barbiturate anes-
thesia (1), subsequent recall of the experience may be seriously
compromised. For example, Duncan (13) and Gerard (14, 44)
have shown that rats or hamsters trained in an avoidance situation
or in maze-learning have a normal learning curve if a maximal
electro-shock is delivered four hours after each training session.
If the shock follows the training by one hour there is slight de-
terioration; at a fifteen minute interval there is major interference
with retention and at five minutes or less, learning is completely
prevented. Acute anoxia introduced at similar time intervals has
the same effect (53). Since all of the agencies known to produce
amnesia or loss of recent memory are also known to alter electrical
activity of the central nervous system, the mechanisms subserving
the initial stage of memory recording are inferred to be electrical
in nature. Other evidence supporting a clear distinction between
short-term and durable memory mechanisms is the finding that

189

1 90 Information Storage and Neural Control

focal epileptogenic lesions prevent new learning, i.e., impair
memory recording, but do not disturb behavior learned before
establishment of the epileptic lesion (31, 34, 35, 42, 51).

A limiting case in the requirement for a finite fixation time is
the classical example of one-trial learning. However, even in this
instance, it has been supposed (Hebb) (24) that the neural con-
sequences of the single experience persist in the form of rever-
berating impulses for a considerable time after the environmental
signal has ceased. x\lthough all or none impulses circulating in
closed neuronal chains represent one possible mechanism for the
initial imprinting or short-term memory the actual kind or kinds
of electrical activity involved remain unknown. In fact, there is
nothing in the experimental evidence concerned with manipula-
tion of the consolidation process which affords compelling proof
that consolidation depends upon reverberating impulses of the
all or none type (41). Other kinds of electrical activity, that is,
other than the classical axon spike or even the conventionally
recorded EEG may well be equally important.

I should like to present some evidence which suggests that
cortical steady potential gradients may have a determining in-
fluence in the process wherein a sequence of impinging impulses
is transformed into structural change in the nervous system. This
portion of the paper, therefore, is concerned with the initial or
electro-sensitive stage of memory recording.

Significant shifts of the cortical steady potential have been shown
to occur consequent to stimulation of peripheral receptors (19) as
well as when stimulating electrodes are applied directly to brain
substance (2, 7, 8, 9, 16, 17, 18). Some years ago, we found (37)
that the surface negative DC shift resulting from low frequency
stimulation of nucleus centrum medianum in the thalamus would
appear as a conditioned response to a pure tone after thirty to
forty paired trials.

Figure 1 illustrates the first paired trial. The tone elicited no
response. Upon onset of the thalamic stimulus, a pronounced
negative shift of the base line of the EEG occurred, which was
confined to the hemisphere ipsilateral to the stimulated site.
After about forty paired trials (Fig. 2) a similar DC shift was
regularly induced by the tone alone. Note particularly that this

Information Storage in Nerve Cells

191

RC
LC

v/-^^^v^MVUWv^

Tone

'^^^-h^^

t Stim.

■U .J

I stim. off

Fig. 1. Initial trial in which a low intensity 500 cycle per second tone lasting
ten seconds is paired with four per second shocks (6 volts, 1 millisecond duration)
delivered through bipolar stimulating electrodes in the left centre median. The
tracing is from an unesthetized rabbit. Electrodes derived from the somato-
sensory regions of both hemispheres and recorded monopolarly to a reference
on the pinna. A and B are a continuous sequence, (A) indicating the pronounced
negative shift witli slight after-positivity on the onset of thalamic stimulation
and (B) the reversal of steady potential shift at the cessation of thalamic stimu-
lation. Note that the steady potential change is limited to the ipsilateral hemi-
sphere. Calibration: 50 microvolts and one second (37).

R C r

L C

•Jr, V^/.V

^>^^^.-^/V■•f'^^^ '^V^ ^ . ,

^YKr}r^f'r,^\-J^r^\j'. --r '

Tone

•/ V ../I

Fig. 2. Same experiment as in Figure 1. After forty trials the tone alone elicited
the same ipsilateral negative-positive steady potential shift. This gradually dis-
appeared over a series of sLx unreinforced trials but was restored by a single
subsequent reinforcement witli thalamic stimulation. Calibration: 50 microvolts

and one second (37).

1 92 Information Storage and Neural Control

conditioned DC shift was also restricted to the previously stimu-
lated hemisphere although the tone was presented to both ears
equally in open field conditions.

In addition to such direct conditioning of a cerebral electrical
event, the increasing availability of DC amplifiers made it possible
for Rusinov (49) and, more recently, Rowland (47) to identify
steady potential shifts occurring regularly in the course of classical
behavioral training. Rusinov (48) also discovered a most intriguing
behavioral eff'ect when low-level surface positive polarizing cur-
rents were applied to a part of the motor cortex. The current levels
employed were sub-threshold with respect to direct production
of limb movement. But during the period of current flow (and
for some minutes afterward) any ambient sound, light or touch
would produce the limb movement to be expected from adequate
(supra-threshold) stimulation of the motor area to which the
current was applied. Rusinov felt that the anodal polarization
produced a "dominant focus" of excitation which facilitated the
development of a temporary connection between, for example,
the auditory and motor systems.

We have been able to confirm the Rusinov experiment in our
own laboratory and, in addition, have made some observations
on the activity of single nerve cells in such polarized regions (40).
Single cells in motor cortex did not respond to acoustic stimulation
before polarization (Fig. 3A). During the passage of anodal cur-
rent (10 microamps) cells of several different types (Figs. 3B, C
and D) were easily triggered by the same acoustic signal. Since
we were interested in mechanisms for information storage we per-
formed the experiment in a slightly diff'erent way, a way which
allowed observation of a selective sensitivity with respect to signals
differing in their history of exposure to polarizing current. A group
of stimuli was chosen and all members of the group were presented
repeatedly to the animal until habituation (as judged by lack of
behavioral or EEG response) was complete. A polarizing electrode
together with a fine microelectrodewas placed on the motor cortex
and the current was turned on. One member of the previously
habituated stimulus group was selected (in this case a 200 cycle
per second tone) and was presented to the animal about thirty
times in the course of forty-five minutes. A burst of unit activity

Information Storage in Nerve Cells

193

Fig. 3. Patterns of response in single units to an acoustic stimulus. Duration of
the tone of 200 cycles per second is indicated by the two upward deflections in
the second channel of the oscilloscope. Before polarization (A) there was no
effect on the discharge frequency of a unit in motor cortex. During polarization
responses to sound appeared either in the form of a single high frequency burst (B),
a sudden cessation of firing (C), or high-frequency bursts at the "on" and at
the "oflf" of the tone (D). Calibration: 5 millivolts and one second (40).

194

Information Storage and Neural Control

A B

Fig. 4. "Generalization and differentiation" in single unit responses. During the
passage of anodal current (A & B) the critical tone (A) and a single presentation
of the indifferent tone (B) were equally effective in provoking high frequency
bursts. Twenty minutes after cessation of current flow (C & D) the critical tone
(C) continued to elicit the response while the indifferent tone (D) did not. Forty
minutes after discontinuing polarization (E & F) neither signal produced any
change in unit discharge frequency. Calibration: 2 millivolts and 500 milli-
seconds (40).

occurred with each stimulation (Fig. 4A). Another member of
the habituated stimulus group (500 cycle per second tone) was
presented once in the polarization period and also elicited unit
discharge (Fig. 4B). The current was then discontinued and in
the following twenty minutes the 200 cycle per second tone con-
sistently provoked unit activity (Fig. 4G) while the 500 cycle

Information Storage in Nerve Cells

195

Fig. 5. Conditioning of a rhythmic burst response to a single flash. Anodal
polarization was applied to the visual receiving area. Single flash elicited a
single burst in a quiescent (A) and in a randomly firing cell (B). Three per
second stroboscopic stimulation (C) produced driving of unit discharge at tliat
frequency. A single flash (D) delivered thirty seconds after termination of the
rhythmic stimulus resulted in repetitive unit discharge at about three per second.
Unit potentials are seen in the upper channel of tlie oscilloscope; stimulus artifacts
in the lower channel. Amplitude calibration: 2 millivolts. Time calibration:
500 milliseconds (A & B) and one second (C & D) (40).

per second tone (Fig. 4D) invariably failed to induce a change in
the pattern of unit firing. About forty minutes after cessation of
polarization neither signal was effective (Figs. 4E and F). Under
these circumstances it seemed evident that the polarized cell
population had retained some stipulation of signal characteristics
so that for a brief period in the post-polarization interval the cells
behaved differentially with respect to the two signals.

Short-term storage of a temporal pattern has also been observed
in cells of the visual cortex. Figure 5A illustrates the response of
a quiescent cell to a brief flash of light, (the flash artifact is recorded
on the second beam of the oscilloscope). Figure 5B shows a similar
burst response in a spontaneously active cell. During anodal
polarization it was extraordinarily easy to "drive" such cells with

196

Information Storage and Neural Contiol

c
en

a;
(/)

c
o

o
E

.d
cr

00 r

B 80 -

60 -

40 -

05 I 10 20 30

Time in minutes after "conditioning"
train of 3 /sec flicker

Fig. 6. Time course for "decay" of conditioned rhytJimic response of single

cortical unit.

low frequency intermittent light (Fig. 5C). After a few minutes
of stimulation the three per second flash was discontinued and
thirty seconds later a single flash resulted in a series of bursts
having a three per second frequency (Fig. 5D). Single flashes
delivered at intervals longer than thirty seconds were less and
less likely to provoke such a rhythmic response but occasional
rhythmic responses to single flash were noted as long as twenty
minutes after the end of the conditioning train (Fig. 6). This
seems a particularly clear illustration of the capacity of the polar-
ized cells to retain some representation of an imposed stimulus
pattern for a relatively long period of time. Indeed the order of
magnitude of this time interval is itself significant. It correlates
well with the data of Gerard (14), Duncan (13) and others (44,
53, 54) on the abolition of learned responses consequent to massive
electro-shock delivered at various intervals following the training
session.

Information Storage in Nerve Cells

197

CJ-2 -\^\^\^\^
A

R»E.

((((

Tet.

-v^v

Post.

S R T

/v^

^A^r-

Pre. Tet. Post.

Fig. 7. Oscillographic tracings from a deeply anesthetized (A) and an unanes-
thetized (B) cat. Derivations are from implanted bipolar electrodes (R) arranged
as indicated in the diagram. Recording electrodes (R) are situated between the
stimulating electrodes (S) on one side and the tetanizing electrode pair (T) on
the other. Explanation in text. Calibration: 100 microvolts and 100 milliseconds.
Negativity at the recording electrode produces a downward deflection of the
beam in this and the two succeeding figures. (Chow, K. L. and Dewson, J.:

unpublished data.)

Short-term storage of a temporal pattern may also be demon-
strated in another way. Following a technique originally described
by Roitbak (45), Doctors K. L. Chow and James Dewson (10)
have used tetanization of a local cortical region to produce an
effect similar to that of the ''dominant focus." Three pairs of
implanted electrodes were arranged as shown in the diagram of
Figure 7 so that the stimulating pair was at one end of the array,
the tetanizing pair at the other end, and the recording electrodes
in between.

In the deeply anesthetized animal (Fig. 7A) nine per second
shocks delivered before tetanization produced only a small direct
cortical response (DCR) arising almost immediately out of the
shock artifact. The nine per second stimulus was continued through-
out the fifty per second tetanus and into the post-tetanization
period. Immediately following the tetanus the response to the
shock was altered and could be distinguished from the DCR by

198

Information Storage and Neural Control

■,'<f "

Pre.

Tet.

3cps

[c d

Fig. 8. Influence of tetanization across a cortical section. Cat is unanesthetized,
awake, and carries implanted bipolar electrodes arranged so that recording (R)
and stimulating (S) pairs are within an island of neuronally isolated cortical
tissue while the tetanizing (T) pair is on the intact cortex outside the isolated
zone. Pre- and post-tetanization voltage is the same as that used in the tracing
labeled "3 cps." Stimulating voltage is then reduced 50% (A) and 70% (B).
The pre-tetanization voltage is then resumed and then stimulation stopped (D).
Further explanation in text. Calibration: 100 microvolts and 100 milliseconds.
(Chow, K. L. and Dewson, J.: unpublished data.)

a longer latency and a hump on the descending limb. The response
had never occurred prior to tetanization and was clearly locked
to the stimulus frequency. However, in the waking animal (Fig. 7B)
where spontaneous background rhythms were more prominent,
phase locking was much less precise and it was difficult to be sure
of a response truly related to the stimulus frequency.

In order to determine whether the effect of tetanization on the
responsiveness of cells at the recording site was mediated through
synaptic connections rather than by an electrotonic or field effect,
a neuronally isolated cortical slab was prepared. The blood supply
to the slab was preserved. The electrodes were arranged so that
the stimulating and recording pairs were within the isolated zone
but the tetanizing pair was placed outside (Fig. 8).

Information Storage in Nerve Cells 199

Background electrical activity in such neuronally isolated regions
is much less prominent than in normal cortex even when the
animal is awake. The pattern more closely approximates the
deeply anesthetized state. The cells perhaps are not so busy doing
other things and are therefore more a\^ailable for recruitment by
an active pacemaker. Thus it is not surprising" that the responses
obtained are similar to those of Figure 7A. Nine per second shocks
before tetanization produced only a small DCR. After tetanization
a new response emerged which was tightly locked to the stimulus
frequency. Changing the stimulus frequency to three per second
perturbed the system somewhat but clear responses did appear
at the new frequency. When the stimulus voltage was reduced
by about half (Fig. 8A) the responses were also reduced and were
less reliably evoked. Further reduction of stimulus voltage abolished
all response (Fig. 8B) but returning to the pre-tetanization voltage
restored the stimulus-locked response in full (Fig. 8C). When the
stiinulus was turned off (Fig. 8D) there was no trace of an after-
discharge.

The variations in stimulus voltage just described indicate that
the response arising after tetanization depends upon the specific
stimulus. It cannot be explained as an apparent coherence re-
sulting from emergence of a background rhythm, paroxysmal or
not, having frecjuency characteristics close to those used to stimu-
late. Finally, as suggested above, this experiment supports the
notion that the tetanization effect is transmitted to the cells at
the recording site by non-synaptic means.

Figure 9 illustrates an experiment similar to that just described.
Electrodes were arranged exactly as in the diagram of Figure 8
within and beside a neuronally isolated cortical region. Direct
your attention particularly to Figure 9B. In this experiinent the
pre-tetanization stimulus frecjuency was nine per second. After the
tetanus an altered but stimulus-locked response was evident at
nine per second. At that point during a period coincident with
the second tracing the experimenter slowly changed the stimulus
frequency from nine to three per second. The response pattern
reflects the changing temporal character of the signal and locks
in at three per second (third tracing). But something else has
happened as well. Between the major three per second deflections

200

BC5I

Information Storage and Neural Control

pre.

5 s.

i".-^\^

let.

\/v/\Ay\,^r^iPw^/^\Ar.

9cps

V^/v

3cps

Fig. 9. Same preparation and same electrode arrangement as in Figure 8. The
relevant section is labeled "B." A pre- and immediate post-tetanization stimulus
frequency of nine per second was shifted to three per second. As the response
"locks" in at three per second (last tracing) a series of inter-stimulus, regularly
spaced waves appear which seem to recapitulate the nine per second rhythm
For discussion, see text. Calibration: 100 microvolts and 100 milliseconds.
(Chow, K. L. and Dewson, J.: unpublished data.)

are evenly spaced smaller potentials which seem to recapitulate
the earlier nine per second sequence. Is this the electrical expression
of the neural trace of nine per second? Unfortunately the experi-
ments are still too few to afford a confident answer.

How, it may be asked, can these experiments with tetanization
be linked to the earlier studies on polarizing" currents? Although
measurements of the steady potential were not made in these
studies the inference is clear from the fact that tetanization is
effective across a solution of neural continuity that an alteration
of electrical field contours has occurred. Furthermore there is
abundant data from many laboratories to indicate the drastic
alterations of steady potential gradients accompanying tetanic
stimulation of this kind (7, 8, 9, 15, 16, 17, 18).

Information Storage in Nerve Cells 201

Now it may be legitimate to question the pertinence of these
examples of cellular memory or the potential gradients with which
they are associated to anything resembling" a functional memory
in the behaving animal or man. Nevertheless, these considerations
have led to a direct test of the hypothesis that manipulation of
the cortical steady potential might alter a learned response.
Particular attention was paid to the sign and orientation of the
electrical field and to behavioral evidence bearing upon a dis-
tinction between learning and memory.

Twelve mature male rabbits weighing between two and three
kilograms were used. Prior to training, the animals were operated
upon under nembutal anesthesia with sterile precautions for im-
plantation of six stainless steel epidural electrodes. In addition,
nylon plugs were inserted bilaterally over motor (both fore limb
areas) and visual cortex.

Beginning no earlier than one week after surgery, the animals
were trained to perform a conditioned avoidance response which
involved lifting of the right forelimb. The unconditional stimulus
(UCS) was an electric shock (sixty per second square waves) of a
voltage just sufficient to cause unconditional limb withdrawal on
all stimulus applications. Stimuli were delivered through needle
electrodes in the foot pad. The conditional stimulus (CS) was a
flickering light at eight to ten flashes per second from a Grass
stroboscope placed ten inches in front of the animal at eye level.
Stimuli were presented by hand and the UCS was omitted if the
animal responded by flexion of the appropriate limb within 1.5
seconds after flicker was turned on. The animals were trained to
a level of 70-75 per cent correct responses in twenty trials before
polarization was begun. The low level of performance was chosen
intentionally in the hope that we might be able to observe enhance-
ment of conditioning as well as depression secondary to polarization.

Capillary pore electrodes (saline bridge and silver-silver chloride)
were placed in the nylon plugs for polarization. A constant current
stimulator having a high source resistance was used. Current was
continuously monitored and maintained at about 10 microamps per
square millimeter. Pore diameter in the visual cortex was 5 milli-
meters, thus covering more than half of the visual cortex on each
side. Pore diameter in motor cortex was 2 millimeters. Current

202 Information Storage and Neural Control

return was effected through a saHne soaked pad held against the
palate by the mouth bar of the head holder. Fine needle electrodes
were inserted into the forelimb flexors to record the electromyo-
gram. The animals were restrained with the head fixed and the
limbs free. Figure 10 illustrates the electrical record from one ani-
mal at the beginning of the paired trials (Fig. 1 OA) when there was
no C;R and later (Fig. lOB) when the ClR was present allowing the
animal to avoid the shock. Polarization had not been applied at all.

These animals were given twenty trials a day every day except
for occasional interruptions which may be seen as breaks in the
graphs. After achieving the 70-75 per cent criterion polarization
was applied (either anodal or cathodal to either motor or visual
cortex) throughout the training session on a given day. "Polariza-
tion days" and "non-polarization days'' as well as type and site of
polarization were distributed randomly within the above criterion
limits. As an additional control for possible sensory effects of the
constant current, training sessions were carried out during polariza-
tion of the ear.

Figure 11 shows a graph of the learning curve in a typical
animal. Per cent correct responses in each day's block of twenty
trials is indicated by the points on the graph. The black dots
represent days on which no polarization was given. The two inter-
ruptions in the graph signify that training sessions were omitted for
one or more days. The legend indicates the site of cathodal polari-
zation in the six sessions in which it was applied in this animal.

Cathodal polarization of the ear and of the motor cortex resulted
in no apparent change in the "expected" per cent response.
Bilateral cathodal polarization of the visual area produced a
striking deficit in performance for the entire training session
during which the current was applied. Strangely enough the
decreased performance was also apparent the following day when
training was carried out without polarization. Note, furthermore,
that a break in the sequence of daily training sessions of one or
more days was almost invariably followed by a prominent deterior-
ation in performance (Figs. 11, 12, 13, 14). The latter was charac-
teristic of all of these animals under the experimental conditions
outlined. The only exception is illustrated in Figure 12A where
the first lapse in training of one day was not followed by deterior-

Injormation Storage in Nerve Cells

203

-Xy^Av^^^-^l^yi.^Vlvjv ~^-~'-"^~'~-^^-'j|yjW'tai1^"'''W\^.XUvvV~

.^-~v'A*'>».~'v»,(^llf(|lJ,YfV'*'''''

'As//^/-^yv'A;-*A,Ay-^//,^;s/v,\,^^-,-v.yvv^^/*

7 v*'V^'^''''^^''',*AVy.»^/

Fig. 10. Unconditioned (A) and conditioned (B) avoidance response in the
rabbit as recorded by electromyogram of the right forelimb. The upper six
channels are EEG tracings obtained at the same time. Electrodes 1, 3, and 5
derive respectively from left motor, somatic sensory and visual corte.x. Electrodes
2, 4, and 6 are corresponding placements on the right hemisphere. Calibration:
50 microvolts and one second. Further explanation in the text.

b60
-a

*-50

:30

;?*io

\ \ \

CoThodal Vo\Q<r\zar\ox\(RiZc-bO)

0 PoloriZQTion ofmoTor corres
9 Polarizorion of visual corr&x
^Polarization of ear ,

Time in daijs

Fig. 11. Effect of cathodal polarization and of a break in training on a typical
learning curve. In this and subsequent curves only the later portion of the full
curve is plotted, i.e., beginning when the animal was making at least 40 per cent
correct responses. In most animals it took two to three weeks (or 280-420 trials)
before the level at which the graphs begin was reached. See text.

204

100
90

60
50
40

0 20

♦-

3-10

t-
.&00

s<?0

880

fj 70
o

1 60
50
40
30
20

Information Storage and Neural Control

a. Cathodal Polarization CR2&C-0O)

0 Polanz-ation (-) of mofor cortex
9 Polarization (-> of visual correx
(§) Polor'iz Qi-ion (-) of ear

I \ r \ r

b. Anodol Polarization (R290-0OJ

O Polarization (■>-) of motor cortex
0 Polariz-otion (.-I-) of visual cortex
@ Polorizotiani.-*-) of ear

10 -

15 20

Titne in daqs

Fig.

12. Effect of cathodal (A) and anodal (B) polarization on learning curves in
two different animals. See text.

ation, although on the second and third occasions when training
was omitted for four and three days, the usual decrease did occur.
It seems unlikely that the impaired performance on the day
following cathodal polarization was due to a long persisting effect
of the cathodal current. Indeed as we shall see later, the level of
decrement on the day after cathodal polarization of visual cortex

Information Storage in Nerve Cells

205

100
90
80
70

^0
•/I
"5 30

•I-
^20

t-
c

•I-

olOO

Cil

o90
o

c80
o
b TO

60
50
^0
30
20
10

a. Cathodal Polari'zafion ('/?/9c-6(3j

0 Polar) zat'i on (-) of motor corTex
9 PolariT-Qtioni-) of viiual correx-
(§) Polar I z Qfion(-)ofear'

\ I . I

b. Anodal Polarixation (Rloa-oO)

O PolariTiotionCf) of motor cor re.\
0 Polar} zotioti (-t) of- v'S uol cartas
@ Polar/ zotion C^) of ear

15 10 15 20 25 30

Time in claims
Fig. 13. Effect of cathodal (A) and anodal (B) polarization on learning curves
in two additional animals. Explanation in text.

corresponds closely to that occurring after a lapse in training.
It would appear that training under conditions of cathodal polari-
zation of visual cortex did not result in registration or retention
of that day's experience. The animal behaved as though there
had been no training at all on that day.

Figures 12B and 13B present curves of the typical response
pattern in two animals subjected to bilateral anodal polarization
of motor cortex, visual cortex and the ear. There is no evidence

206

Information Storage and Neural Control

Carhodoi Polarization Ancdal PolorizQt-ion

R3Z Qc -60

O 0 Polarization of motor cortex
-0' O Polarisation of viiual cortex
© ® Polarization of ear

Tim<2 in claims

Fig. 14. Effect of cathodal and anodal polarization applied on different occasions

in the same animal. Note again the marked decrease in performance following

a six day break in the training schedule.

that performance was significantly altered on the day on which
polarization was carried out at any of the three sites. It is interesting
that in every instance (see also Fig. 14) there was an abrupt rise
in the response per cent on the day following anodal polarization
of visual cortex.

Figure 14 presents the data on one of the two animals receiving
both anodal and cathodal sequences. Note again the depression
of performance during passage of cathodal current in visual cortex
and the maintenance of depression on the day following. The usual
performance decay following a lapse in training is also apparent.

In the same animal the application of anodal current to the
same three areas resulted in no significant change. Yet on the
day following the visual anodal polarization performance reaches
its all time peak for this animal.

Figure 15 illustrates sample records of conditioned responses
obtained under the various conditions in this experiment. Figure
15A represents cathodal polarization of the motor cortex. Note
the characteristically long latency of the CR although the animal
responded correctly as many times per session as it would without

Information Storage in Nerve Cells 207

A B c D

w«^-v-^.v-W'Av^\^/^WwV'^ '*^.-''>^'f^j(./v^*^'"j--»-^v"'^''^ ,^-.^.>^^^l'Wf^^•l||^'V^vV^^ "^"^wUff 1 ^'^-^■^-'^^^''V"

Fig. 15. EEG tracings and conditioned response performed during various con-
ditions of polarization. A. — cathodal polarization of motor corte.x. B. — anodal
(motor). C— cathodal (visual). D. — anodal (visual). Calibration: 50 microvolts
and one second. Explanation in text.

polarization. On the other hand, during anodal polarization of
the motor cortex (Fig. 15B) the latency of CR was very short and
the amplitude of photic "driving" was much reduced. A similar
EEG pattern was observed during application of cathodal current
to the visual areas (Fig. 15C). Reversing the direction of current
flow (Fig. 15D) resulted in considerable augmentation of photic
"driving."

A summary of data in critical training sessions is given in Table I.
The number of observations and the median and range for the
number of conditioned responses per session are listed for the
following conditions:

Polarization of ears; motor cortex, anodal, cathodal; visual cortex,
anodal, cathodal; session before anodal and cathodal (visual)
polarization; session after cathodal (visual) polarization; session
after anodal (visual) polarization; session after break in training.

Statistical analysis of these findings leads to the following con-
clusions:

1) Performance under tlie condition of visual cathodal polari-
zation differs from that of visual anodal, motor anodal
and cathodal and ear (anodal and cathodal) at better
than the 1 per cent level of confidence.

208 Information Storage and Neural Control

2) There is no significant difference in performance between
any one of the polarization concUtions (except visual
cathode) and any other.

3) Performance on the day JoUowing visual cathodal polari-
zation differs from that on the day preceding it at the 1
per cent level.

4) There was no significant difference between performance
on the day following visual cathodal polarization and the
day after a break in training.

5) Comparison of performance on the day after visual anodal
polarization with that on the day preceding (visual,
anodal) polarization yields a difference significant at
better than 1 per cent level of confidence.

With respect to the last "conclusion'' listed, one must hasten
to add that there is no justification for attributing the improve-
ment to an effect of anodal polarization. The difference may
simply reflect the rising learning curve or the gain normally
expected in two days of practice. Only if the experiment were
performed at a point on the learning curve where the gain to be
expected in two days of practice was negligible could one attribute
the change to the neurological intervention. Such was not the
case in these experiments and therefore the influence (if any) of
anodal polarization of the cortical receiving area for the conditional
signal remains uncertain.

In summary, it is clear that the imposition of a surface negative
potential gradient, along the axis of the main neural elements
of the cortical receiving area for the conditional signal, interferes
with conditioned performance and prevents retention of the
experience acquired during such polarization. Although the evi-
dence is much less conclusive it seems possible that surface positive
currents, while not producing any improvement in performance,
may lead to increased retention of the information transmitted
during the period of current flow. These last experiments lend
some support to the notion that the electrophysiological changes
secondary to imposed potential gradients, illustrated in the previous
studies, may have behavioral significance and may be relevant
to the manner in which the central nervous system achieves short-

Information Storage in Nerve Cells

209

pa oi

^^r

^ TT< '^

^ >")

(U ^» G

210 Information Storage and Neural Control

term information storage. Despite these brief and uncertain insights,
it is most tantaUzing to realize that even at the single unit level
of analysis the nature of the neural code has still strangely escaped
detection. Thus in Figure 5D the segment of record preceding
the application of the single test flash revealed no trace of the
information which the subsequent stimulus demonstrated had
been retained in that particular cell. This negative evidence argues
against the notion that the short-term memory trace is preserved
by means of nerve impulses continuously circulating in more or
less closed neuronal chains. The recording systems employed
should have been adequate to discern such activity had it been
present. Perhaps the relevant electrical signs are more likely to
be found in tlie slow local oscillations of synaptic potential or
other sources of slowly varying voltage. Such oscillation would
have been missed by the short time-constant recording system.
It is also possible, of course, that the encoding process took place
in cells penultimate to the one monitored.

I should now like to leave the question of electrical mechanisms
for information storage in the nervous system and turn to some
other approaches which have recently gained prominence.

While it seems reasonable to postulate an electrical basis for
the labile short-term storage mechanism, it is certainly difficult
to assume that the relatively permanent memory trace which
remains undisturbed by the drastic perturbations of cerebral
function produced by convulsions, electro-shock, concussion, or
anesthesia so deep as to cause electrical silence, can be based
upon continuously circulating nerve impulses (6, 41). Most
workers, therefore, have tended to think more in terms of morplio-
logical or chemical alterations. The essential thesis argues that
recurrent impulse impingement or synaptic bombardment results
in a durable morphological or chemical change which renders
tliat particular junction or cell more easily susceptible to subse-
quent activation via the same pathway.

Recently hypotheses implicating ribonucleic acid (RNA) in the
molecular organization responsible for long-term information
storage have been proposed quite independently by a number of
workers (14, 26, 27, 29, 30, 32, 39, 43). To my knowledge the first
statement of this general hypothesis in the English literature was

Information Storage in Nerve Cells 211

that of Katz and Halstead in 1950 (30). Most recently in a series
of lectures and articles Hyden (26, 27) has forcefully argued for
implication of RNA in the molecular mechanism of memory.
Although the concrete evidence is sparse, some is now available.
Kreps (32, 56) in the Soviet Union, is reported to have demon-
strated an alteration in RNA synthesis in regions of the nervous
system related to the conditioned stimulus after establishment of
the conditioned response. In cats, John, Wenzel and Tschirgi (29)
noted that intraventricular injection of ribonuclease was followed
l^y deterioration of pattern discrimination lasting" about four days.
Avoidance CRs in the same animal were unaffected. Unfortunately
no control data were presented with regard to local or general
changes in brain RNA content or turnover. Therefore, since other
substances such as calcium or potassium ion also reversibly impair
CR performance, it is not certain that the disturbance was related
to an alteration of the RNA substrate rather than to another more
nonspecific action of ribonuclease.

Our own explorations in this area were derived from an inci-
dental observation made in the course of investigation of an
entirely separate problem. We had been studying some physio-
logical properties of the chronic focal epileptogenic lesion produced
in animals by local freezing of a small area of the cortical surface
(36). Following this procedure, the gradual establishment of an
epileptogenic lesion may be verified by recording the paroxysmal
electrical activity which appears in the cortical tissue immediately
adjacent to the frozen zone. We were interested in studying the
ontogenesis of an epileptic lesion from a chemical as well as an
electrical point of view, and among a number of findings was the
observation that nerve cells in the area of epileptic discharge
stained densely with methyl green pyronin (39). Methyl green
pyronin is one of the substances generally used for the histochemical
demonstration of RNA.

It was not particularly surprising to find increased concentra-
tions of RNA in cells discharging at abnormally high rates. Hyden
and co-workers (4, 5, 20, 21, 22, 23, 25) using much more elegant
technicjues had already demonstrated increases in cellular RNA
consequent to prolonged stimulation. Recently lizuka et al. (28),
in Japan, have confirmed our own observations specifically with

212

Information Storage and Neural Control

1_3 A R 459

3-5

'»A»jf\VvK/V^'^'*''V^Ai>^^

5-7

[/Vf/VWVv^

v<^,^/JNv^VJ■

vJ^/~v^J\/v'AA^^vVV^--'^,A'vv^'VJV^''^-'^/VW^^

Fig. 16. Electroencephalogram of an unanesthetized rabbit twenty-four hours (A)
and three days (B) after production of an ethyl chloride lesion. The site and ex-
tent of the lesion are indicated by the cross-hatched area on the diagram. Deriva-
tions are bipolar from implanted electrodes over the indicated regions. Cali-
brations: 50 microvolts and one second (39).

respect to cells undergoing convulsive discharge. However, there
was another phenomenon noted in the animals with chronic
experimental epilepsy which made it possible to probe more
deeply into the relationsliip between ribonucleic acid and cellular
memory (43).

Freezing a small segment of the surface of one cerebral hemi-
sphere results within a few hours in the appearance of high voltage
epileptiform spikes confined to the site of the primary lesion. These
are illustrated in the upper part of Figure 16. Simultaneous
recordings from the opposite hemisphere and from other portions
of the same hemisphere did not reveal any abnormality. After a
time, varying from a few days to three weeks, one may observe
(Fig. 16B) the development of similar paroxysmal activity in an

Information Storage in Nerve Cells

213

Fig. 17. Characteristics of the dependent mirror focus. In the ink-written tracing
the upper two channels record the primary focus and the lower two channels
the mirror focus. The ethyl chloride lesion is indicated by cross hatching. Cali-
bration: 100 microvolts and one second. In the oscillographic tracing, the upper
channel records the primary region and the lower one the secondary region.
Calibration: 100 milliseconds (39).

area of the opposite hemisphere homotopic with that of the primary
lesion. The contralateral hemisphere had not been exposed or
damaged in any way during the original operative intervention.
The paroxysmal discharge in the contralateral hemisphere results
directly from massive synaptic bombardment over known ana-
tomical pathways from cells of a primary epileptogenic lesion.
Consequently the electrical abnormality in the contralateral focus
is considered to represent a secondary epileptogenic lesion (38).
At first the secondary discharge w^as clearly dependent upon the
primary in the sense that spikes only occurred in temporal con-
junction with those in the primary lesion, had a measureable
latency following the primary spike (Fig. 17) and disappeared
altogether after excision or neuronal isolation of the original focus.
The pattern of activity in the secondary area looks like a "reflec-
tion" of that in the primary and thus has earned the colloquial
name of "mirror focus." If the primary lesion was not excised
or isolated the mirror focus eventually became independent.
Secondary spikes were then unrelated in time to those in the
primary focus (Fig. 18) and did not subside if the original lesion

214 Information Storage and Neural Control

1-2

3-4
4-5

6-7
7-8

Fig. 18. Electrographic characteristics of the independent mirror focus. Electro-
encephalogram of an unanesthetized rabbit taken three weeks after production
of an ethyl chloride lesion in the area designated by crosshatching. Discharges
originating in the primary lesion (electrode 2) and in the secondary region
(electrode 7) are unrelated in time of occurrence. Note also that there is some
depression of activity in electrodes just posterior to the primary lesion while
this is not true in electrodes posterior to the mirror focus. Calibration: 50 micro-
volts and one second (39).

was subsequently ablated. We have demonstrated that the func-
tional characteristics of the cell network within the mirror focus
are more or less permanently altered and that the alteration is
manifested both by the spontaneous behavior of these cells and
by their response to stimulation (38, 39, 43).

The sequence just described may be prevented by section of
the corpus callosum either before production of the primary lesion
or within twenty-four hours afterward. In addition, the develop-
ment of independent secondary discharge may also be prevented
if the callosal connections remain intact, but a sub-pial partial
isolation of the contralateral cortex is carried out within the same
time interval. Figure 19 illustrates such a preparation. The
isolation deprives the cortex of all of its subcortical connections
as well as those relating it to other cortical areas in the same

Information Storage in Nerve Cells

215

Fig. 19. Dissectionof rabbit brain to illustrate features of the extracallosal isolation.
A slab of cerebral cortex in the hemisphere opposite the ethyl chloride lesion is
dissected so that the cortex is separated from all subcortical connections and
from the surrounding intracortical regions as well. The callosal pathway remains
intact and is the only connection through which input is available to the dissected
region. For photography, the cortex was lifted to demonstrate the underlying
white matter but the operative procedure, of course, is done in such a way as
to preserve the pial circulation to the cortical slab (38).

Fig. 20. A Weil stained cross section of the extracallosal isolation. The integrity
of the callosal pathway is well visualized (38).

hemisphere. Dependent secondary discharge does occur in such a
preparation as do electrically evoked trans-callosal potentials,
indicating that the undercut region is viable and the callosal
pathway intact. A Weil-stained section is shown in Figure 20.

216 Information Storage and Neural Control

It appears then that the enduring" changes in synaptic function
which form the basis of the independent mirror focus require that
at least two forms of input be available to the cortical region
concerned.

It seemed appropriate to inquire whether the change in excita-
bility or irritability of the mirror focus was dependent upon
impulses circulating in closed chains of neurones or whether it
was based upon structural alterations of cells within the network.
As a first step, neuronal isolation of the region of primary discharge
was carried out according to the technique of Kristiansen and
Courtois (33). Figure 21 A illustrates persistent, perhaps even
augmented activity, in the mirror focus after isolation of the pri-
mary lesion. There was cessation of paroxysmal discharge in the
isolated primary lesion. The mirror region was then similarly
isolated (Fig. 21 B and C). Some residual spiking sometimes
persisted for several minutes in the isolated mirror region (Fig. 2 IB)
but soon disappeared to be replaced by electrical silence (Fig. 21C).
After these isolations were performed the calvarium was replaced
and the animal returned to its cage for several months. Surface
recording during" that period indicated no return of paroxysmal
discharge. The lack of grossly recordable spontaneous paroxysmal
activity was associated with a corresponding absence of spon-
taneous unit discharge when at a later date, single cells of the
isolated epileptic zone were probed with microelectrodes. The
last two observations afford reasonably compelling proof that self-
re-exciting impulse chains do not persist after the isolation pro-
cedure. If the increased excitability characteristic of the epileptic
focus is dependent upon continuous self re-excitation, the isolation
procedure should abolish the abnormal excitability. A direct test of
this prediction was then undertaken.

The animals which had been subjected to complete neuronal
isolation of both primary and secondary epileptogenic regions
were prepared for an acute experiment. Several non-epileptic
animals had had a comparable isolated cortical slab prepared
at the same time as those in the epileptic group. In a third group
of animals neuronal isolation of normal cortical tissue in one
hemisphere was accomplished prior to the introduction of an
epileptogenic lesion in the opposite hemisphere at a point exactly

biformation Storage in Nerve Cells

217

s/Ia-u~^|vV''^'-^A'^'-^H^V^i

7-!

,/vw\-/V^wVV^'l^v^JV*^vv|v/

6-4

I -2

3-5

6-4

Fig. 21. Bilateral cortical isolation in an animal with well developed independent
mirror focus. This is the same animal shown in Figure 18. Recordings were
made with the animal under 40 milligrams per kilogram of nembutal anesthesia.
Derivations are from the electrodes indicated in the diagrams. Isolation of the
primary lesion is first carried out (21A) and demonstrates loss of paroxysmal
spike discharge in the primary region while the secondary area continues to
discharge actively. Isolation of the secondary region is then undertaken (21 B &
C). For a few moments abnormal discharge persists in the isolated secondary
region (B) but soon disappears (C) to be replaced by almost complete electrical
silence. Note that the electrode positions have been changed in B and C. Cali-
bration: 50 microvolts and one second (39).

218 Information Storage and Neural Control

contralateral to the center of the isolated slab. Once the epileptic
lesion had begun to discharge actively it too was isolated in the
same way. It was thus possible to compare the properties of neurally
isolated non-epileptic tissue in one hemisphere with similarly
isolated but epileptic tissue in a comparable region of the opposite
hemisphere in the same animal.

Although many different test situations were investigated, only
one will be discu.ssed at this time. Approximately three months
after the cortical isolations were made the animals were prepared
for an acute experiment. Wide exposure of both cerebral hemi-
spheres and a tracheotomy were performed under ether anesthesia
after which the ether was allowed to dissipate and the animals
were maintained under Flaxedil and artificial respiration. The
pial surface was covered with warm mineral oil or saline. Epilep-
tiform after-discharges were induced in the intact normal cortex
outside the isolated zones either by direct electrical stimulation
or by placement of small pledgets of filter paper soaked in Metrazol.
Propagation of these after-discharges was monitored by means of
recording electrodes distributed throughout the intact cortex and
within the isolated area. The extent to which high voltage dis-
charge originating externally spreads across the solution of neural
continuity to excite cells within the isolated zone is considered to
be a measure of the excitability of those cells.

In our experience it was rare indeed for paroxysmal discharge
to cross the neural gap and excite non-epileptic isolated cortex
(Fig. 22). As may be seen in Figure 22 this was true even when
the epileptiform activity was of extremely high voltage, long
duration, and spread quite readily to the opposite hemisphere.
On the other hand the isolated epileptic tissue of the mirror focus
was quite easily invaded by epileptiform activity arising ex-
ternally (Fig. 23).

In the experiment illustrated in Figure 23, tungsten micro-
electrodes having tip diameters of 1-5 micra were inserted to
a depth of 500-1000 micra into the isolated slab. Since a search
for spontaneously firing units was rarely successful it was necessary
to rely upon multiple placements at a depth where unit discharge
might reasonably be expected in connection with surface electro-
graphic paroxysms. Microelectrode recording was employed in

Information Storage in Nerve Cells

219

2-4,

7-M

Fig. 22. Failure of epileptiform after-discharge to invade non-epileptic neuronally
isolated region. Unanesthetized rabbit. Implanted electrodes at sites indicated
on diagram. Channel designations refer to the correspondingly numbered elec-
trodes and denote grid 1 and grid 2 respectively. Electrical stimulus had been
applied to cortical surface at site of electrode 4. The electrode wiUiin the isolated
region (7) is connected to a reference (M) sewed into the cervical muscles.
Calibration: 50 microvolts and one second.

Normol corlex (1-21

Normol cortex (2-3) ^

Isolated cortex (4-M)

Normol cortex

Microelectrode

Fig. 23. Propagation of epileptiform discharge into an isolated epileptic region.
Two examples with both surface and simultaneous microelectrode recording.
A pledget of filter-paper soaked in Metrazol was placed on normal cortex out-
side the isolated slab at 2 cm. distance. The electrographic discharge so induced
spread slowly across the cortex and after some delay invaded the isolated zone.
Single units within the slab were recorded through tungsten microwires having
a tip resistance of 10-40 megohms. Calibration: 50 microvolts and one second
for the ink-writer tracings and one second for the cathode ray oscilloscope (39).

220 Information Storage and Neural Control

order to avoid the ambiguity engendered when high amplitude
potentials arising externally are conducted in volume to the large
electrodes resting upon the surface of the isolated segment. Thus
in the first part of the tracings in the two experiments illustrated
in Figures 23A and B the large electrodes on the surface of isolated
cortex (Channel 3, Fig. 23A and Channel 2, Fig. 23B) record
potential variations precisely concordant in time with those in
the surrounding normal cortex where the seizure was initiated.
Not until seconds later did the microelectrode tracing reveal that
single elements within the slab had developed high frequency
self-sustained discharge. Since the high impedance of the micro-
electrode tip precludes recording at any distance, we cannot
escape the conclusion that nonsynaptic activation of ganglionic
elements within the isolated region had occurred.

Although only negative evidence can be presented for the case
of non-epileptic isolated cortex, the contrast between that and
the ease with which invasion of epileptic zones can be demon-
strated has led to the conclusion that abnormal excitability per-
sists in the secondary epileptogenic focus for several months after
an isolation procedure which eliminated a self-reexcitation mech-
anism. Presumably, therefore, the persistence of abnormal behavior
in these cells depends upon structural or biochemical alterations
rather than upon continuing electrical input.

On the basis of the reasoning discussed earlier the ribonucleic
acid distribution in the mirror focus was examined histochemically,
first with the methyl green pyronin method and subsequently
(with concordant results) with Azure B and Gallocyanin at
acid pH. After preliminary electrical studies had clearly indicated
the extent and distribution of both primary and secondary dis-
charging areas the animals were sacrificed and brains perfused
in situ. Serial sections were prepared and those from primary and
secondary foci were compared with those from electrically un-
involved areas of brain. Figure 24 demonstrates a small nest of
darkly stained cells in a section taken from the electrically defined
mirror focus. The border of the densely stained region is fairly
sharp, and to the left is the adjacent normal cortex, so that one
may compare the dye-binding property of normal cortical tissue
with that of the electrically abnormal zone on the right. A slightly

Information Storage in Nerve Cells 221

1 . ^

. i" ^'

Fig. 24. Sections Lhruugh ilic region ul ilit- uiinur lucus. Note the collection
of densely stained cells to the right of the photomicrograph compared with the
characteristic staining of normal cortex to the left. Methyl green pyronin stain.

Magnification x75 (39).

higher power photomicrograph (Fig. 25) illustrates the pene-
tration of the pyronin-positive material into the dendrite and
also indicates the wedgelike distribution of the stained cell system.
Pigmented cells extend throughout the depth of the cortex. At
still higher magnification the extent of penetration into the dendrite
is clearer (Fig. 26) and one may observe a concentration of the
pyronin-positive material in a dense layer along the inner surface
of the cell membrane. The altered tinctorial properties of these
cells were abolished by pre-treatment of the slide with ribonuclease
and were unaffected by similar treatment with deoxyribonuclease
and other enzymes. Although the histochemical picture was some-
what obscured by surgical artifacts the cells in the isolated mirror
focus exhibited the same pyronin-dense-pattern as had the intact
secondary region. Further controls may be found in the original
report (39).

222 Information Storage and Neural Control

~ f ^ ' 'y

Fig. 25. Slightly higher power photomicrograph through region of mirror focus.
The appearance of normal cortical cells with this method is seen in the lower
right and upper left hand corners. Methyl green pyronin stain. Magnifica-
tion x85 (39).

Interpretation of the histochemical results is still an entirely
open question. The evidence is not sufficient to conclude that the
alteration in RNA is specifically related to afferent bombardment
since, although the general areas coincide, there is no way to
know whether a given pyronin-dense cell has participated in the
epileptiform activity. Furthermore the nature of the nucleotide-
dye mole interrelation is still incompletely understood (52).
Increased staining with basic dyes does not necessarily indicate
an increase in absolute amount of RNA. It is also possible that
changes in polymerization and possibly submolecular factors
affecting charge distribution may influence dye-binding.

Despite many areas of uncertainty the bulk of experimental
evidence is consistent with the notion that except for certain
plant viruses the nucleotide sequence in RNA is specified by
genetic information in DNA. If the DNA-RNA specification

hiformation Storage in Nerve Cells 223

Fig. 26. Higher power photomicrograph demonstrating the characteristic con-
centration of pyronin-positive material along the inner surface of the membrane.
The stained material extends far into the dendrite. Note also the appearance of
a bilobed nucleus. Methyl green pyronin stain. Magnification x840 (39).

system were susceptible in a random way to ionic fluxes induced
by nerve impulses the ordinary metabolic machinery of the cell
would be rapidly undone. This could be avoided only if the nerve
cell represented a special case of uncoupling of the DNA-RNA
specification system, thus allowing a degree of freedom for the
nucleotide sequence in RNA to be influenced by environmental
factors. Or alternatively one might assume that only certain pre-
selected molecules are available to influence by ionic flux. One
may entertain the view that all possible RNA nucleotide sequences
and their correspondingly coded proteins are already available
within the cell. An incoming pattern of electrical impulses might
select or re-orient some of these molecules at the expense of others.
Availability of the stored information might be based upon a
cellular "recognition" of the same pattern of impulse impingement

224 Information Storage and Neural Control

or synaptic activation which established the original alteration.
Such "recognition" may be similar in mechanism (still known)
to those occurring in morphogenesis and in antigen-antibody
reactions. However, it is well to be aware that when we substitute
electric currents (whether or not generated by chemical trans-
mitters) for the "antigen" we enter a realm of biological phe-
nomena not based upon the classical chemistry of atoms and
molecules — one in which electron or charge transfer reactions
afford the more crucial energizing mechanisms. It is also apparent
that those who would consider a role for the nucleic acids in the
molecular basis of memory must also explain how an electrical
current could induce a molecular rearrangement which is there-
after irreversible and immune to further perturbations of its
electrical surround. Perhaps the binding of an appropriately
modified RNA protein complex to phospholipid would not only
protect it from further electrical influence but also fix it to the
cell membrane where the function of "recognition" is most likely
to take place.

Finally I should like to return to the beginning and add one
more note of complexity to an already complex story. We have
mentioned the retrograde amnesia produced by a cerebral con-
cussion. Clinical experience gives clear evidence that immediately
following an injury the memory loss may extend backwards in
time for weeks, months, or even years, so that the patient reports
his age as several years younger than is actually the case. During
recovery the memory gap decreases gradually with recall of more
distant events first and recent events last. Russell and Nathan,
in an extensive review (50), have emphasized that the pattern of
recovery shows no relationship to the importance of the events
remembered. Thus one patient remained amnesic for his marriage,
which had occurred three weeks prior to the injury, but recalled
perfectly reading a trivial newspaper story six weeks earlier. It is
clear that memory returns not in order of importance but only
in order of time. To be sure, even under the best of circumstances
recovery is never complete; it is almost always possible to demon-
strate a complete and permanent loss of memory for the events
immediately preceding an injury. Perhaps it is this last, brief,

Injormation Storage in Nerve Cells 225

blank interval which is relevant to the electrical aspects of the
memory mechanism discussed earlier. Yet the total recovery pat-
tern in retrograde amnesia stresses the lability and vulnerability
of the most recently acquired experience and suggests that it
is not only the electrical or short-term aspects of memory which
consolidate; some form of consolidation must also occur in the
structural or "permanent" stage of information storage (12).

A molecular mechanism for information storage must embrace
all these features. As a provisional target we might envision a
molecular species which may be altered by ionic flux but once
altered is immune to other electrical interventions, which has
nothing to do with basic metabolic processes, which can replicate
itself within a cell and which can alter the output of that cell so
as to disseminate its "spoor" to the next cell along the pathway.
But to envision is not to identify. The target promises to be elusive.
The analogy of the inirror focus may be rough indeed but it is
pertinent to recall that the alterations observed in electrical and
chemical properties are brought about through the same neural
pathways available to physiological stimulations. No quantitative
relationship between these data and the events responsible for
behavior is implied. Perhaps there is no relationship at all. Never-
theless, used as an experimental tool this model and the observa-
tions it has yielded so far indicate that we have in hand, to see
and to investigate, clear-cut and permanent changes in cellular
and synaptic properties related to the past history of that cell
or synapse.

ACKNOWLEDGMENTS

These studies were supported by U.S.P.H.S. grant B-3543. I
wish to express my gratitude to the many individuals who have
helped in various aspects of these investigations. Special appre-
ciation is due to Dr. K. L. Clhow and Mr. Paul Naitoh for help
in some of the experiments and to Professor Lincoln Moses for the
statistical analysis. Gratitude is hardly the word to express the
indebtedness to my wife, Dr. Lenore Morrell, whose forebearance
with dinners grown cold and evenings in the laboratory made
this work possible.

226 Information Storage and Neural Control

REFERENCES

1. Abt, J. P., Essman, VV. B. and Jarvik, M. E.: Ether-induced retro-

grade amnesia for one-trial conditioning in mice. Science, 733:
1477-1478, 1961.

2. Arduini, A.: Enduring potential changes evoked in the cerebral

cortex by stimulation of brain stem reticular formation and
thalamus. In: Reticular Formation of the Brain, edited by, Jasper,
H. H., Proctor, L. D., Knighton, R. S., Noshay, W. C, and
Costello, R. T. Boston, Little, Brown and Co., 1958, 333 pp.

3. Bishop, G. H., and O'Leary, J. L.: The effect of polarizing currents

on cell potentials and their significance in the interpretation of
central nervous system activity. Electroenceph. Clin. Neurophysiol., 2:
401-416, 1950.

4. Brattgard, S.: The importance of adequate stimulation for the

chemical composition of retinal ganglion cells during early post-
natal development. Acta Radiologica Supp. 96., 1952.

5. Brattgard, S. and Hyden, H.: Mass, lipids, pentose nucleoproteins

and proteins determined in nerve cells by x-ray microradiography.
Acta Radiologica Supp. 94, 1952.

6. Brazier, M. A. B.: Long-persisting electrical traces in the brain of

man and their possible relationship to higher nervous activity.
In: The Moscow Colloquium on Electroencephalography of Higher
Nervous Activity, edited Ijy Jasper, H. H. and Smirnov, G. D.
Electroenceph. Clin. NeurophysioL, Supp. 13, 1960.

7. Burns, B. D.: Some properties of the cat's isolated cerebral cortex.

J. Physiol., 777.-50-68, 1951.

8. Burns, B. D.: The production of after-burts in isolated unanesthetized

cerebral cortex. J. Physiol., 725.- 427-446, 1954.

9. Burns, B. D.: The mechanism of after-bursts in cerebral cortex. J.

Physiol., 727.- 168-1 88, 1955.

10. Chow, K. L. and Dewson, James: Unpublished data.

11. Chow, K. L.: Brain Functions. Ann. Rev. Psychol., 72.-28 1-310, 1961.

12. Deutsch, J. A.: Higher nervous function: the physiological bases of

memory. Ann. Rev. Physiol., 24:259-286, 1962.

13. Duncan, C. P.: The retroactive effect of electroshock on learning.

J. Camp. Physiol. Psychol., 42:32-44, 1949.

14. Gerard, R. W.: The fixation of experience. In: Cioms Symposium on

Brain Mechanisms and Learning, edited by Delafresnaye, J. F.,
Fessard, A., and Konorski, J. Oxford, Blackwell Scientific Pub.,
1961.

Information Storage in Nerve Cells 227

15. Gerard, R. W. and Libet, B.: The control of normal and convulsive

brain potentials. Amer. J. Psychiat.. 96.- 1 125-1 152, 1940.

16. Goldring, S., and O'Leary, J. L.: Experimentally derived correlates

between EGG and steady cortical potential. J. Neurophysiol., 14:
275-288, 1951.

17. Goldring, S., and O'Leary, J. L.: Summation of certain enduring

sequelae of cortical activation in the rabbit. Electroenceph. Clin.
Neurophysiol., J.-329-340, 1951.

18. Goldring, S. and OTeary, J. L.: Cortical D.C. changes incident to

midline thalamic stimulation. Electroenceph. Clin. Neurophysiol., 9:
577, 1957.

19. Gumnit, R. J.: The distribution of direct current responses evoked

by sounds in the auditory cortex of the cat. Electroenceph. Clin.
Neurophysiol., 13:889-895, 1961.

20. Hamberger, C. A.: Gytochemical investigations on N. vestibularis.

Acta Otolaryng. Supp., 78:53-62, 1949.

21. Hamberger, C. A. and Hyden, H.: Production of nucleoproteins in

the vestibular ganglion. Acta Otolaryng. Supp. 75:53, 1949.

22. Hamberger, C. A. and Hyden, H.: Transneuronal chemical changes

in Deiter's nucleus. Acta Otolaryng. Supp. 75:82, 1949.

23. Hammer, G.: On quantitative cytochemical changes in spiral gang-

lion cells after acoustic trauma. Acta Otolaryng. Supp. 740.T37-144,
1958.

24. Hebb, D. O.: The Organization of Behavior. New York, Wiley, 1949.

25. Hyden, H.: Determination of mass of nerve-cell components. J.

Embryol. Exp. Morph., 7;315-317, 1953.

26. Hyden, H.: Biochemical changes in glial cells and nerve cells at

varying activity. In: Proc. Fourth International Congress of Bio-
chemistry. Vol. Ill Biochemistry of the Central Nervous System. London,
Pergamon Press, 1960.

27. Hyden, H. and Pigon, A.: A cytophysiological study of the functional

relationship between oligodendroglial cells and nerve cells of
Deiter's nucleus. J. Neurochem., 6:57-12, 1960.

28. lizuka, R., Takeda, T., Tanabe, M., Takahata, N. and Suwa, N.:

Histochemical studies on the nucleic acid and thiamin of nerve
cells under electroconvulsion, insulin coma and malononitrile
injection. Folia Psychiat. Neurol. Jap., 13:\-\A, 1959.

29. John, E. R.: High nervous functions (Brain functions and learning).

Ann. Rev. Physiol, 2J;451-484, 1961.

30. Katz, J. J. and Halstead, W. C: Protein organization and mental

function. Comp. Psychol. Monog., 20:\-58, 1950.

228 Infort?iation Storage and Neural Control

31. Kraft, M. S., Oljrist, W. D. and Pribram, K. H.: The effect of

irritative lesions of the striate cortex on learning of visual dis-
criminations in monkeys. J. Cornp. Physiol. Psychol., 53:\7-22, 1960.

32. Kreps, E.: Cited in the use of radioactive isotopes in the study of

functional biochemistry of the brain by Palladin, A. V. and
Vladimirov, G. E. Proc. International Conference on the Peaceful
Uses of Atomic Energy. Vol. 12, United Nations, New York, 1955.

33. Kristiansen, K. and Courtois, G.: Rhythmic electric activity from

isolated cerebral cortex. Electroenceph. Clin. JVeurophysiol., 7;265-272,
1949.

34. Morrell, F.: Interseizure disturbances in focal epilepsy. Neurology,

(5; 327-334, 1956.

35. Morrell, F.: Effects of experimental epilepsy on conditioned elec-

trical potentials. Univ. oj Minn. Med. Bu'l., 29:82-102, 1957.

36. Morrell, F.: Experimental focal epilepsy in animals. AM A Arch.

Neurol., 7:141-147, 1959.

37. Morrell, F.: Microelectrode and steady potential studies suggesting

a dendritic locus of closure. In: The Moscow Colloquium on
Electroencephalography of Higher Nervous Activity, edited by
Jasper, H. H. and Smirnov, G. D. Electroenceph. Clin. Neurophysiol.,
Supp. 13, 1960.

38. Morrell, F.: Secondary epileptogenic lesions. Epilepsia 7;538-560,

1960.

39. Morrell, F.: Lasting changes in synaptic organization produced by

continuous neuronal bombardment. In CIOMS Symposium on Brain
Mechanisms and Learning, edited by Delafresnaye, J. F., Fessard, A.,
Konorski, J. Oxford, Blackwell Scientif. Pub., 1961.

40. Morrell, F.: Effect of anodal polarization on the firing pattern of

single cortical cells. Ann. New Tor k Acad, of Sci., 92:860-87 6, 1961.

41. Morrell, F.: Electrophysiological contributions to the neural basis

of learning. Physiol. Rev., 47.-443-494, 1961.

42. Morrell, F., Roberts, L. and Jasper, H.: Effect of focal epileptogenic

lesions and their ablation upon conditioned electrical responses of
the brain in the monkey. Electroenceph. Clin. Neurophysiol., 8:2X1-
236, 1956.

43. Morrell, F., Sandler, B. and Ross, G.: The "Mirror Focus" as a

Model of Neural Learning. Proc. XXI Internat. Physiol. Cong.,
Buenos Aires, 1959, 193 pp.

44. Ransmeier, R. E. and Gerard, R. W. : Effects of temperature, con-

vulsion and metabolic factors on rodent memory and EEG. Amer.
J. Physiol., 779;663-664, 1954.

Information Storage in Nerve Cells 229

45. Roitbak, A. I.: Bioelectric Phenomena in the Cortex of the Cerebral

Hemispheres. Part 1 Acad. Sci. Georgian SSR, Tiflis, 1955.

46. Roitbak, A. I.: Electrical phenomena in the cerebral corte-x during

the extinction of orientation and conditioned reflexes. In: The
Mo.scow Colloquium on Electroencephalography of Higher Ner-
vous Activity, edited by Jasper, H. H., and Smirnov, G. D. Elec-
troenceph. Clin. NeurophysioL, Supp. 13, 1960.

47. Rowland, V.: Second Conference on Brain and Behavior, edited

by Brazier, M. A. B. In press.

48. Rusinov, V. S.: An electrophysiological analysis of the connecting

function in the cerebral cortex in the presence of a dominant
area. Communications at the XIX International Physiological
Congress., Montreal, 1953.

49. Rusinov, V. S.: General and localized alterations in the electro-

encephalogram during the formation of conditioned reflexes in
man. In: The Moscow Colloquium on Electroencephalography of
Higher Nervous Activity, edited by Jasper, H. H. and Smirnov,
G. D. Electroenceph. Clin. NeurophysioL, Supp. 13, 1960.

50. Russell, W. R. and Nathan, P. W.: Traumatic Amnesia. Brain, 69:

280-300, 1946.

51. Stamm, J. S. and Pribram, K. H.: EfTects of epileptogenic lesions

in frontal cortex on learning and retention in monkeys. J . Neuro-
physioL, 2J;552-563, 1960.

52. Steiner, R. F. and Beers, R. F., Jr.: Polynucleotides. Natural and

Synthetic Nucleic Acids. Amsterdam, Elsevier Pub. Co., 1961.

53. Thompson, R.: The comparative effects of ECS and anoxia on

memory. J. Comp. Physiol. Psychol., 50.-397-400, 1957.

54. Thompson, R. and Dean, W.: A further study on the retroactive

eflfect of ECS. J. Comp. Physiol. Psychol., 45.-488-491, 1955.

55. Tobias, Julian M.: Experimentally altered structure related to

function in the lobster axon with an extrapolation to molecular
mechanisms in excitation. J. Cell. Comp. Physiol., 52.-89-126, 1958.

56. Vladimirov, G. E.: The influence of excitation of the central nervous

system on some aspects of metabolism in the cerebral hemispheres
of animals. Proc. XIX Internat. Physiol. Cong. Montreal, pp.
856-857, 1953.

CHAPTER
X

HOW CAN MODELS FROM INFORMATION
THEORY BE USED IN NEUROPHYSIOLOGY?*

Mary A. B. Brazier

HY is it that information theory has had such an attraction for
neurophysiologists? From the earliest dissemination among scien-
tists of Shannon's information theory (9), developed in the context
of communications technology, and of Wiener's communication
theory (14) which expanded its frontiers, neurophysiologists have
been prominent among those who wished to explore the potenti-
alities for their field.

There were many reasons for this, but I would suggest that there
were three major ones, namely:

1) de-emphasis on energy-coupling within systems and emphasis
on informational coupling;

2) the formulation of models for dealing with signals-in-noise;
and

3) the exploration of piobabilistic models rather than determin-
istic ones.

These are, of course, all interrelated. I would like to discuss the
first two items briefly, together with some other facets of information
theory that impinge on neurophysiology, and then give more
detailed attention to the subject of probabilistic models of nervous
system activity.

*The work reported here was supported by USPHS Grant NB-03160 and Contract
Nonr 233(69) from the Office of Naval Research.

230

How Can Models Jrom Information Theory he Used in Neurophysiology)? 231

Let us look first at the difference between energy-based concepts
and information-based concepts. All down the ages the nerves have
been recognized as message-carriers and as late as the last century
the most distinguished physiologist of the time, Johannes Miiller,
was using the term "nerve energy.'' "We are," he wrote, "com-
pelled to ascribe, with Aristotle, peculiar energies to each nerve,
energies which are vital qualities of the nerve." Even the later 19th
century neurophysiology, dominated by Du Bois Reymond, was
primarily focussed on the concept of the conservation of energy.

You will remember that it was because of his adherence to
energy concepts that Sherrington (10) found himself unable to
envisage a physiological basis for mental processes. In Man on His
Nature he wrote:

"No attributes of 'energy' seem findable in the process of mind.
That absence hampers explanation of the tie between cerebral and
mental."

He goes on to write of the brain being "a physiological entity held
together by energy-relations" and expresses his despair of being
able to correlate such a physiological entity with a mental experi-
ence. "The two for all I can do," he wrote "remain refractorily
apart. They seem to me disparate; not mutually convertible; un-
translatable the one into the other."

Coming to our own times, we have seen a great deal of investiga-
tive effort go into a search for energy correlates of brain function.
An example is the search for a metabolic change underlying the
sleep state. Anesthesiology is another field in which one finds many
studies centering around alterations of brain metabolism as the
major factor of importance in the changing levels of consciousness.

It is only with recent years that we find attention being diverted
from the question "What is the level of activity in the brain as a
whole?" to "Which system within the brain is now dominantly
active?" The latter question contains the implication that it is a
re-routing of nerve impulses, a change in the informational coupling
rather than in the general metabolic level of the brain's activity
that may yield the clue to functional changes. In order to effect a
coupling of parts within the nervous system there does not have
to be a great interchange of energy— only the infinitesimal transfer
concomitant with the passage of the nerve impulse.

232 Information Storage and Neural Control

Taking" the examples just given, neurophysiologists are finding
closer correlates with the states of sleep and anesthesia from studies
of the coupling between the cortex, the thalamus and the brain
stem than they have found in their measurements of energy-transfer
reflected in arterio-venous differences between carotid and jugular
blood. In the limbic system there is now evidence for re-routing
taking place during the learning process in animals being trained
in a T-box (1). Many other examples could be quoted.

The second attraction that I mentioned was the way information
theory handles the problem of signals-in-noise; but here, neuro-
physiologists generally use this term in the vernacular rather than
in its critically defined sense. This is because we do not usually
apply the criteria for randomness when speaking of biological noise.
As a matter of fact, many use the term 'noise' in quite the opposite
sense from that defined by mathematical theory. In the neuro-
physiological journals we frequently find 'noise' used to describe
disorderly, unpredictable activity in which no regularity can be
detected.

On the other hand, the mathematical approach (5, 11) has a
very clear-cut set of criteria for random processes — criteria based on
probability distributions that effectively result in statistical regu-
larity, statistical orderliness and statistical predictability.

The whole gamut of criteria for a mathematical model of random
processes would be very difficult to apply to the nervous system, but
already some consideration has been given to this problem (6).
The probability functions that have seemed to be the least difficult
to carry from the mathematical model into the 'real' nervous
system have been those of means, spectra and correlation functions.
These comparatively simple factors bring us only to a limited and
fractional descriptive usefulness, and hence an increasing number
of neurophysiologists are exploring this approach.

The statistical regularity of a random process bears considerable
interest for the neurophysiologist because of his familiarity with
the concept of the statistically steady state that has earned itself the
name of homeostasis. The fact that the brain, in its evolution, has
reached a stage in man where the neuronal mechanisms for homeo-
static control of his milieu interieur are handled by his medullary
brain stem, frees the cortex from these concerns and reserves it for

How Can Models from Injormation Theory be Used in Neurophysiology? 233

higher functions, thus giving man what Claude Bernard, in his
famous phrase, called "la condition de la vie libre."

This concept of a statistically regular, predictable randomness
of 'noise' against which the neurophysiologist emphasizes his
'signal' when averaging by computers, has a close relationship to
one of the most basic principles of information theory. This prin-
ciple is that information is carried by departure from orderliness or,
in other words, by departure from the predictable. Even the
intuitive concept of information is a change from what you already
know and can predict.

Several neurophysiologists have now invoked this principle to
explain such phenomena as "attention" and "habituation" and
"the orienting reflex," together with their attendant electrical
concomitants. One such example is the model proposed by Sokolov,
(12) which envisages novelty, i.e., departure from the statistically
expected state, as being the factor that evokes activity in the brain
stem and the resultant orienting reflex. The concept of "attention"
being related to matching the probability of a neuronal event
against the expected distribution of possible events, will be found
in the work of many neurophysiologists. *

This brings us to the third major attraction of information theory
for the neurophysiologist: the use of a probabilistic model for the
nervous system rather than a deterministic one. I would like to
approach this from the neurophysiologist's angle.

I have spoken earlier about Johannes Miiller and you will re-
member his famous "Law of Specific Nerve Energies" by which
each of the myriad facets of sensation was assigned its special nerve
— how uneconomical, but how simple. A single output would be
obtained from a single input. Nothing could be more deterministic.
One could design no simpler code. But it was too good to be true.

In the earlier part of this century, the belief in a ubiquitous
all-or-nothing law for the nervous system and the demonstrations of
the coding of intensity by frequency of discharge in single fibers
of the peripheral nervous system, led eventually to exploration of
single cell discharges within the brain itself.

Iminediately, it became clear that coding was no simple problem.
Miiller would have been chagrined to see how many difl'erent

"For early examples see references (2) and (7).

234 Information Storage and Neural Control

peripheral loci could fire an individual cell in the brain. Many
examples drawn from the somatic and other sensory systems have
been published, but Miiller would have been even more dismayed
had he been shown that there are cells within the brain that are
no respecters of sense modalities. Proof has been given of con-
vergence of sensory modalities onto individual neurons of the
midbrain and thalamic brain stem and onto units in the limbic
system. Even cortical neurons are not simon-pure.

The complexities do not cease there, for such coding as can be
established for individual presynaptic fibers is found to be trans-
formed at the synapse and to send on a different pattern of discharge
in the postsynaptic output. Some of the exquisite response patterns
that investigators have been able to identify in the primary neurons
from the receptors are therefore only one link in a chain of se-
quential codes. Moreover, the evidence that recoding in post-
synaptic fibers varies in different neuronal aggregates is over-
whelming, for there is great variation in the degree of convergence
and divergence.

The biologist has long known how rare is a one-to-one relation-
ship between input and output of a synapse but is now beginning
to realize that the relationships may not even be linear. He may
well have to wait for the mathematicians to progress further with
their analyses of nonlinear systems before he himself can master the
transformation characteristics of the code as it passes seriatim
through a chain of synaptic relays.

As an example of changing code, the findings of Whitfield (13) in
the auditory system may be quoted. Whitfield found that the rate
of firing becomes progressively less as the impulses proceed through
the serial synaptic stations on their way to the cortex. Moreover,
the rate of unit firing becomes less and less dependent on the
strength of the stimulus at each successive relay station. In other
words, the intensity of the stimulus is no longer being signalled
simply by frequency of discharge. The coding has changed and
some clues to its nature are already known. These point to the
distribution of excitation and inhibition among the fibers of the
pathways as being a crucial factor.

Although in this outline which poses the problem, all the facets
that must enter into any consideration of neural coding cannot be

How Can Models from Information Theory be Used in Neurophysiology? 235

enumerated, the phenomenon of lateral interaction among mem-
bers of a neuronal population should not escape attention. This is
seen, perhaps most strikingly, in the zone of inhibition that develops
around a locus of excitation. The "inhibitory surround" has now
been demonstrated for the visual, auditory, and somatic afferent
systems and emphasizes complex patterns of interaction rather
than conduction over isolated paths. There is also the interference
with direct routing of impulses from the receptor to the cortex,
mediated by recurrent collaterals and centrifugal feed-back control
over afferent pathways. Of two more contributions to knowledge
which have added to the neurophysiologist's task, one is the
realization that the all-or-nothing discharge is a comparatively rare
event in the central nervous system, graded responses (which may
or may not lead to cell discharges) being all important. Wliat of
these graded changes? How do they affect the code?

One aspect of the problem has been approached by the analysis
of the intervals between nerve discharges. Although the action
potential of an axon is all-or-nothing and hence digital, the graded,
analog character of the receptor's action can be preserved in the
code by the intervals between discharges, for intervals between
spikes are continuously variable and therefore can transmit graded
input. In fact, a great deal of work in many laboratories is cur-
rently being devoted to pulse-interval analysis of the message set up
by stimulation of receptors.

More difficult is the problem of graded delivery of the inessage
at the higher cerebral level where its result may be effector cell
discharge, passage into association areas, passage into storage
neurons changing their cellular function, modulation of other
currently incoming messages, or dissipation of a type about which
we have, as yet, no knowledge. Graded responses in dendrites do
not necessarily induce discharges of their cell bodies; nevertheless,
their influence as modulators inay be critical for the "meaning'' of
the message.

Last, but not least in importance, is the evidence for a great
deal of endogenous discharge of many neurons of the central
nervous system in the absence of overt external stimuli. How is the
brain to select those discharges that are evoked by messages initiated
in its environment from those that form its background activity?

236 Information Storage and Neural Control

If this selection can be made, on what grounds is a resukant efferent
discharge determined?

No one yet knows what the mechanism effecting these decisions
may be, but it has occurred to many that discriminations may be
made by the brain on a statistical basis, i.e., on the probability that
the afferent patterns are significantly different from those which
are currently taking place in the brain or which its past experience
has set its neurons to "expect" by a change in their cellular
function.

This statistical viewpoint may be defined as the "probabilistic"
model in contrast to a "deterministic" one in which a given stimulus
elicits a stereotyped response irrespective of the likelihood of its
occurrence.

The probabilistic approach recognizes the need for the brain to
assign iinportance to those signals which require effector action
and suggests that this assay of importance must be on a basis of the
probability of the signal not being a chance variation. With all
the on-going discharges of cerebral neurons that workers with
microelectrodes have so convincingly demonstrated, some pro-
cedure must surely take place before a 'meaningful' signal can be
selected from this incessant activity.

No assessment of probability can be made without averaging.
Therefore, those who have begun to explore a statistical model for
coding in the nervous system have turned to techniques for averag-
ing neuroelectric activity over the passage of time as well as over
space as represented by neuronal aggregates. To aid in this task
many have adopted a prosthesis Just as the microanatomist has
adopted the microscope as a prosthesis to enrich his visual ability,
so has the neurophysiologist begun to use the computer as a pros-
thesis for his calculating ability.

The statistical characteristics of spontaneously discharging
neurons must be known to the brain before it can react appropri-
ately to an unexpected, meaningful signal requiring action. One
might even speculate that the nonresponding but spontaneously
discharging neurons that so many observers have found with their
microelectrodes, are "comparison" generators and the responding
neurons "information" generators. If this were so, only when the
normally expected difference between the two categories of genera-

How Can Models from InJormatio7i Theory be Used in Neurophysiology? 237

tor activity was exceeded by a statistically significant amount
would the incoming" signal be meaningful.

To analyze the myriad complexities of the brain's function by
nonstatistical description of unit discharges is too gigantic a task
to be conceived, but exploration in terms of probability theory is
both practical and rational.

In characterizing nervous activity, therefore, one would not
attempt the precise definition that arithmetic demands but would
seek the statistical characteristics of the phenomena that appear
to be relevant. The margin of safety that the brain has for appropri-
ate reaction is thus much greater than a deterministic, arithmetic-
ally precise operation would impose. Chaos would result from the
least slip-up of the latter, whereas only a major divergence from
the mean would disturb a system working on a probabilistic basis.
The rigidity of arithmetic is not for the brain, and a search for a
deterministic code based on arithmetical precision is surely doomed
to disappointment.

Turning now to the scanty data which are all that today's neuro-
physiologist has as yet. In terms of actual data culled from the brain
I propose to mention only two categories here:

1 ) The averaging, over time, of intervals between unit discharges
in the brain;

2) The averaging, over time and space, of activity in neuronal
aggregates.

An example of averaging units over time, the first category, is
the work of Mountcastle (8) in which he has been designing experi-
ments to test the hypothesis that an intracortical mechanism
exists which integrates frequency over short periods of time and
responds only when intervals of sufficient brevity occur. These
experiments have revealed a striking change in pulse-interval
distribution in circumstances that give support to this hypothesis.

This investigation is alluded to so briefly at this time because
it is being quoted solely as an example of the first category of
statistical approach, i.e., averaging over time only. But the central
nervous system must have some mechanism for dealing with
multiple complex inflow, and it would seem more profitable to
expand this approach to the second category of study that I men-

238 Information Storage and Neural Control

tioned; namely, averaging" not only over time but over neural
aggregates, in order to get the profile of a population of neurons.
This is of particular importance in the brain because of the demon-
strated interaction of units within populations. This second ap-
proach necessitates the use of electrodes large enough to record
from populations of neurons and thus able to average over space
as well as time.

I will illustrate this approach by brief mention of some examples
drawn from our laboratory. Suppose we take the response of an
unanesthetized cat to a flash of light that is repeated monoton-
ously without any change in timing, or intensity, or in any other
of its parameters.

At the beginning of a train of such stimuli, the message the brain
will receive will contain at least three major components;

1) the stimulus is visual,

2) the stimulus is repetitive,

3) the stimulus is novel.

On prolonged repetition, however, the third of these messages
(that the stimulus is novel) is no longer being sent. The probability
of its arrival is now very high.

If the hypothesis is to be regarded as tenable, one of the tests
the neurophysiologist must make is a demonstration that the
response of the brain to a novel stimulus is difTerent on the average
from its reponse to a familiar one.

What would be demanded by the hypothesis under discussion?
Averages of a sample of responses late in the series would be ex-
pected to carry two of the same components of the message as are
carried by the first set of flashes; namely, that the stimulus is visual
and that it is repetitive, but the third component, i.e., that the
stimulus is novel, would need some change of signal.

When the responses to a repetitive train of flashes are recorded
from the visual cortex of an unanesthetized cat with permanently
implanted electrodes, one finds that the short latency responses
that have been identified with transmission in the specific aflferent
systems persist for the whole duration of the train. They apparently
carry the first two components of the message (that the stimulus is
visual and that it is being repeated).

How Can Adodels from Information Theory be Used in Neurophysiology? 239

At the beginning of such a train of flashes there are also long
latency responses in the visual cortex. These have been shown to
reach the cortex by the nonspecific afferent systems of the midline
brain stem and thalamus. It would seem possible that tlie third
major component of the message, the one signalling 'novelty' in
the stimulus, may be carried by these nonspecific afferents, for as
repetition continues, this sequence of later waves fades out. Averag-
ing of the first sixty to arrive, then the second sixty, the third sixty,
and so on, shows this late component of the multiple response to
be dropping out as the novelty wears off.

The effect can be fractionated even farther in the nonspecific
system by actually recording in a nucleus of this midline nonspecific
system (the centre mechan) where one of the most prominent of its
average electrical responses to flash (the late wave) can be seen to
fail with repetition of the unchanging stimulus, while the earlier
components persist.

The serial change in the late component of the multiple response
is very marked. Whatever the mechanism for this depressed respon-
siveness may prove to be, it is tempting to propose that this forms
part of the mechanism that conveys presence or absence of novelty.

This work has been described in detail and illustrated elsewhere,
(3, 4) so it will not be given more space here. However, lest these
examples appear to suggest too simple a picture of the brain's
message-receiving systems, let me add that not only does one find
presence or absence of a component of the response, as novelty
wears off, as in the foregoing sample, but one also finds situations
in which the time-relationships of the components of the brain's
electrical responses may change. Possibly it is in the time domain
that the neurophysiologist will find the most clues for the solution
of this problem.

I make only a brief allusion here to the laboratory work. This
is not intended as the report of a research, but as an example of
work carried out with a probabilistic model in mind, and to illus-
trate the point that the response probabilities of the nervous system
are influenced by the past events it has experienced.

Returning now to the more general topic of the utilization by
neurophysiology of information theory, let us not forget that one
of the innovations of information theory was the axiom that in-

240 Information Storage and Neural Control

formation is measurable, and tliat, in fact, Shannon in his classical
paper gave a precise mathematical definition of information. It is
so difficult to define information measures for ensembles in biology
that most biologists who use information theory usually do not
attempt to do so in a quantitative way. Generally, they do not
actually measure the information; and hence, they fail to exploit
the full potentialities of the theory. Yet many feel that someday,
somehow, more exactly defined information measures may be
brought into neurophysiology. I need only mention as an example
Shannon's formulation of the problem of channel capacity and his
solution for dealing with equivocation. C^hannel capacity is surely
a basic factor in the communication functions of the nervous system.

It is so tempting to think of information transfer in the brain
as being simply a matter of transniission in specific nerve tracts.
If this simple-minded concept could for one moment be defended,
one would then begin to study such communication channels in
terms of the finite set of signals that can be initiated in the
channel, the set of signals that arrives and the probability of the
reception of any given signal. If only the brain worked like a simple
telegraph system we would immediately be able to make precise
statements about such things as channel noise and would be able
to calculate channel capacity.

In contrast, all the work that the neurophysiologists have
pursued has revealed to us the enormity of interaction within the
brain — the correlations, couplings, linkages, and statistically inter-
dependent elements that contribute to its organization and make
any measurement of its interacting ensembles or any mathematical
statement of its entropy conditions formidable in the extreme.

In closing, let me say that the application of quantitative in-
formation theory to neurophysiology lies largely in the future.
Possibly a partial answer to the question in the title of this paper
is that if information theory has not led to the uncovering of many
new facts in neurophysiology, it may have led to many new ideas.

REFERENCES

1. Adey, W. R., Dunlop, C. W., Hendrix, C. E.: Hippocampal slow
waves; distribution and phase relations in the course of approach
learning. AM A Arch. Neurol., 3.-74-90, 1960.

How Can Models from Information Theory be Used in Neurophysiology? 241

2. Bates, J. A. V.: Significance of information theory to neui-ophysiology.

In: Information Theory Symposium. London, 1950, p. 137.

3. Bi'azier, M. A. B.: Responses in non-specific systems as studied by

averaging teclmiques. In: Specific and Unspecific Mechanisms of
Sensory-Motor Integration, Ed. G. Moruzzi (in press).

4. Brazier, M. A. B.: Information carrying characteristics of brain

responses. In: The Physiological Basis of Mental Activity, Ed. R.
Hernandez-Peon (in press).

5. Davenport, W. F., Root, W. L.: An Introduction to the Theory of Ran-

dom Signals and Noise. New York, McGraw-Hill, 1958.

6. Goldstein, M. H.: Averaging techniques applied to evoked responses.

In: Computer Techniques in EEC Analysis, Ed. M. A. B. Brazier,
EEG. Clin. Neurophysiol., Supp. 20, 1962, p. 59.

7. Grey Walter, W.: In: Brain Mechanisms and Consciousness, Ed. J. F.

Delafresnaye, Oxford, Blackwell Scientific Publications, 1954,
p. 372.

8. Mountcastle, V. B.: Duality of function in the somatic aflferent

system. In: Brain and Behavior. Ed. M. A. B. Brazier, Washington,
D. C., American Institute of Biological Sciences, 1961, p. 67.

9. Shannon, C. W.: A mathematical theory of communication. Bell

System Tech. J., 27.-379-424; 623-657, 1948.

10. Sherrington, C. S.: Man on His Nature. London, Cambridge University

Press, 1951.

11. Siebert, W. M.: The description of random processes. In: Processing

of Neuroelectric Data, Tech. Report 351, Communications Bio-
physics, RLE MIT, 1959, p. 66.

12. Sokolov, E. N.: Neuronal model and the orienting reflex. In: Central

.Kervous System and Behavior, Ed. M. A. B. Brazier, New York,
Josiah Macy, Jr. Foundation, 1960, p. 187.

13. Whitfield, I. C: The physiology of hearing. In: Progress in Biophysics

and Biophysical Chemistry 8:\-Al , 1957.

14. Wiener, N.: Cybernetics, New York, John Wiley, 1948.

DISCUSSION OF CHAPTER X

Harold W. Shipton (Iowa City, Iowa): In the averaged
record that you showed, were the stimuli being delivered ran-
domly, or were they as regular as you could make diem? Would
you care to comment on whether the time characteristics of the
external drive signal appear to you to be important in the con-
struction of the model?

242 Information Storage and Neural Control

Mary A. B. Brazier (Los Angeles, California) : They were
certainly not absolutely random. On the contrary, the intervals
between stimuli were as constant as we could make tliem. In an
experiment such as tliis, there is very great difficulty for the
neurophysiologist because the responses depend so much on the
state of the animal. Although one would like to have a longer
interval between stimuli, it is, in my experience, almost impossible
to hold an animal in the same stage of the sleep-wakefulness
continuum for as many stimuli as we use, if the interval between
flashes is longer than one second.

L. M. N. Bach (New Orleans, Louisiana) : I am curious about
the disappearance of the second component in tlie centrum
medianum response with repetitive stimulation as a possible
inverted index of post-tetanic potentiation. Do you consider it
a testable proposition that the disappearance of the second com-
ponent could be correlated with post-tetanic potentiation, or do
you consider tliat there is no relationship at all?

Brazier: It should be testable, but it is rather difficult to design
an experiment in wliich to test this.

Gregory Bateson (Palo Alto, California): Would you have
expected the part of the signal which denotes novelty to follow
the other two components? Would it not have been a better
arrangement to have the system, when it had diagnosed novelty,
transmit the information ahead of the other components of the
signal?

Brazier: I had no "expectation," tliough now that you raise
the question, would you not expect the brain to need to receive
the signal before it could assess its novelty? What you have sug-
gested would make a very good design for a communication
system, although the nervous system does not appear to be de-
signed in this manner.

CHAPTER
XI

NEURAL MECHANISMS OF
DECISION MAKING*

E. Roy John, Ph.D.**
GENERAL CONSIDERATIONS ABOUT MEMORY

HIS paper is largely concerned with the rather specialized
decision-making involved when a cat decides which of two previ-
ously experienced frequencies of flickering light is being pre-
sented. Since the constituent flashes of the two flicker frec[uencies
are identical, such decision-making, or differential discrimination,
would seem difficult to perform on the basis of the instantaneous
quality of the stimulus. In contrast to existential discriminations,
based on the presence or absence of a stimulus, differential dis-
crimination of this sort logically would seem to require the nervous
system to analyze the temporal sequence, or pattern, of stimulation.
Although one can conceive of possible alternate niechanisms
for the mediation of such behavior by time-measuring devices or
filter networks, a plausible mechanism for the analysis of sequential
stimuli would be a coincidence detector which compared patterns
of incoming activity with patterns generated by a stored representa-
tion of previously experienced sequences — a memory. This hy-
pothesis, with some relevant electrophysiological evidence, has been
presented in detail elsewhere (7, 9). My purpose here is to review

*The work described in this paper was supported in part by Research Grant
MY-2972 from the National Institute of Mental Health, and Grant G21831 from
the National Science Foundation.

**The author takes pleasure in acknowledging the kindness of Marc Weiss for
making available the data shown in Figures 6, 7. 8, and 9, and the assistance of Arnold
L. Leiman and Anthony L. F. Gorman in acquisition of portions of the data here
reported.

243

244 Information Storage and Neural Control

that evidence and to supplement it with a number of recent findings
in our laboratories which will also serve to illustrate some technical
innovations we were utilizing for these purposes.

Before I undertake this task, I wish to emphasize that the hy-
pothesis stated does not imply the mediation of memory by
regenerative electrical activity. The large literature on the con-
solidation process reviewed recently (4) shows that there are at
least two phases of memory storage: 1) An early, labile consolida-
tion phase, in which the representation of a recent experience is
susceptible to severe interference or destruction by numerous
chemical or electrical perturbations, and during which memory
may well consist of persisting electrical patterns of a reverberatory
sort; and, 2) a later stable phase in which such perturbations have
no effect, and during which memory is stored in some other fashion,
perhaps as a structural modification. This necessitates a coupling
mechanism whereby the reverberatory electrical activity main-
tained during the consolidation phase gradually stipulates the
structural change which will serve to represent it. A number of
workers have discussed the possibility that such structural changes
might be the specification of macromolecular configurations
(5, 6, 25); and, as Dr. Morrell has told you, a number of labora-
tories, including his and mine, have presented data suggesting that
ribonucleic acid (RNA) may play a role in this function (1, 2, 3,
11, 18). Whether or not RNA does participate in long-terin
memory storage, it seems reasonable at present to assume that
some form of long-term structurally mediated storage does exist.
Various data seem to require, further, that the postulated coupling
between electrical patterns and the long-term storage device be
reversible — that the pattern of iterated or sustained electrical
activity stipulate some representational structural modification,
and that this structural modification be able to generate an elec-
trical pattern identical to the one which established it.

Time does not permit detailed review here of the evidence which
I believe is relevant to the dynamics by which such a representa-
tional system is built, but such a detailed discussion has been pre-
sented elsewhere (7). For our present purposes, I hope it will suflSce
to summarize what I consider to be the salient characteristics of
these representational systems: 1) The repeated occurrence of as-

Neural Mechanisms of Decision Making 245

sociated neural activity in anatomically extensive regions of the
nervous system causes a functional relationship to become estab-
lished between these regions. Subsequent to such association,
stimulation of one region causes a response to occur in other regions,
although this response did not occur before the associated activity;
2) such altered response relationship cannot be interpreted as
merely a reflection of altered threshold, since Morrell has shown
that the new response is differential, and is displayed only to the
stimulus to which the association was established and not to closely
similar stimuli; and 3) discharge can occur from such a representa-
tional system with a temporal pattern which reflects the pattern of
stimulation while it was established.

TRACER STIMULI, LABELED POTENTIALS,
AND INFORMATION

The bulk of the data which I wish to present here was obtained
in studies of changes in the electrophysiological response to inter-
mittent stimuli during the establishment of conditioned responses.
The technique, used most profitably before us by Livanov and
Polyakov (14), was applied by Killam and me in our studies of
conditioned avoidance and approach responses in cats (8, 9). We
reasoned that, whatever the nature of the new responses established
in the brain during conditioning, such responses should appear
fairly reliably whenever the stimulus was presented. We selected
an intermittent light flash, which we called a "tracer conditioned
stimulus" (TCS), and searched the electrical activity of the brain
for the appearance of waveforms at the frequency of the TCS, which
were called "labeled potentials." Such a procedure greatly en-
hances one's ability to detect stimulus-bound signal in the midst of
the tremendous amount of ongoing business in the nervous system.
The appearance of labeled potentials in a structure during the
presentation of a TCS is sufficient evidence to conclude that in-
formation about the TCS is reaching that structure. It is clear that
such labeled potentials are not necessary for a structure to be in-
fluenced. A structure which shows no labeled potentials can be
receiving information about a TCS.

246 Itiformation Storage and Neural Control

In recent reviews, both Morrell (19) and I (6) have summarized
the large amount of data obtained in many laboratories from many
different species of experimental animals, showing that striking-
changes in the amplitude and distribution of labeled potentials
take place during the establishment of conditioned responses to
intermittent stimuli. Although the appearance of labeled potentials
in a structure justifies the conclusion that information about the
presentation of a TCS is reaching that structure, one cannot assume
that such labeled potentials actually are the neural coding of
information about stimulus frequency. Labeled potentials may
simply be nonfunctional correlates of the actual processing by
nerve cells of otherwise coded information about the TCS. Con-
versely, one cannot prove on the basis of present evidence that
labeled potentials are not the effective neural code for stimulus
frequency.

ASSIMILATION AND MEMORY TRACE

Many phenomena observed in earlier work directed my at-
tention to this problem because they suggested a functional role
for labeled potentials. The first of these phenomena was called
"assimilation of the rhythm" by Livanov (14), who first observed it.
It has since been described by many workers utilizing various
species in diverse experimental situations (6, 19). If one studies
the resting electrical activity of various brain regions in an animal
learning a conditioned response to an intermittent stimulus, one
observes that during the intertrial intervals a marked hypersyn-
chrony appears at the stimulus frequency, or at a harmonic thereof.
This spontaneous, frequency-specific activity can dominate the
resting electrical activity in early stages of learning. In our experi-
ence, it tends to diminish and disappear as the conditioned response
becomes well established, but will return briefly following per-
formance of an erroneous response. Figure 1 illustrates assimilation
and is taken from a paper by Killam and me (8). Note that the slow
hypersynchrony, in this case at one-half the stimulus fr-equency,
appears in the reticular formation, fornix, and septum in close
relationship. Assimilated rhythms, in our experience, appear
earliest, are most marked, and persist longest in nonspecific

Neural Mechanisms of Decision Making

SPOHTAWOUS ACTIvmr AT WFFEREMT STAGES OF TNAININO

247

... ^ I.

M TNAINM Mr 10%)

KMh TMUNMa DAY (24%)

20t« TKAININS Mr (*9%l

Fig. 1.

CON — Bipolar transcortical (visual) derivation

IPSI — Bipolar derivation from the same optic gyrus

RF — Midbrain reticular formation

SUP COLL — Superior colliculus

FX — Fornix

SEP — Septum

AUD — Auditory cortex

AMYG — Lateral amygdaloid complex

POST HIPP — Dorsal hippocampus
"Assimilation of rhythm" during avoidance training using ten per second flicker
as conditioned stimulus. (From John, E. R. and Killam, K. F.: J. Pharmacol.
Exp. Therap., 725:252-274, 1959.)

248 Information Storage and Neural Control

regions. Other workers have reported that assimilation of the
rhythm appears only in the training situation and is not observed
when the animal is in his home cage. Such observations demonstrate
that the brain has the capacity to generate temporal patterns of
potentials which are substantially the same as those elicited by a
previously experienced stimulus. Although obtained under different
experimental conditions, the phenomena described by Morrell and
his colleagues in studies of cortical conditioning (17, 20, 21) and
by Stern et al. (24) in their studies of trace conditioning provide
additional evidence of this capacity. It is of interest that one can see
these assimilated rhythms appearing with apparent simultaneity in
regions relatively distant from each other, as if an anatomically
extensive system were activated. One might reasonably ask whether
such sustained patterns in the absence of a previously experienced
stimulus do not reflect neural processes which represent that ex-
perience. These endogenously generated potentials may be a mani-
festation of the elusive "memory trace."

ASSIMILATED RHYTHMS AND GENERALIZATION

Some evidence presented earlier by Killam and me is com-
patible with a functional role for such endogenously generated
frequency-specific patterns. We observed that an animal trained
to perform a conditioned avoidance response to a ten per second
flickering light characteristically displayed twenty per second po-
tentials in the visual cortex, as seen in Figure 2. On presentation of
a seven per second flicker after the animal reached criterion to the
ten per second flicker, the animal showed evidence of generaliza-
tion by repeatedly performing the conditioned response to the
new stimulus frequency. Examination of the electrical records
showed that the response of visual cortex to the seven per second
flicker was a twenty per second potential, as is visible in Figure 3A.
The arrow denotes the beginning of the behavioral response.
After repeated presentation of the seven per second flicker, the
animal no longer performed the generalized response but sat
quietly. At this time, the seven per second flicker elicited pre-
dominantly seven per second activity in the visual cortex. Presenta-
tion of the original ten per second conditioned stimulus at this
point failed to elicit performance of the conditioned response for

Neural Mechanisms of Decision Making 249

SIGNAL ^AAA/VV^AAA-/V^AAAAJ^AAAAAAAAAAAAAA

ipsi vis ^

SEP

AUD I

VA f- ^

AMYG I

Fig. 2.

LG — Lateral geniculate

VA — Nucleus ventralis anterior. Abbreviations otherwise as in Figure 1.
Characteristic electrical response on presentation of the ten per second flicker
conditioned stimulus to the fully trained animal (100% performance). (From
John, E. R. and Killam. K. F.: J. Pharmacol. Exp. Therap., 125:2S2-274, 1959.)

several trials, during which a slow wave at about seven per second
could be observed in cortex, as shown in Figure 3B. When per-
formance reappeared to the ten per second stimulus, twenty-per-
second potentials again were elicited in cortex.

Another example of this is provided by Figure 4, which illustrates
recordings obtained during generalization to a ten per second
flicker by a cat previously trained to perform a conditioned avoid-
ance response to a four per second flicker. Note that upon presenta-

250

Information Storage and Neural Control

C
O

s ^

it: „

TD O

-a

c
o

«J

c
o .i^

he ^
C C

2 3

o •-:

-5 aj |j-l a.

o ^

s c .o;

03 lU

be — .
bO

C .i3 ^

C
O

i3 o

<^1 en

o -S^ r^

o S

<
bb I- '

Neural Mechanisms of Decision Making

251

-^. 4

'■%-

'j;

' IM

•*>.

_>-

'-4

-<■'

'"'^-^

"^4

'<.

?•

J3 ■" 'i^

.2 =5 -B

3
"3 "O

o ;5

'T3 .a ^

:5 '5 'a

.-c Qh U

-owe

t) o c

c^ V .^

O J pri

Q I I

Q "^ ^

MOO

™ O 1-

O
<

CLqn

?3 a

!^ i-i pd J

5 5P

3 -^

-t

^-. O

u >

o i!

Vi '^'

;r~ ^

V-

"¥:

1^ !^ C O

o c S ^ ^ 5^

^ .^ -?. « ^ -

C/J

0
c

1 <

P. h

^§

^
W

■fi

> 0

w
u

0
0

c
0
u

252 Information Storage and Neural Control

tion of the ten per second flicker, potentials at the same frequency
clearly appear in visual cortex and lateral geniculate, with less
marked evidence of response in the intralaminar nuclei and the
reticular formation. The cortical response suddenly shifts to a
hypersynchronous slow wave at a frequency between four and five
cycles per second, while the animal shows a startled movement
and four seconds later performs the conditioned lever press estab-
lished to a four per second flicker. Notice that the lateral geniculate
maintains ten per second potentials during this period, although
potentials at lower frequency are visible in the reticular formation
and occasionally in the intralaminar nuclei.

A comparable observation has been reported by Majkowski (16).
After a rabbit was trained using a three per second light, generaliza-
tion was obtained upon presentation of a five per second light. As
can be seen in Figure 5, during such a generalization a three per
second wave can be observed in motor cortex, although the re-
sponse of visual cortex is at five per second. Related findings have
been described by other workers (6).

Data of this sort suggest that during generalization a neural
system, which has become established as a consequence of experi-
ence with the intermittent conditioned stimulus (CS), is somehow
released by the new stimulus, but discharges with the character-
istic temporal pattern of the original conditioned stimulus. This
system seems to include the mesencephalic reticular formation and
the intralaminar nuclei in association with the visual cortex. It is
interesting that during generalization phenomena of the sort
described, regions of cortex other than the region of the conditioned
stimulus, such as ectosylvian or medial suprasylvian, tend to display
potentials at the frequency of the peripheral stimulus.

Additional data on this phenomenon have recently been obtained
in our laboratory by Marc Weiss (26) who trained a cat to perform
a conditioned avoidance response to a four per second flickering
light. After establishment of this response, the cat generalized
readily to a ten per second flicker. Figure 6 (Top) shows the
EEC's obtained from various brain regions during such generaliza-
tion. Note the irregular slow activity in the visual cortex contrasted
with the regular ten per second response in the lateral geniculate.
Figure 6 (Bottom) shows records obtained after differentiation of

Neural Mechanisms of Decision Making

253

*- ■- i_A_ *- — *—

-\>^^-

R. MOTOR

L. VISUAL

SEC.

50>iV

I I i I 1 » 1 > I I I I

L. EMG

^\r -~\

R. EMG

^'»^-

Fig. 5.

L. MOTOR — Left motor cortex

R. MOTOR — Right motor cortex

L. VISUAL — Left visual cortex

L. EMG— EMG of left hind limb

R. EMG— EMG of right hind limb
Electrical responses to five per second flicker during generalization of right hind
leg flexion response after training with a three per second flicker (rabbit). (From
Majkowski, J.: Acta Physiologica Polomca, /A'( 5): 565-581, 1958.)

the conditioned response, during which the animal was taught to
discriminate between ten and four per second flicker. Note that
the visual cortex now displays markedly increased regularity of
ten per second potentials during the ten per second flicker.

As is evident from the stimulus trace, these two records were
obtained using a "limp circuit" which periodically deleted a flash
from the flicker train. The purpose of this technique was to attempt
to evaluate the extent to which endogenously generated potentials
would "fill in" the period of the deleted flash. Sufficient data have
not yet been obtained to warrant discussion of this aspect of the
records, and it is not central to our present purpose.

254 Information Storage and Neural Control

GENERALIZATION TO 10 cp» FLICKER AFTER TRAINING TO 4 cos SOIIV

R VIS ex. ' 'J t J i ■.

L VISClt.1*^'>,l'-'- ' .

" ^' '■'■'''' ■, ' ' . , '

L.IAT «t»(»^^' .'','"''.'■•", /'•»."'•' ■''■''■■-'

IS. WW . J
L. HP-VW^yiJVnV.'AV

.KIM-——'-' — , ,, -, , „.^,^,_^„^ , , .1 J J ,■■,,, J... ..,...,.. ,,,,f,ff '•■'""

' I sec '
RESPONSE TO 10 cps FLICKER AFTER DIFFERENTIATION lOpv

L OORS ner .■**''VV>'-■^V^^''■'v'''"»--•-^v'•■^-A/•%'"v■A'J^«^A<;v■-•r--^'^^"^v.■'■^^

Li«s «M^*,,^_,w\V'^W"f-^V^VvV''''A'^vv\WVi.>A^^^^^^

Fig. 6.

R. NUC. RET. — Right nucleus reticularis
L. MSS CX — Left medial suprasylvian cortex
R. VIS CX— Right visual cortex
L. VIS CX — Left visual cortex
L. LAT. GEN. — Left lateral geniculate
L. DORS. HIPP.— Left dorsal hippocampus
L. RF — Left mesencephalic reticular formation
L. CM — Left centre median
(Histological verification not yet available.) {Top) Electrical responses to ten
per second flicker during generalization, after avoidance training using a four
per second flicker tracer conditioned stimulus. (Bottom) Electrical responses to
ten per second flicker following differentiation of avoidance response. (4 per
second — S^, 10 per second — S'^). (From Weiss, Marc: Unpublished master's
thesis, University of Rochester, 1962.)

Figure 7 illustrates an average response waveform obtained from
the lateral geniculate body of this animal during generalized per-
formance of the conditioned response to the ten per second flicker.
This computation was obtained using a Mnemotron average re-
sponse computer and is based on 100 periods of ten per second

Neural Mechanisms oj Decision Making 255

AVERAGE RESPONSE OF LATERAL GENICULATE DURING
GENERALIZATION TO lOcps AFTER TRAINING TO 4cps

100 SWEEPS

lOcps on
Fig. 7. Average response computed from lateral geniculate during generalization
to ten per second flicker, after avoidance training using a four per second flicker
tracer conditioned stimulus. (From Weiss, Marc: Unpublished data.)

flicker, each period beginning at a deleted flash and lasting for
625 milliseconds. Note the regularity of the computed waveform.
Similar regularities were observed in average responses computed
during generalization to ten per second flicker from dorsal hippo-
campus, centre median, nucleus reticularis, and medial supra-
sylvian cortex. *

Figure 8A shows the average response waveform computed from
the visual cortex at this stage of training during correct performance
to a four per second flicker.

Figure 8B shows a comparable average response waveform com-
puted from potentials recorded from the visual cortex during
generalized performance to a ten per second flicker. Note the
complex, irregular waveform.

Figure 8C shows the average response waveform computed from
the visual cortex during correct performance to the ten per second
flicker after diff'erentiation. In contrast to Figure SB, note the
markedly increased simplicity and regularity of the waveform.

I was impressed by the fact that these data might provide the
basis to test the hypothesis that the waveforms observed during
the generalization represented the interaction between an endogen-
ously generated representation, or memory, of the stimulus fre-

*Histological verification of electrode placements has not yet been obtained.

256 Information Storage and Neural Control

AVERAGE RESPONSE OF VISUAL CORTEX

4cps AFTER AVOIDANCE
TRAINING

DARK PERIOD

DURING GENERALIZATION
lOcps AFTER TRAINING TO
4cps

lOcps ON

lOOms

DARK PERIOD

lOcps AFTER DIFFERENTIATION

Ocps ON

, lOOms

I — 1

DARK PERIOD

100 SWEEPS

iOO SWEEPS

100 SWEEPS

CALCULATION OF B. FROM
C+A AND C-A

26 ms ^ .,

GENERALIZATION WAVEFORM

o- — oCALCULATED WAVEFORM

0=10 + 4 •=10-4

Fig. 8. Average response computed from visual cortex: (A) In response to four
per second flicker after avoidance training using a four per second flicker tracer
conditioned stimulus. (B)During generalization to ten per second flicker. (C)
In response to ten per second flicker after difTerentiation training. (D) Com-
parison of generalization waveform with calculated interference pattern. (A, B, C
from Marc Weiss: Unpublished data.)

quency used during training" and the exogenously derived neural
response to the new stimulus eliciting generalization. Therefore, I
explored the interference patterns which could be constructed by
algebraic addition or subtraction of the waveforms (Figs. 8A and

Neural Adechanisms of Decision Making

257

8C) obtained from the visual cortex during behaviorally appropri-
ate response to ten per second and four per second flicker.

Figure 8D shows the approximation to the generalization wave-
form which can be produced by these simple algebraic manipula-
tions. At each point of the curve, the manipulation which gave the
better approximation (10+4 or 10— 4) was selected. It is not clear
what the physiological basis might be for the particular sequence
of algebraic operations used to achieve this approximation.

AVERAGE RESPONSE

4cps AFTER AVOIDANCE
TRAINING

RETICULAR FORMATION

100 SWEEPS

250ms

DARK PERIOD

100 SWEEPS

B.
DURING GENERALIZATION
lOcps AFTER TRAINING
TO 4cps

lOcps AFTER DIFFERENTIATION

lOOms
DARK PERIOD

CALCULATION OF B FROM
C + A AND C-A

26 ms

- — GENERALIZATION WAVEFORM
°—o CALCULATED WAVEFORM

0=10 + 4 •=10-4

Fig. 9. As Figure 8, but data derived from mesencephalic reticular formation.

258 Information Storage and Neural Control

Figure 9A shows the average response waveform obtained from
the mesencephaHc reticular formation at this stage of training
during correct performance to a four per second flicker.

Figure 9B sliows the average response waveform obtained from the
mesencephalic reticular formation during generalization to the ten
per second flicker. Note the highly complex and irregular waveform.

Figure 9C shows the average response waveform obtained from
the mesencephalic reticular formation during correct performance
to the ten per second flicker following differentiation. In contrast
to Figure 9B, note the increased simplicity and regularity of the
waveform.

Figure 9D shows the fit to the generalization waveform of the
interference pattern which can be obtained by arbitrary algebraic
addition or subtraction of the two waveforms elicited from the
reticular formation during behaviorally appropriate performance
to four per second and ten per second flicker, as shown in Figures
9A and 9C. Again, that manipulation (10 +4 or 10 — 4) which
gave the better fit was selected.

Thus, one can synthesize interference patterns from average
response waveforms computed during behaviorally appropriate
response to two different stimuli and can approximate closely the
actual average response waveform obtained when an animal re-
sponds to one of these stimuli by a previously learned behavior
appropriate to the other. This demonstration provides striking
evidence in support of the suggestion that the neural response to
the ten per second stimulus actually presented was modified during
generalization by an electrical influence identical with the response
to the four per second conditioned stimulus repeatedly experienced
during the earlier establishment of the conditioned response. At
the moment I see no way to evade the conclusion that the conse-
quence of experience with the four per second flicker during learn-
ing somehow caused a modification of neural structure which there-
by gained the capacity to generate electrical activity like that which
established it. These data support the interpretation that such
patterns of potentials are of functional significance and are closely
related to the actual processing of information.

At our present stage of knowledge, no mechanisms come to mind
which might serve to generate and mediate an interaction of the

Neural Mechanisms of Decision Making 259

sort described. Yet some insight may be offered from the fact that
only visual cortex and reticular formation, among the structures
studied in this animal, displayed these peculiar waveforms during
generalization. Lateral geniculate was notably regular in its
response. This configuration suggests that somehow an interaction
between visual cortex and reticular formation may be central in
the mediation of phenomena of this sort. Further work is obviously
necessary before the interpretations offered here can be accepted
as accurate.

CHARACTERISTICS OF MISTAKES
DURING DIFFERENTIATION

Although the data presented in the preceding section are of a
different sort from those which Killam and I described previously,
they are in accordance with observations we made while studying
the difference in electrical recordings obtained during correct and
erroneous performance of flicker discriminations in a differential
approach-avoidance situation (9). In those animals, we observed
that among the most marked changes in labeled potentials during
differential conditioning were those which occurred in the reticular
formation, intralaminar nuclei, and hippocampus. A particular
relationship between the configuration of potentials in these struc-
tures and in visual cortex seemed to be closely related to appropriate
performance. During signal presentation, potentials in the non-
specific structures could often be observed at either of the two
flicker frequencies between which differential response had been
established. Wlien behavioral performance was appropriate to the
peripheral OS, the frequencies of potentials in visual cortex and
in nonspecific regions were in good correspondence to the OS.
However, when behavioral performance was inappropriate, the cor-
respondence of labeled potentials to tlie CIS diininished and periods
of hypersynchrony appeared at the frequency of the stimulus
appropriate to the behavior actually performed, particularly
in centralis lateralis, dorsal hippocampus and reticular forma-
tion. In Figure 10 are presented recordings obtained from a cat
trained to perform a lever press to obtain milk during a ten per
second flicker without reinforceinent during six per second flicker.

260 Information Storage and Neural Control

After Operant Conditioning to IO/5 "$<=', 6/s -5^

LG AA«'^,VWvVw''^*^i/^^ 1

Fx ^''.A^^vv^^\AAV^\^4^f*^(^^M i

10* ; ,

SIG W/WVVWVVWWVWWWWVWVWVVW/WWWWWVWV'JVW^

VH ^>/\^ht^\f^^/^j>f^^ I

Correct

FX \\\hY¥4^'^'*^^^^

s iG /vwwwv ;;;;M\/ww\/'//w'^vw/w\/'yw^AWM/vv;MWAVvvwwvMVWvwv^^

Error

Fig. 10.

MG — Medial geniculate

VC— Visual cortex

LG — Lateral geniculate

FX — Fornix

VH — Ventral hippocampus

CL — Centralis lateralis

MSS — Medial suprasylvian cortex

Records obtained during differential approach conditioning (10 per second — S ,

6 per second — S^). (Top) Correct response to ten per second flicker. (Bottom)

Error of omission to ten per second flicker. (From John, E. R. and Killam,K. F.:

J. Nerv. Merit. Dis., 73/.-183-201, 1960.)

The top records were taken during correct performance to ten
per second flicker. Note in particular the marked frequency-
specific response in fornix and centraHs lateralis. The bottom
records were obtained during an error of omission when the cat
failed to press the lever in response to the ten per second signal. Note
the diminished ten per second labeled potentials and, in particular,

Neural Mechanisms of Decision Making 261

After Operant Conditioning to ICVs-S*^, 6/i-S^

s'G — ^ s;f-o^.^^^vA\\^^^^^\J\J^^^\^^J\,\^J\^

Correct

siQ — ^v:vVvV^.\\Vv\\\^,■"^^^^^^^^J\\

tnror

Fig. 11.

MG — Medial geniculate

VC — Visual cortex

LG — Lateral geniculate

FX — Fornix

VH — Ventral hippocampus

CL — Centralis lateralis

MSS — Medial suprasylvian cortex

Records obtained during differential conditioning (10 per second — S^, 6 per

second — S ). (Top) Correct response to six per second flicker. (Bottom) Error of

commission to six per second flicker. (From John, E. R. and Killam, K. F.:

J. Nerv. Merit. Dis., 737.- 183-201, 1960.)

the slow potential at about six per second seen most clearly in
centralis lateralis.

Figure 11 shows the converse phenomenon in the same cat. The
top record shows correct performance to the non-reinforced six
per second flicker. The bottom record shows an error of commission
to the six per second flicker. Note the lessened frequency specificity

262 Information Storage and Neural Control

After CAR to 6/S

Fig. 12. Records obtained during lever press to 10 per second flicker after avoid-
ance training to the 6 per second flicker. Arrow indicates conditioned response.
(From John, E. R. and Killam, K. F.: J. Mrv. Merit. Dis., 7J7.-183-201, 1960.)

of potentials in the lower record as contrasted with the upper; in
particular, observe the period of approximately ten per second po-
tentials in centralis lateralis.

Figure 12 shows the potential configuration reliably obtained in
this cat in response to ten per second flicker following the establish-
ment of a conditioned avoidance response to the six per second
flicker, while the conditioned lever pressing" response to ten per
second flicker was maintained. At this stage in this animal, presenta-
tion of the ten per second flicker elicited clear labeled potentials
in visual cortex and several other structures, while an initial slow
wave at about six per second appeared in centralis lateralis and
fornix. Superimposed on this slow potential, almost as a modulation,
is a ten per second potential which gradually becomes clearer and
eventually dominates the record. Wlien lever press occurred to
the ten per second flicker, it almost invariably took place during
a period when the ten per second labeled potential dominated the
activity of centralis lateralis. Characteristically, as this correspond-
ence between the frequency of the dominant activity in the non-
specific structures and in the visual cortex occurred, a change

Neural Mechanisms of Decision Makirrg 263

During Blockc3dc of CAR After Rcserpinc

vc -*v^A^AM^^MMM'V^^A^^MAA/W\A^ -^o^/v i

LG ^H^aT'''^^^ I

Fig. 13. Records obtained in response to ten per second flicker after performance
of the avoidance response to six per second flicker was blocked by injection of
reserpine (1007/kg). (From John, E. R. and Killam, K. F.: J. Nerv. Ment. Dis.,

737.- 183-201, 1960.)

was observed in the recorded waveforms. This change was a shift
from rounded "waves" to more sharply peaked spikes and was
foUowed one or two seconds later by performance of the conditioned
response.

Some indication of the possible functional relevance of the slow
six per second centralis lateralis waves seen during the approach
signal after avoidance training is provided by the data in Figure 13.
When performance of the avoidance response to the six per second
TCS was completely blocked after administration of 100 7/kg. of
reserpine, presentation of the ten per second TCS no longer elicited
the previously marked slow potentials in centralis lateralis and
elsewhere, but instead resulted in the appearance of massive
labeled responses at ten per second frequency. Wlien 0.5mg/kg. of
amphetamine was administered to this cat, the reserpine blockade
of the conditioned avoidance response performance to the six
per second TCS was completely reversed in a few minutes. Presenta-
tion of the ten per second TCS for the lever pressing response to

264 Information Storage and Neural Control

obtain milk once again elicited the same slow potentials in centralis
lateralis and elsewhere as seen previously in Figure 12.

These observations seemed to support the interpretation that
the labeled potentials reflected some aspect of information process-
ing and might be of functional significance. Such an interpretation
would also be in agreeinent with the findings of Livanov et al. (13)
and Liberson et al. (12) who have reported that direct electrical
stimulation of various brain structures at frequencies like those of
the intermittent conditioned stimuli used in establishing a condi-
tioned response resulted in performance of the learned behavior.

Nonspecific structures seem to play a central role in the processing
of information during differentiation. Evidence of differential
suppression of potentials after habituation, of the major signs of
assimilation, of the inost marked increinents in labeled potentials
during differential training, and of shifts in the frequency of
labeled potentials during behaviorally inappropriate response have
all been observed in these structures. The particular configuration
of potential patterns during differential response suggested several
hypotheses: 1) The role of specific sensory systems may be con-
ceived of as the central propagation of information representing
the present state of the environment to a particular cortical region;
2) this information may be compared, via the diffuse projection
system, with a representation of past experiences activated in the
rhinencephalon and the reticular formation by the similarity be-
tween past and present environment, modified by the state of
the organism in terms of effect and drive level; and, 3) the
appropriate selective performance of adaptive behavioral responses
may depend upon achievement of a sufficient congruence, via some
unknown coincidence detection mechanism, of the potentials
reflecting present and past experience.

CONCURRENT PERIPHERAL AND
CENTRAL STIMULATION

These various considerations led our group to investigate further
the question of whether temporal patterns of potentials might be
information. When animals are trained to perform a differential
discrimination between two flicker stimuli differing in frequency,

Neural Mechanisms of Decision Making 265

are the observed frequency-specific potentials a reflection of the
coding" and processing of information causafly related to the
behavioral performance, or do they merely reflect generalized
processes of local excitation and inhibition that are not specifically
informational and bear only a relationship of concomitance to
the behavioral performance?

In the initial studies which we undertook to resolve these ques-
tions (10), an attempt was made to evaluate directly the functional
significance of labeled potentials observed in various brain struc-
tures in cats fully trained to perform diff'erential avoidance re-
sponses to two flicker conditioned stimuli of different frequencies.
We studied the behavioral effects of direct electrical stimulation
of the brain at frequencies concordant or discordant with the
frequency of the peripheral conditioned stimuli presented simul-
taneously. After pilot studies showed that low frequency electrical
stimulation was not effective, a modulation technique was devised.
A standard "carrier" waveform, consisting of a 100 cycle per
second biphasic square wave with a 2 millisecond pulse duration,
was modulated at the frequency of the peripheral TCS. This pro-
duced trains of bursts of 100 cycle per second square waves, with
the burst frequency identical with the flicker frequencies to which
the animals were conditioned. Trains at different frequencies could
be manipulated to achieve equal duration of constituent bursts
or to equate total electrical energy by selection of appropriate
burst durations.

Most structures were explored both unilaterally and bilaterally.
For each structure, we determined the current level at which
central stimulation at both the reinforced (S ) and the non-
reinforced (S^) frequency blocked performance to concurrent
photic stimulation at the S frequency. This current level was
defined as the occlusion threshold, or cut-off. The current intensity
at which conditioned response perfoimance returned to concurrent
photic and central stimulation at one central frequency but not
the other was defined as the differential threshold. If a differential
threshold was observed, a series of trials was carried out to de-
termine the reliability of such an effect. Throughout such stimula-
tion sessions, central stimuli were presented in counterbalanced
frequency sequence, and each sequence was bracketed by trials

266 Information Storage and Neural Control

using only the peripheral conditioned stimuli. Only central se-
quences bracketed by correct performance to the peripheral
stimulus alone were considered acceptable.

Intensive studies of the effects of concurrent central and peri-
pheral stimulation have been carried out in two cats. One of these
animals (Cat 4) was conditioned to press a lever to avoid shock
within fifteen seconds after the onset of a four per second flicker,
but was punished if lever press was performed during" a ten per
second flicker. The other animal (Cat 10) was trained to the
opposite significance of flicker frequency, pressing" the lever to
ten per second flicker but not to four per second. Results of the
concurrent stimulation studies on these two animals are sum-
marized in Table I.

Note that the data show, at a very high significance level, that a
four per second electrical stimulation of the visual cortex is much
more effective than a ten per second input in achieving inhibition of
conditioned avoidance response performance to a simultaneously
presented TCS in both Cat 4 and Cat 10, although the meaning
of a four per second flicker was opposite for these two animals.
Since this was true both for central stimuli of equal burst duration
and for those of equal energy, the severe disruption can be at-
tributed to the frequency of the simulated input. Four per second
central stimulation was much more inhibitory than ten per second.
This effect was not observed in auditory or medial suprasylvian
cortex, but appeared to be rather specific for the cortex of the
CS modality. This suggests that the input in some way interferes
with activity in the visual system and that the visual cortex or
regions to which it projects are involved in the mediation of the
conditioned response. Such conclusions would be consonant with
those of Zuckermann (27), who observed interference with per-
formance of conditioned responses to visual stimuli during after-
discharge following stimulation of visual cortex but not of motor
cortex or reticular formation. Such a conclusion is difficult to
reconcile with the remarkable ability of Cat 4 to sustain appropriate
behavioral response to a four per second flicker when 2.5 times
more electrical energy was applied to the same visual cortex at
ten per second. In contrast to the cortical current values for dis-
ruption, note the exceedingly low current required in subcortical

Neural Mechanisms of Decismi Making

267

o o o
VVV

o o o o o

A A A A A
-^ -* ^ -^ •*

o o
VV

V V

AAII AAAAAAAAAII All A

■*0 TfOO-^OOOOO o o

i O (M O

I I I I I I I I I I I .
Tj-oO-^O<NOLnOr0cq\0n

to • • ■ CN T-. ■ r-c

'-I o o o

OOO'^OOOO

„ „ i I I I I I I I

00C^h00CN1i-O(N1O\000

fe -54,

(.1 ^

+ ^

<^l OI ^ Ol ^ T-c ^_ <N] ro .^ tv] fn so to

^-c ■rt CMt-it-c-t-. (NJcOt-c T-.OJcOr-i'^i-.Tf (

'*0'*OtJ-0-*0-^0^0'^0-^0-^0^0-D-'

„

(«

C/3

■£-G

■a

. ^

i; G

lue repr
th powe
d found

t va
t wi
shol

'c5

.3^

ren
can
hre

^Oi

3S-

Crrl

^.§^•2

'So

u 3 "

;; s

^ cr o

n o

hMZ22£

* 1 \ \-XK

fe=^

268 Information Storage and Neural Control

stimulation, particularly in the reticular formation, to achieve
similar results.

These experiments showed that low frequency stimulation seemed
to have an intrinsically inhibitory effect. Perhaps the fact that
differential inhibition occurred in the same direction in both of
these animals may be understood on this basis. However, this
study did not provide evidence in support of the hypothesis that
the configuration of labeled potentials during performance of
a difTerentiated conditioned response to a TCS represents coded
information about the peripheral stimulus being processed by the
nervous system. Work must be pursued with more animals, using
other frequencies and additional anatomical placements, in order
to clarify these questions.

Yet one can conceive of a number of possible reasons for the
results obtained: 1) The brain does not code or process information
in a manner which is related to the observed configurations of
labeled potentials; 2) powerful intrinsic "resonance'' to low fre-
quency input resulted in an uncoded inhibitory effect masking our
ability to find differential effects based on the significance of a
particular frequency for an animal; or 3) the organization of the
coding and processing of this sort of information in differential
response might proceed exactly as we would conjecture on the
basis of configurations of labeled potentials. Our inability to
demonstrate differential effects based on central stimulation at
presumably informational frequencies might simply be due to
the fact that the response of neural tissue to our artificial waveforms
was inappropriate for functional interaction with "brain language."

DIFFERENTIAL CONDITIONING TO STIMULATION
OF A CENTRAL SITE

A number of experimental strategies have been devised to explore
these alternative explanations. One relatively straightforward
approach was to attempt to make our artificial input functionally
equivalent with brain language. We were struck by the fact that in
hundreds of trials we had failed to get any indication of behavioral
response to direct central stimulation alone. This seemed to contra-
dict the work of Livanov et al. (13), Liberson et al. (12), and Neff ^/

Neural Mechanisms of Decision Making

269

al. (22, 23), who reported response to central stimulation after con-
ditioning to a peripheral stimulus. We came to the conclusion that
this difference might be due to the fact that our animals had been
highly overtrained to differential response, with punishment for
error. Subsequent pilot work by Karl Corley in our laboratories has
confirmed that central stimulation will elicit responses previously
established to peripheral stimuli when erroneous performance has
not been punished. Since our central stimuli had never been coupled
with primary reinforcement, we attempted to train these animals
to differentiate between the pulse trains which had been used in
the previous work. Frequency significance for each animal remained
the same as for the peripheral flicker. Thus, one can consider this
to be an attempt to transfer the differential response from inter-
mittent photic stimulation to intermittent central stimulation.

Figure 14 shows the learning" curve for Cat 4. Differential training
was instituted at the arrow after reliable performance of the avoid-
ance response had been established to four per second bursts
delivered to the electrodes on left and right visual cortex. Trials

CAT 4
CENTRAL CONDITIONING USING BILATERAL STIMULATION
OF VISUAL CORTEX

25 I 58 I
50 83

8 9 10
SESSIONS

CUMULATIVE TRIALS

-T—
101

I 121 I 171 I
112 146 183

— I—
30

— r-

— r-
63

8 13 17 25

Fig. 14. Learning curve for avoidance response to direct central stimulation in
cat previously differentially trained to flicker conditioned stimulus. At arrow
central diff"erentiation training began (4 per second — S , 10 per second — S'^).

270

Information Storage and Neural Control

resulting in failure to perform in response to the S^ were scored
as correct only when bracketed by correct performance of the
conditioned response to the S . Four per second bursts were
usually 25 milliseconds in duration, although response could be
elicited by shorter bursts. Ten per second bursts were usually 10
milliseconds in duration. Central pulse trains at different modula-
tion frequencies were equated for total electrical energy. "Carrier"
frequency was usually 200 cycles per second, but response could also
be elicited reliably at 100 cycles per second. Carrier pulse width
was 2 milliseconds. Threshold current for performance was found
to be between 1.8 and 2.1 milliamperes. Note that the occlusion
threshold at the stimulation site for concurrent peripheral and
central stimulation had been 4.0 milliamperes.
^^Figuie 15 shows the learning curve for Cat 10. Stimulus par-
ameters were as for Cat 4, but the opposite significance was at-
tached to frequency. Threshold current was around 1.8 milli-
amperes.

CAT 10
CENTRAL CONDITIONING USING BILATERAL STIMULATION
OF VISUAL CORTEX

8 9 10 II
SESSIONS

CUMULATIVE TRIALS

I 1 1 1 1 1 : 1 1 1 1 —

12 ' 59 ' 96 ' 117 I 167 ' 203
37 71 107 142 183

— I —
84

~~i —
27

Fig. 15. As Plgure 14, but in tliis animal the significance of the stimulus fre-
quencies was reversed. (10 per second — S , 4 per second — S^). Note apparent
transfer of previously established peripheral frequency discrimination to central

stimuli.

Neural Mechanisms of Decision Making 271

It is of interest that in this animal, once the central stimulus had
been established as informationally adequate by conditioning, the
peripherally established c/ifferrntiated response appeared to generalize
to the central stimuli. Differential response to central ten per
second and four per second stimuli was perfect on the twelfth
session, which was the first occasion on which the four per second
stimulus was presented following conditioning to central ten per
second stimulus. The animal seemed to benefit from the previous
differential experience with peripheral stimuli at these frequencies.
Whether or not such generalization will take place reliably, it is
obvious that these two animals have been trained to diff"erentiate
between two sequences of events of identical energy occuring
at the same central site. Thus, the temporal pattern of events at a
place in the brain can serve as information. Further, since the con-
stituent pulses of all central stimuli are but two milliseconds
wide, the temporal pattern of significance here is the slow modula-
tion frequency characterizing the stimulus trains. Such evidence
does not demonstrate that the patterns of slow labeled potentials
which appear in certain brain structures are information, but
it does establish that slow patterns at a place can be information.

Clearly, it is desirable to explore the propagation of such centrally
delivered difTerential stimuli to other brain regions from the input
site before and after they are established as adequate conditioned
stimuli. The interaction of such pulsed central stimuli with con-
current photic stimuli must be investigated, and the behavioral
as well as electrophysiological consequences of concordant and
discordant central and peripheral tracer stimuli are presently being
studied in our laboratories. Such studies should provide additional
insight into the functional role of labeled potentials in performance.

Some additional information of interest has been obtained from
Cat 10. Table II shows the consequences of a number of trials in
which one or the other of the visual cortex electrodes was stimu-
lated together with some other cortex placement. Note that
differentiated conditioned response was obtained fairly consistently
when the stimulated electrode pair included the right visual cortex
electrode but not when it included the left visual cortex electrode.
It is pertinent to recall that the central pulses were biphasic. These
data suggest that whatever the nature of the neural mechanism

272 Information Storage and Neural Control

TABLE II

Generalization of Differentiated Avoidance Response to Central

Stimulation of Other Electrode Placements Following Establishment of

Differential Response to Electrical Stimulation of

Left vs. Right Visual Cortex

10/Sec

4/Sec.

CR NR

Right Visual +
other cortical sites

0 11

Left Visual +
other cortical sites

2 4

Sub-Cortical Sites

mediating the differential performance to the central stimulation,
this relationship has been preferentially established to only one
of the two stimulated regions. This finding is consonant with
the analogous report of Loucks (15), but extends his observation
to differentiated responses.

It is also of interest that bipolar subcortical stimulation, without
reinforcement, did not elicit generalization of performance. In
subsequent training, we obtained some indication of performance
of conditioned responses to stimulation of the reticular formation in
this animal. A total of 175 conditioned responses were obtained in
460 trials. Performance fluctuated between 70 per cent and 0 per
cent and did not stabilize. This may indicate changes in neural
threshold since all trials were conducted in the same current range,
or may reflect artifact due to an increase of operant level which took
place. Systematic studies of generalization and transfer of differ-
ential conditioned response from one site of central stimulation to
another, in conjunction with electrophysiological studies, may help
elucidate the neuial mechanisms mediating such perfoimance.

DIFFERENTIAL EFFECTS OF LATENCY ON
DISRUPTION DURING CONCURRENT STIMULATION

Some months after the preceding experiments were concluded, a
final study was carried out on Cat 4. Under extinction conditions, it
was observed that stimulation of visual cortex at previously effective
parameters no longer elicited the conditioned response. No attempt
was made to ascertain whether the introduction of reinforcement

Neural Mechanisms of Decision Making 273

would restore performance. Instead, we measured the occlusion
threshold for concurrent four per second flicker and four per second
visual cortex stimulation and observed it to be around 3.0 milli-
amperes. An experiment was then devised to explore whether the
disruptive eff'ect of cortical stimulation varied as a function of the
time that such stimulation occurred with respect to the instant
when the flash of light was presented.

Specifically, we investigated the consequences of using our
stimulus generators (Tektronix) to delay direct cortical stimuli so
as to cause them to coincide either with the early or late phase of
the cortical response to the peripheral conditioned flash. Stimuli
were arranged in a counterbalanced sequence, thus:

4 per second flicker alone

4 per second flicker + 4 per second visual cortex stimuli (early)

4 per second flicker + 4 per second visual cortex stimuli (late)

4 per second flicker alone

4 per second flicker + 4 per second visual cortex stimuli (late)

4 per second flicker + 4 per second visual cortex stimuli (early)

4 per second flicker alone

All cortical stimuli were at 2.8 milliamperes. Carrier frequency
was 100 cycles per second with two millisecond pulse width. Bursts
consisted of five biphasic pulses (25 millisecond burst width).
"Early" stimuli were so phased as to reach visual cortex 15 milli-
seconds after each flash of the four per second flicker. "Late"
stimuli were timed to reach the cortex either 80 milliseconds or
110 milliseconds after each flash of the four per second flicker.
Shock to the feet was delivered if the avoidance response was
not elicited within fifteen seconds when the four per second flicker
was presented alone. All trials involving central stimulation were
under extinction conditions, i.e., no shock was delivered. Central
sequences were scored only if bracked by correct performance
of the conditioned response in less than fifteen seconds to four per
second flicker alone, without punishment.

Table III summarizes the results of these experiments. As can be
seen, central stimuli arriving 'iate" were very much more dis-
ruptive than identical perturbations arriving early. This suggests

274

Information Storage and Neural Control

TABLE III

Effects of Electrical Stimulation of Visual Cortex at

Various Delays After Presentation of Four per Second Flash

From Peripheral Tracer Conditioned Stimulus

{2.8 mA, 100/cps. Biphasic, 2 mS Pulse Width, 25 mS Duration)

CR No CR

Delay

15 mS
80 + no mS

Cl<

No CR

16
2

12
26

ro 7

No CR

34
65

X2 =11.7
p < .001

X2 = 15.1
p < .001

X2 =25.7
p < .001

that the processing of information about the peripheral conditioned
signal is at a more crucial stage in the visual cortex, or at the site
to which the central stimuli propagate, during the late phase of
the cortical evoked potential than during the early phase. Examina-
tion of average response computations from various brain structures
in this animal suggests that the late phase of the cortical average
response waveform varies in form and latency with the average
response seen in reticular formation and centralis lateralis.

SUMMARY AND CONCLUSIONS

Diverse kinds of evidence have been presented here both to
illustrate the nature of research in progress and to evaluate a body
of data. Although the number of animals for which each of these
kinds of data has been obtained is as yet small, the consistency and
the clear statistical significance of these intensive studies seem to
warrant some consideration at this time.

Findings have been reviewed which show a correlation between
certain electrophysiological phenomena and differential condi-
tioned behavior. Results have been presented from a number of
studies primarily designed to explore two hypotheses based on
this earlier work: 1) The configuration of labeled potentials in

Neural Mechanisms of Decision Making 275

these situations reflects the coding and processing of information
as the brain performs differentiated conditioned responses to two
intermittent photic stimuh differing in frequency; and 2) the
estabhshment and performance of such differentiated behaviors
involve the measurement of similarity between past experience,
as reflected primarily in the neural activity of nonspecific regions
of the brain, and present stimulus configuration, as refiected
primarily by the specific sensory systems of the brain.

The observations of "assimilation of the rhythm" which have
been reported by many workers suggest that the brain has the
capacity to reproduce previously experienced patterns of neural
activity. Manifestation of such endogenously generated patterns
is marked in nonspecific systems. During generalization, behavioral
performance seems to be accompanied by departures from stimulus-
bound response, notably in the cortex of the relevant sensory
modality and in the reticular formation. Computer analysis of
waveforms from various structures during such behavior shows
that these two regions cHsplay clear evidence of endogenously
generated coinponents appropriate to the behavior, while other
regions of the brain respond to the stimulus more accurately.
Analogous observations have been made when cfifferentially
trained animals commit errors.

These data, which are compatible with the hypotheses, are
contradicted by the failure to elicit erroneous performance dif-
ferentially as a consequence of central stimulation at a frequency
discordant with the frequency of a concurrent peripheral con-
ditioned stimulus. It has, however, been demonstrated that
temporal patterns of excitation at a site can serve as coded infor-
mation for the brain. Evidence has also been presented indicating
that a crucial step in cortical data processing may take place at
the time when influences arrive from the nonspecific system.

To date, therefore, we have not succeeded in establishing an
unequivocal functional role for labeled potentials as direct reflec-
tions of data processing in the brain. However, an increasing and
consistent body of evidence does seem to support the view that a
cortical-reticular interaction is an important component in the
evaluation of incoming information in the context of past ex-
perience.

276 Information Storage and Neural Control

The rapid rate of technical progress in this problem area gives
us good reason to hope that further clarification will shortly be
forthcoming.

REFERENCES

1. Corning, W. C, and John, E. R.: Effect of ribonuclease on retention

of conditioned response in regenerated planarians. Science, 134:
1363, 1961.

2. Dingman, W., and Sporn, M. B.: The incorporation of 8-azaguanine

into rat brain RNA and its effect on maze learning by the rat;
an inquiry into the biochemical basis of memory. J. Psychiat. Res.,
7.-1-11, 1961.

3. Eiduson, S., Geller, E., and Beckwith, W.: Some biochemical cor-

relates of imprinting. Fed. Proc, 20:345, 1961.

4. Glickman, S. E.: Perseverative neural processes and consolidation

of the memory trace. Psychol. Bull., 58:218-233, 1961.

5. Hyden, H.: Biochemical changes in glial cells and nerve cells in

varying activity, in Proc. Fourth Internal. Congr. Biochem., Vienna,
1958, J;64-89, 1960.

6. John, E. R.: High nervous functions: brain functions and learning.

Ann. Rev. Physiol., 2J.-451-484, 1961.

7. John, E. R.: In, Theories of the of Mind, ed. by J. Scher. Glencoe,

lUinois, Free Press, 1962. (in press)

8. John, E. R., and Killam, K. F.: Electrophysiological correlates of

avoidance conditioning of the cat. J. Pharmacol. & Exp. Ther.,
125:252-274, 1959.

9. John, E. R., and Killam, K. F.: Electrophysiological correlates of

differential approach-avoidance conditioning in cats. J. JVerv.
Ment. Dis., 737.-183-201, 1960.

10. John, E. R., Leiman, A. L., and Sachs, E.: An exploration of the

functional relationship between electroencephalographic potentials
and differential inhibition. Ann. New York Acad. Sci., P2.-1160-
1182, 1961.

11. Kreps, E., cited by: Palladin, A. V., and Vladimirov, G. E., in

Proc. Internal. Conf. Peaceful Uses Atomic Energy, vol. 12, 1955.

12. Liberson, VV. T., and Ellen, P.: In, Recent Advances in Biological

Psychiatry, ed. by J. Wortis, New York, Grune and Stratton, 1960.

13. Livanov, M. N., and Korolkova, T. A.: ^hur. Vyss. Nerv. Delal.

Pavlova, vol. 1, J.-332-346, 1951; as reported in Rusinov, V. S.,

Neural Mecha7iisms of Decision Making 277

and Rabinovich, M. Y., Electroencephalographic researches in
the laboratories and clinics of the Soviet Union. Electroenceph. Clin.
NeurophysioL, supp. 8, 1958.

14. Livanov, M. N., and Polyakov, K. L.: Bull. Acad. Sc. USSR, 3:

286-307, 1945: as reported in: Rusinov V. S., and Rabinovich,
M. Y., Electroencephalographic researches in the laboratories and
clinics of the Soviet Union. Electroenceph. Clin. NeurophysioL, supp.
8, 1958.

15. Loucks, R. B.: In, Electrical Stimulation of the Brain, ed. bv D. E.

Sheer, Austin, University of Texas Press, 1961.

16. Majkowski, J.: EEG and EMG pictures of differentiation of con-

ditional reflexes. Acta Physiol. Pol., 9.-565-581, 1958.

17. Morrell, F.: Some Electrical Events Involved in the Formation of

Temporary Connections. In Henry Ford Hospital International
Symposium, Reticular Formation of the Brain. Boston, Little, Brown
and Co., 1958.

18. Morrell, F.: In, symposium, Council for International Organization

of Medical Sciences. Brain Mechanisms and Learning, ed. l^y J. S.
Delafresnaye, Oxford, Blackwell Scientific Publications, 1961.

19. Morrell, F.: Electrophysiological contributions to the neural basis

of learning. Physiol. Rev., 4l:AAl)-A9A, 1961.

20. Morrell, F., Barlow, J., and Brazier, M. A. B.: In, Recent Advances

in Biological Psychiatry, ed. by J. Wortis, New York, Grune and
Stratton, 1960.

21. Morrell, F., and Jasper, H. H.: Electrographic studies of the for-

mation of temporary connections in the brain. Electroenceph. Clin.
NeurophysioL, 5.-201, 1956.

22. Neff, W. D., Nieder, P. C, and Oesterreich, R. E.: Learned response

elicited by electrical stimulation of auditory pathways. Fed. Proc,
vol. 18, supp. 3, item 442, 1959.

23. Nieder, P. C, and Neff, W. D.: Auditory information from sub-

cortical electrical stimulation in cats. Science, 7JJ.-1010-1011, 1961.

24. Stern, J. A., Ulett, G. A., and Sines, J. O.: In, Recent Advances in

Biological Psychiatry, ed. by J. Wortis, New York, Grune and
Stratton, 1960.

25. von Foerster, H.: Das Gedachtnis, Vienna, Deuticke, 1948.

26. Weiss, M.: Unpuljlished master's thesis. College of Engineering,

University of Rochester, Spring 1962.

27. Zuckermann, E.: Effect of cortical and redcular stimulation on con-

ditioned reflex acdvity. J. NeurophysioL, 22:633-643, 1959.

278 Injormation Storage and Neural Control

DISCUSSION OF CHAPTER XI

Frank Morrell (Palo Alto, California): I would like to ask
two questions, Dr. John, both relating to your central theme.
In the experiment dealing with equivalence of central stimulation
and a peripheral signal you found that highly trained animals
punished for errors did not transfer. Do you think that the "equiv-
alence'' you demonstrate represents generalization rather than
transfer and actually serves to prove the nonequivalence of central
stimulation and the peripheral signal?

On a more theoretical plane, do you think that a representational
system which stores information in the form of an ongoing pulse
code is an efficient method for storage, or even for comparison?
And since it is clear that animals can distinguish frequencies in
the visual, somatic or auditory modalities which are far above
the range of EEG rhythms, is it necessary to postulate an entirely
different coding mechanism for temporal sequences beyond the
limited range within which a translation in terms of brain wave
response is possible?

E. Roy John (Rochester, New York): I believe your first
question is directed at whether information might be encoded as
a wavefoim and stored by a mechanism which could reproduce
waveform. Clearly, at some level there must be a common domain
of discourse between information about immediate experience and
representation of past experience. Recognition of an input requires
such an interaction. As I stated in the beginning, there are various
possible ways that this might be accomplished. Some workers
have suggested neural filter networks, structured by experience,
such that passage of an input constitutes identification. It is not
clear how such throughput is to be related to the experience
which stipulated the filter characteristics. If a filter stands for
experience A and permits passage of an impulse when experience A
occurs, no mechanism has been proposed which would assign to
that passed impulse the content of experience A.

Other workers have suggested an intracellular macromolecular
device functioning almost as a tape recorder to register experience.
To my knowledge, no mechanism has been proposed which
would achieve "playback"' from these molecular recordings. One
can conceive of networks which would accomplish coincidence

Neural Mechanisms of Decision Alaking 279

detection, in whicli things could be compared in the same coin.
I believe that the coin in such networks might be the spatio-
temporal distribution of electrical activity.

It is at this point that your second question becomes relevant.
You are really asking, "Can such encoding be possible for any-
thing other than the very artificial situation which we have
devised? Can the temporal pattern of electrical events really be
suggested for the representation of stimulus configurations which
are not characterized by particular frequencies of events? Could
a temporal pattern of neural potentials represent stimulus fre-
quencies above the range of EEG rhythms?" I think this is clearly
possible. One could conceive of spatio-temporal transforms so
that a characteristic distribution of simultaneous events in dif-
ferent regions of the brain would generate a characteristic se-
quence of temporal events at soine loci. Spatial distributions
can be transformed to temporal patterns, and temporal patterns
can be transformed to spatial distributions. Projection pathways
of different lengths and diflferent propagation velocities could
conceivably project a characteristic representational temporal
pattern which would correspond to the distribution of simultaneous
excitation in anatomically dispersed areas of a neural population.
Representational patterns need not be isomorphic with that which
they represent.

Your question also relates to parsimony, which I do not con-
sider to be a law of nature, but which is a help in the intuitive
ordering of probabilities. Admittedly, the conditions which we
use in our experiments are artificial, and deliberately so. Cats
live in a world containing more information than simply the
frequencies of flickering lights. We hope that this artificial situ-
ation might give us some insight into the processing of information.
However, once one reaches the conclusion that information about
this carefully constrained and defined environment may be handled
by mechanisms related to the temporal patterning of electrical po-
tentials, a problem arises. If one wants to postulate a diff'erent mech-
anism for coding other kinds of sensory information, one has
introduced chaos into the nervous system. Rapid and accurate
integration seems more compatible with a system which codes
all data in one language and decodes it in the same tongue than

280 Information Storage and Neural Control

with a system which uses a different language for each kind of
message. I cannot assert that information about diverse events
is necessarily coded in the same way, but I would prefer to test
that hypothesis rather than to accept a doctrine of specific message
languages. I have presented evidence which suggests that the
temporal pattern of macropotentials may be related to the coding
of information about flicker frequency. Were I convinced that
this were the code for this carefully specified stimulus, I would
be inclined to suggest temporal pattern of potential as the most
probable code for other sorts of stimuli.

I would like to take advantage of this opportunity to ask you
a question about your paper this morning, which is not unrelated.
Certain aspects of the model which is implicit in what you said
seem to pose appreciable difficulties. If I understand you, you
suggested a memory based on the specification of protein sequences
by ribonucleic acid. You did not touch on the question of how
to obtain a readout from this memory. To use your terms, are you
sure that such a memory would be more efficient than a memory
which would operate as follows: The spatio-temporal distribution
of activity caused by a stimulus in an extensively interconnected
network of cells is such that, due to local micro-environments,
characteristic interspike intervals, fiber diameters and distances
between elements, etc., there is some population of cells for which
re-entrant pathways exist such that a reverberation can be sus-
tained to a given stimulus configuration. Maintaining this rever-
beration for a sufficient time, which might be the duration of the
consolidation period, might accomplish a change in macro-
molecular synthesis. As the average intracellular electrolyte con-
centration was altered by sustained reverberatory activity, a
change might occur in the carbon-to-carbon bond angle of RNA,
thus altering the distance between purine and pyrimidine bases.
Such changes in the spacing of the template would alter the amino
acid species which would fit in that place. The concentration of
the appropriate amino acid in the environment would determine
the probability that the appropriate fit would be made. The rate
of assemblage of a protein would depend on the achievement of
the appropriate concatenation of amino acids on the template.
Therefore, since the concentrations of various amino acids in the

Neural Mechanisms of Decision Making 281

cell differ, such ion-induced changes in template might accom-
plish increases or decreases from the usual rate of protein synthesis.

In this view, macromolecular specificity is relevant only insofar
as the control of synthesis rate is concerned. The synthesized
material, no matter what its specific configuration, simply binds
charge. The presence of bound charge in some cellular regions
causes an inhomogeneous distribution of diffusible ions. The pro-
portion of diffusible to bound charge need not be the same every-
where within a cell nor from cell to cell. Consequently, the
equilibrium concentration for diffusible potassium is altered, the
rate of restoration of membrane polarization after discharge is
variable, the RC constants are different, and the interspike
intervals are different. By such a mechanism, this population of
cells which sustains reverberation could be shifted away from the
population mean with respect to the interspike interval and
become isolated.

A mechanism of this sort seems to provide a way to get around
some of the severe problems which face a memory mechanism
based on protein specificity. For example, if memory were a
particularly specified macromolecular configuration, what would
protect it once it was built? Further, if RNA structure is at the
mercy of every influence which impinges on the cell, if you can
make many kinds of RNA depending on the afferent stimulus
configuration, then how does a cell sustain the enzymatic activity
necessary for its survival? Why assume that you can make any
kind of RNA or any kind of protein or enzyme? Why not assume
that the cell can make only stipulated kinds of macromolecules
with a facilitation mechanism of the sort I suggested regulating
the amounts of each? Such macromolecules could play many
roles, including that of binding charge. There is no specific macro-
molecular sequence which requires protection to preserve memory.
One cannot prevent the cell from being excited to enable memory
to be preserved. Why not assume that the protection is achieved
by randomness?

A memory of the sort I describe here could be perturbed only
by a sustained nonrandom influence which impinged on a sub-
population of the coupled assemblage of cells mediating a memory,
and only if that influence were sustained long enough to alter

282 Information Storage and Neural Control

the rates of macromolecular synthesis. Global nonrandom, as well
as random influences, and localized random influences would
have no eff"ect on the relative interspike intervals of the ensemble
of cells. All messages are encoded in the same language in such
a scheme. I see no sort of information which could not be coded
by a spatio-temporal pattern of this sort. The memory readout
which would result from the simultaneous activation of a large
enough proportion of the cells in the assemblage to sustain itself
and propagate via re-entrant pathways would generate a spatio-
temporal distribution of electrical activity quite comparable to
the readin. The resting memory here is not a reverberation, but a
set of structurally mediated temporal relationships which generates
a reverberation only when the ensemble is excited. Would a
scheme of this sort seem to meet more of the constraints with
fewer ad hoc hypotheses than the sort of mechanism which you
had in mind?

Morrell: I still do not see how your supposition does away with
the notion that the critical factor is the distribution of charged
sites available for bonding.

John: I think there is an essential difference. Your code is the
specification of sequence on a molecule.

Morrell: Only in a limited sense. For example, a possible
alteration might involve only the exchange of glutamic acid for
glutamine at a specific site. Perhaps potassium is involved as well.
We cannot say. All one can reasonably suggest is that a change
in charge distribution on an impermeable molecule would be
necessary to influence the ionic environment permanently.

John: There would be many ways to accomplish the binding
of ionic charge which did not require the specification of sequence.

Morrell: Well, to put your question back to you, would not
the readout from such a system be a temporal pattern of cell dis-
charge in space?

John: Yes, the readout would be a spatio-temporal pattern
of cell discharge, but it could be a much smaller space than the
set of neurons initially excited by a stimulus.

Morrell: Oh yes, there would be a difference.

CHAPTER
XII

ANASTOMOTIC NETS COMBATING NOISE*

Warren S. McGulloch, M.D.

E INHERITED from Greek medicine a recognition that
knowledge depends in some manner upon a mixture of a knower
and the known. The Fathers of Medicine supposed that this mixing
took place locally in the anastomotic veins and was carried by
the blood to the general mixture in the heart. Except for a few
chemical messengers like hormones, we have abandoned this
cardiocentric theory of knowledge for a cephalocentric one. We
have replaced their mixture of substances with an interaction of
signals, but have retained the essentially anastomotic quality of
the net. In fact, we conceive our nervous system to be so anasto-
motic that every efferent peripheral neuron can be affected over
a multiplicity of paths by every afferent peripheral neuron.

For the purposes of this paper, I shall ignore all other sources
of reliability in the process of perception. I mean such things as:
1) closed loops of reflexive and regulatory mechanisms; 2) use of
topological mapping to preserve local sign; 3) redundancy of code
that is inherent in the repetition rate characteristic of those nervous
structures that determine posture and motion; and 4) autocor-
relative functions of the cerebellum that are used to raise signals
out of a background of noise.

I shall say nothing about evolution, adaptation, learning, or
repair. My reason is this: The nervous system is state-determined;
that is, at any one time its change into another state is determined

*This work was supported in part by the U.S. Army Signal Corps, the Air Force
Office of Scientific Research, and the Office of Naval Research; in pai-t by the National
Institutes of Health Grant B-1865, (C3); and in part by the U.S. Air Force, Aero-
nautical Systems Division, under Contract AF33(616)-7783.

283

284 Information Storage and Neural Control

only by the state in which it is and by the input to that state.
Consequently, we do not care how it came to be in that state.

For our problem of the moment, perception, we shall deal only
with essentially synchronous signals to a layer of neurons whose
axons end on the succeeding layer, for as many layers in depth as
we choose. Such a net can be designed to compute in any layer
at any one time as many Boolean functions of its simultaneous
inputs as there are neurons in that layer, and no others. We shall
not consider any other nets. We shall suppose that our nets have
been designed so that functions computed by the output neurons
lead to those responses that are most useful to the organism. This
assumption simplifies our problem.

Some twenty years ago, when Walter Pitts and I began our
study of a logical calculus for ideas that are immanent in nervous
activity, there was good evidence that a neuron had a threshold
in the sense that it would fire if adequately excited; that impulses
from separate sources, severally subthreshold, could add to exceed
the threshold; and that the neuron could be inhibited. For sim-
plicity, we took inhibition as being absolute. These few properties
served our purpose, which was to prove that a net of such neurons
could compute any number that a Turing machine could compute
with a finite tape. Some five years later, these properties sufficed
for a theory of how we can perceive universals, such as a chord,
regardless of key, or a shape, regardless of size. These two papers
were crucial in the development of Automata Theory.

But, ten years ago the inadequacy of these assumptions came to
light, theoretically, in von Neumann's paper on probabilistic logic
concerned with building reliable computers from less reliable
components.

By that time spontaneously active neurons had been demon-
strated in most parts of the mammalian nervous system. Inhibitions,
like excitations, had been found to sum, and we had come to grips
with those interactions of axons that are afferent to a cell and by
which signals in one prevent signals in another from reaching the
recipient neuron.

We could demonstrate this interaction as peripherally as the
primary bifurcation of afferent peripheral neurons. In Nature

Anastomotic Nets Combat mg Noise 285

(January 6, 1962), E. G. Gray has published the first electron
microscopic anatomical evidence of axonal terminations upon
boutons of other axons, which may account, as proximally as
possible, for the interaction.

Interaction of afferents is of great theoretical importance. First,
it enables a neuron to compute any Boolean function of its inputs,
i.e., to respond to a specified set of afTerent impulses, not merely
those functions available to so-called threshold logic; and, .second,
it permits a neuron to run tlirough all possible sequences of func-
tions as its threshold is shifted.

The first is of great importance in audition. The Boolean func-
tion is an exclusive OR, and the important cells are in the superior
olive. Each cell will respond to an impulse from either ear unless
there is one from the other, but never to both or neither. The
utility of this arrangement is obvious to anyone with wax in one
ear. Put on a pair of earphones with a beep in one ear and drown
it 10 decibels under with noise. Next, put the same noise into the
other ear also, and the beep is as loud and clear as it is without
the noise. Finally, put that beep into the other ear also and it
disappears, for it is 10 decibels below the noise. Please note that
this noise is external to the central nervous system and is not the
kind that we shall consider later.

The second, or sequence of functions determined by shifting
threshold, is of great importance in respiration but is not so easily
stated. As nearly as I can tell from old experiments and from the
literature, the rise in threshold to electrical stimulation that is due
to ether is approximately the same in all neurons; yet the respira-
tory mechanism continues to work under surgical anesthesia when
the threshold is raised, at least in cortex and cord, by approxi-
mately 200 per cent. The input-output function of the respiratory
mechanism remains reasonably constant, although the threshold
of its component neurons has changed so much that each is com-
puting a different function (or responding to a diflTerent set) of
the signals it receives. Von Neumann called such nets "logically
stable under a common shift of threshold," and Manuel Blum has
cleaned up the problem for appropriate nets of neurons with any
number of inputs.

286

Information Storage and Neural Control

To explain this, I would like to introduce to you the only
symbols with which I have been able to teach the necessary
probabilistic logic. I use a X with a jot for true, a blank or 0
for false, a dash for "I don't care which," and a p for a 1 with
probability p. For "A alone is true" {i.e., a sign for A alone), X;
for B alone, X; for both, X; and for neither, X. Then I can write
the sixteen logical functions, or firing diagrams, of a neuron with
two inputs, as shown in Figure 1, and we can draw the diagrams,
as in Figure 2, to show how the computed function depends upon
the threshold 9.

X 'X X X' 'X ^ X- 'X' X 'X ^ X* 't, X' X' '^

Figure 1

A B

+2\fKVl

\^2

1 X

-IX

-1 X

-2X

-IX

-2X

-3X

■X

Figure 2

You will note that the first four neurons, without interaction of
afferents, compute all but two of the sixteen logical functions,
and these missing ones can be computed by the two neurons at
the right in Figure 2. The upper right neuron does the trick in
the superior olive.

Anastomotic Nets Combating Noise

A B

287

Figure 3

To explain respiration, we now use a net of three neurons
(Fig. 3), and suppose that we want [X],

[1] [(X) X (X)]

or.

[2]

[(X) X (X)] = [X]

and compute it as in Equation 1, and tlien decrease every 6 by
one so as to compute tlie same [X]. Now every component is com-
puting a new function of its input; hence, this net is logically
stable under common shift of 6 over a change of one step. If we
were to carry it a second step, we would have a net that always
fires or never fires.

The maximum range for neurons with two afferents is clearly
two steps, but it can only be achieved by nets with interaction of
afferents, and then it can be achieved always and for any number
of afferents per neuron. For example. Figure 4 shows these expres-

Information Storage and Neural Control

(•^)^-(^)>[X.

(X-)-X(X)>LaJ

(X)X(;^)>

—

Figure 4

Figure 5

sions for a net of three neurons whose output neuron goes through
X and requires interaction, as does the left-hand neuron. Obviously
no more is possible, for the output would always or never fire.

One more trick served by interaction is the use of separate
shifts in d that are produced by feedback to secure flexibility of
function. Consider the net of Figure 5 in which the feathered
arrows indicate feedback affecting 0's. This net can be made to

Anastomotic Nets Combating Noise 289

compute fifteen out of the sixteen possible functions. Had I drawn it
for neurons with three inputs each, it could have been switched so as
to compute each of 253 out of the 256 logical functions of three
arguments. I strongly suspect that this is why we have in the eye
some 100 million receptors and only approximately one million
ganglion cells, but note that it depends upon interaction of afferents.

Finally, Manuel Blum has recently proved that this interaction
enables him to design nets that will compute any one specified
function of any finite number of inputs with a fixed threshold of
the neuron at a small, absolute value, say, 1 or 0. This prevents
the neuron from having to detect the small difference of two large
numbers, thus allowing the brain a far greater precision of response
to many inputs per neuron, despite a fluctuation of a given per
cent of the threshold 6. This fluctuation of 6 is the first source of
noise which I wish to consider.

The effective threshold of a neuron cannot be more constant
than that of the spot at which its propagated impulse is initiated.
This trigger point is a small area of membrane, with a high
resistance, and it operates at body temperature. It is, therefore,
a source of thermal noise. The best model for such a trigger is
the Node of Ranvier, and the most precise measurements of its
value are those of Verveen. For axons '■^A^ in diameter, he finds
it to be ^^ ±1 per cent of 0; it is larger for small axons. Moreover,
his analysis of his data proves that the fluctuations have the random
distribution expected of thermal noise. There are, of course, no
equally good chances to measure it in the central nervous system,
for one cannot tell how much of a fluctuation is due to signals or
to stray currents from other cells. Our own crude attempt on the
dorsal column of the spinal cord indicates far greater noise, but
not its source.

What goes for thresholds goes, of course, for signal strength;
and for fine fibers, say, 0.1^, the root mean-square value of the
fluctuation calculated by the equation of Fatt and Katz is —0.5 mv.
If we accept a threshold value of 15 mv., this is several per cent.
It may be much larger.

Moreover, it is impossible that the details of synapsis are per-
fectly specified by our genes, preserved in our growth, or perfected
by adaptation. They are certainly disordered by disease and injury.

290

Information Storage and Neural Control

A B

Figure 6

Nevertheless, it is possible to cope with these three kinds of
noise — 0, signal, and synapsis — as long as the output of a neuron
depends, in some fashion, on its input by an anastomotic net to
yield an error-free capacity of computation. This is completely
impossible with neurons having only two inputs each. The best
we can do is to decrease the probability of error. Consider, for
example, a net like that of Figure 6 to compute [X], where each
neuron is supposed to have 0=3, but each drops independently
to 2 with a frequency p. As long as p is less than 0.5, the net
improves rapidly as the product of the p's of successive ranks
decreases. The trick here is to segregate the errors.

The moment we look at neurons with three inputs, the picture
changes completely; but to describe this change we need to increase
the complexity of our logical symbols by putting a circle on the X,
so that inside it is C, outside not C, as in Figure 7(a). Now con-
sider a net to compute some function, say, all or else none. We
can schematize this, as in Figure 7(b). The dash is a "don't-care"
condition; it may be a 1, or 0, or any p that you choose. This net
makes no mistakes. Let us suppose that each of the first rank

Anastomotic Nets Combating Moise

291

NONE

ALL OR NONE

(a)

ABC

(b)

(c)

Figure 7

292

Information Storage and Neural Control

exerts +2 excitation on the third. Then its threshold can vary
harmlessly: 3 < 0 < 6, or nearly 50 per cent. Moreover, if the
threshold is better controlled, then the strength of the signals can
vary. Finally, if both are fairly well controlled, the connections
can be wrong, as in Figure 7(c), and the input-output function
[S5] is still undisturbed.

If we want to extend our symbols to four arguments, then the
pattern becomes that of Figure 8, and for five arguments it becomes
more complex. In general, each new line must divide all existing
areas into two; thus for N inputs there are 2 spaces. Oliver Selfridge
and Marvin Minsky have worked out simple ways of making such
symbols, with sine waves, for any finite number of inputs.

Eugene Prange has invented a way of devising the distribution
of don't-care conditions so that there are as many as possible for
a net of N neurons in the first rank and one in the output rank,
each rank having N inputs per neuron. The number of don't-care
conditions, or dashes, depends upon the number of ones in the
spaces for the function to be computed. The dashes are fewest
when the function to be coinputed has exactly one-half its spaces
filled with ones. Manuel Blum has solved the following questions:
1) Suppose that there are no don't-care conditions, or dashes, in
the symbol for the output neuron; what fraction of the spaces for
each neuron of the first rank can have dashes and the calculation
be error-free for the toughest function (half-filled with ones), all
as a function of N?; 2) With all those dashes in the first rank, what

1.0

0.5

^""'

Figure 8

Figure 9

100

Anastomotic Bets Combating Noise 293

fraction of the spaces in the symbol of the output neuron can
harmlessly be dashes? Figure 9 is Blum's diagram, based on
equations that are exact if N is a perfect square and fairly good
approximations for the rest.

You will notice that for N less than 40, the output neuron (the
solid line) has fewer dashes. At '^40 they are equal, being ^^80
per cent of all spaces. For larger N, the output neuron has the
larger fraction; and, for N = 100, 90 per cent of spaces in the
input rank are dashes, and 98 per cent in the output neuron
are dashes. From this outcome, it is very clear that the output
neuron cannot be a majority organ like the one for N = 3.

We all know that real nervous systems and real neurons have
many other useful properties. But I hope I have said enough to
convince you that these impoverished formal nets of formal neurons
can compute with an error-free capacity despite limited pertur-
bations of thresholds, of signal strength, and even of local synapsis,
provided the net is sufficiently anastomotic. If I have convinced
you, it has been in terms of a logic in which the functions, not
merely the arguments, are only probable. But even this prob-
abilisitic logic, for all its don't-care conditions, is adequate to
cope fully with noise of other kinds. Our neurons die — thousands
per day. Neurons, when diseased, often emit long strings of im-
pulses spontaneously and cannot be stopped by impulses from any
other neuions. And, finally, axons themselves become noisy, trans-
mitting a spike when none should have arisen or failing to transmit
one that they should have transmitted.

To handle these problems in which the output of a neuron has
ceased to be any function of its input, von Neumann proposed
what is called "'bundling.'"' In the simplest case, one replaces a
simple axon from A by two axons in parallel. This alters the logic,
for now if all fibers in the bundle fire, A is regarded as certainly
true; if none fire, as certainly false; but between these limits there
is a region of uncertainty — call it a set of values between true and
false. In the simplest case, there are two such intermediate values.
Von Neumann found that if there are only two inputs per neuron,
the neurons had to be too good and the bundles too big. To com-
pute, say, X or ¥, with a net constructed like the net of Figure 10,
we find that given a probability of an error on the axon, say.

294 Information Storage and Neural Control

A B

Figure 10

€ = 0.5 per cent, to have the bundle usably correct all but once
in one million times, he needed 5000 neurons and two more ranks
of 5000 to restore his signal so that it was usable. His difficulty
was chiefly the poverty of the anastomosis. We have found that,
with the same e and the requirement that the bundle be usably
correct all but once in one million times, if each axon is connected
to every neuron, we only need one rank of 10 neurons.

Leo Verbeek has looked into the problem of the death and
fits of neurons, and has found that again the probability of an
erroneous output decreases as the number of inputs per neuron
and the width of the first rank (both 5 in number) increase, at
least for probabilities of death and fits reasonably under 50 per
cent. Figure 11 shows his graph, where 5 is the number of inputs,
p the probability of error in the input neurons, and a:s(p) the
probability of erroneous output. Even for a small 5, these calcu-
lations are enormously laborious.

We are all much indebted to Jack Cowan for our knowledge of
many-valued logic for handling bundling, and for conclusive
evidence that this is not the cleverest way to obtain reliability.
He and Sam Winograd have made a much greater contribution,
which I could not expound to you if I wanted to, and I do not
because it will probably be communicated in full by Professor
Gabor for publication in the Philosophical Transactions. Vaguely,
its purport is this:

Anastomotic Nets Combating Noise

295

0.4 -

0.2 -

In the theory of information concerned with communication,
there is a theorem, due to Shannon, that, by proper encoding
and decoding, if one transmits at something less than the capacity
of a noisy channel, one can do so with as small a finite error as
one desires by using sufficiently long latencies. Except for things
like X and X, no one before Cowan and Winograd was able to
show a similar information-theoretic capacity m computation.
They have succeeded for any computation and for any depth of
net, limited only by the reliability of the output neurons. The
trick lay in a diversification of function in a net that was sufficiently
richly interconnected. Their fundamental supposition is that with
real neurons the probability of error on any one axon does not
increase with the complexity of its neuron's connections. The
recipients of most connections are the largest and, consequently,
the most stable neurons. Again, it is the richest anastomosis that
combats noise best.

ACKNOWLEDGMENT

I wish to acknowledge the contributions of those who have
worked with me in this endeavor, namely: Anthony Aldrich,
Michael Arbib, Manuel Blum, Jack Cowan, Nello Onesto, Leo
Verbeek, Sam Winograd, and Bert Verveen.

296 Information Storage and Neural Control

DISCUSSION OF CHAPTER XII

Bernard Saltzberg (Santa Monica, California): In the head-
phone experiment, I assume you used a single noise source which
divided its power between the earphones. Was an experiment
attempted with two independent noise sources? How did the
results come out?

Warren S. McCulloch (Cambridge, Massachusetts) : It does not
help much. It has to be the same noise. Different noise is no good.
What they were trying when I was last involved was lagging one
earphone a little behind the other to see what phase difference
they could make in it and still have it work. As far as I know, this
has not been cleaned up yet.

Gregory Bateson (Palo Alto, California): What is the price
of this increased reliability in terms of loss of educability? Obviously,
to obtain a new function — a new relationship — out of this net, you
have to alter a large number of connections. In a sense, I suspect
that the more reliable your new constructions, the more non-
educable the net becomes; but I am not a good enough logician
to know that this is so.

McCulloch: Look at the flexibility end of it. We have here a
neuron with a couple of inputs (A and B) and one output neuron.
Let us take the case of three neurons. Incidentally, I cannot build
this without the interaction of afferents. I have one output neuron.
Now I can send signals back from the central nervous system and
tell my eye what it is to look for, what it is to see. You get 256
possible logical functions. You can calculate 253 of them by giving
these first rank neurons a nudge on the threshold. Reliability
does not mean that the net is inflexible. This is a remarkably
flexible device. The flexibility goes up with the anastomosis; it
does not go down. That is one of the beautiful things about it.
If it was simple majority logic, the situation would be impossible.
The stupidest thing to do, so to speak, if you want to get the
maximum life out of a rope is to use it until it breaks and then
replace it with another one. No mountain climber that I know
takes such a chance. The next worse thing is always to stretch
two ropes from man to man. What you want is the richness of

Anastomotic Nets Combating Noise 297

connections. The dynamics of the picture is beginning to show up,
but the matliematics is too comphcated for us as yet.

Eugene Pautler (Akron, Ohio) : What type of detector would
be required to recognize the results of this output — the computa-
tions inherent in this output neuron?

McCulloch: I think it is probably all done in the eye. Suppose
you tell your eye to look for four-leaf clovers. You simply send
out the message, "Find a particular pattern in those leaves"; and
when you have found it signal, "Here is one! Here is another!"
You knew what you were looking for so you set your filter ac-
cordingly.

A frog, when he jumps, sends back impulses to his eye to give
as great a response as possible to an affair of lesser curvature or
greater radius of curvature, which informs his eye. This works
during the first part of the jump while his eyes are open. One
tells one's eye what to see, what too look for. It would be almost
unthinkable that otherwise one could go into, say, Grand Central
Station, look off across the hall, and, knowing that there is a
chance of so-and-so being there, find him, unless one has in some
manner set a filter. Just how much of that matching is done
in the eye, I do not know. The mouse, which does not turn its
eyes and keeps them open, is another nice animal to work on.
His retina is the same all over, and whether you get a response
from a particular ganglion cell or from a particular axon depends
upon whether the mouse is hungry or whether it has smelled its
cheese. If it has, then it bothers to look, but it will not look the
rest of the time. The mouse shows very little response to any
visual stimulus. The situation is far too complicated to be solved
with a set of electrodes.

Homer F. Weir (Houston, Texas): In the use of the injured
neuron, you are apparently producing noise from non-input
sources. Is it correct to say that your injured neuron is putting
out output without input?

McCulloch: Yes. Either it is doing that or it is dead.

Weir: At what level would this have to occur, relatively speak-
ing, before it would override this protective error mechanism that
you were speaking of?

298 Information Storage and Neural Control

McCuUoch: I have not seen my own cerebellum, but I have
seen that of many a man my age. I am in my second century,
and I expect that at least 10 per cent of the Purkinje cells in my
cerebellum are replaced by nice holes at my age, but I can still
touch my nose. It is incredible how little brain has to be left in
order for it to function.

PART IV — THE HUMAN NERVOUS SYSTEM

Moderator: Wavne H. Holtzman, Ph.D.

CHAPTER
XIII

THE INDIVIDUAL AS AN INFORMATION
PROCESSING SYSTEM

James G. Miller, M.D., Ph.D.

CONSIDERING human beings as information processing" sys-
tems has in tiie last decade proved useful in both experiment and
theory. Some of the hoary old problems of behavior and learning
theory have received a new form or have been bypassed, and some
fruitful approaches to human individual, group, and social be-
havior have arisen.

It has been estimated (1) that in fifty years of waking life an
individual may process 10"^ (ten thousand trillion) bits of infor-
mation. A person may be looked upon as a component in an
interpersonal system in which messages are sent from one node
to another along channels and through nets. As an individual,
he may be studied as a "black box'' whose input-output relation-
ships can be detei mined, or as a system of interrelated components
whose performance and capacities are increasingly available to
experimental investigation.

At the Mental Health Research Institute of The University of
Michigan some of us work within the general systems orientation
which regards all life as a part of the physical space-time continuum.
We consider this continuum to be organized into a hierarchy of
levels of systems, all of which have subsystems and are themselves
subsystems of larger organizations or supersystems. The smallest
living system, the cell, is composed of nonliving molecules. These
may be free-living or may be components of organs, which in
turn are organized into more complex individual systems. These

301

302 Injormaiion Storage and Neural Control

may band into face-to-face groups or larger social organizations
and societies.

There is continuity and there are cross-level similarities in
structure and process at all levels of this hierarchy, even though
there are, of course, at the same time, many specific differences
among individual systems, species, and levels. We have sought
for and found cross-level "formal identities" which can be studied
experimentally.

All living systems are open systems. That is, they maintain
steady states of several variables and counteract entropic dis-
integration by means of inputs and outputs. Living systems at
all levels process both energy and information. These always flow
together. For example, energic inputs such as food convey infor-
mation in the patterning of their molecular structures, and coded
verbal communications are carried on the energy of sound waves.
Energic and informational inputs are distinguished by whether
the receiver responds to their energic or their informational
aspects. Sometimes the response is to both.

SUBSYSTEMS

Living systems at any level require certain crucial subsystein
functions in order to survive, unless they exist in a relationship of
parasitism or symbiosis with another system which supplies them.
Free-living cells, for example, may be shown to have subsystems
that accomplish all the essential functions, while cells which are
part of organs may lack some of them. Groups which survive over
time isolated from other people have all these subsystems while
groups which are parts of organized societies almost never do.
Subsystems may be either local, like the eye, or dispersed, like
the reticuloendothelial system.

There are essential subsystems which deal with the processing
of energy and others which process information. The essential
energy-processing subsystems in the general order of their operation
are: boundary, ingestor, distributor, decomposer, producer, energy
storage, excretor, and mover or output transducer.

The essential subsystems in information processing, listed in the
general order of flow in information processing are:

The Individual as an Information Processing System 303

1) Boundary. This may be the limits of the sense organ of a
cell or animal or the mechanisms of a group or society which
receive information from outside the system.

2) Input Transducer. A transducer changes energy from one
form to another. The sense organ of an animal transduces patterned
energic inputs to nerve impulses. There are analogs at the society
level in the translaters that receive and recode information from
outside the society.

3) Internal Transducer. This subsystem receives and passes
on information from within the system, as the input transducer
does from without. In an animal there is the system of internal
sense organs and chemical sensitivities which activate control
mechanisms. There are analogs at the group and society levels.

4) Channel and Net. The channel is the route — neuron, wire,
air or ether — over which a message is sent from a transmitter to
one or more receivers. In the individual the sensory nerves are
channels over which the input is transmitted to the central nervous
system. Channels may intersect at points called nodes and may
be interconnected to form a net. The nervous system of individuals
is an information processing net. The blood and lymph of the
individual also act as information carrying channels as well as
energy distributors. There are two distinct common uses of the
word "channel."' The more restricted meaning includes only the
flow route for the information, without intervening subsystems
of any other sort (such as transducers, decoders, or encoders).
The other, broader meaning includes such components together
with the intervening flow routes. "Channel" is employed in both
these senses in electronics and little confusion appears to result.
We follow the second usage.

5) Decoder. The decoder alters input information into a code
or language which can be transmitted and "interpreted" inside
the systein.

6) Learner. This subsystem establishes a reliable and enduring
association between certain information inputs and other infor-
mation from outside or inside the system. Thereafter, the system
will make an altered output to an input which previously elicited

304 Information Storage and Neural Control

another response, or no response, or make the same output to a
different input.

7) Memory. This subsystem stores information over time.

8) Decider. A given set of inputs may ehcit two or more
alternate outputs. The decider selects the one that is put into
action. Each of the subsystems of a system is also a system at its
own level and must make its own decisions, as well as carry out
other critical functions. The neuron has the binary decision to
fire or not to fire, which is based upon the strength and charac-
teristics of its inputs and the present state of the neuron. The
individual has a central decision-making subsystem which deter-
mines output for the whole system.

9) Encoder. This prepares information for output by putting
it into a code which can be transmitted to and interpreted by
other systems in the environment.

10) Motor or Output Transducer. The motor in an animal
is the same for both energy and information outputs. Nervous
impulses trigger activities like gross physical movements, speech,
ingestion, or excretion.

11) Reproducer. This is capable of giving rise to other systems
similar to the one in which it is found. We consider it an infor-
mation processing subsystem because its primary activity is
transmission of information or patterning. The reproducer, while
not essential for the survival of the individual, is necessary for
the continuation of the species and all social organizations which
endure for more than one generation.

Each of these subsystem functions is carried out within the
individual, but as we have seen in this symposium, it is not possible
at present to show the precise localization of all of them. The
specific neural arrangements for decoding, learning, memory,
perception, deciding, and encoding, for example, are all being
studied but are not yet understood.

We have emphasized the use of standard centimeter-gram-
second or information theory units, or units which are derivatives
of these, rather than the welter of unrelated measures which
have been used in the different fields of behavioral science. Since
we are looking for cross-level measurable uniformities or dif-

The Individual as an Information Processing System 305

ferences, the quantitative study of tiiese requires the use of com-
parable measures at different levels, and the units of the natural
sciences seem best suited, though, of course, all sorts of phenoinena
cannot yet be expressed in them.

THE ROUTE OF INFORMATION FLOW

Each one of a person's subsystems may participate in the
preparation of the output. Input of appropriate kind and strength
crosses the individual boundary and is transduced into the proper
form for nervous transmission. If a language or code is involved,
it is translated by the decoder and classified by the perceiver in
terms of a perceptual schema which represents the world as the
individual has experienced it. Reference may be made to stored
memories. There may be some recoding or other preparation of
all or part of the input for storage in the memory. On the output
side, a decision is made from among the alternate possible outputs;
encoding for external transmission is carried on, and the nervous
message is transduced into physical response, through either the
speech mechanism or other musculature. There is a large literature
on each of these functions and it is impossible to do more than
give a brief review of some of the material on some of the sub-
systems. Not all input, of course, is channeled through all the
subsystems. A reflex response to an input may involve only a
small number of subsystems. Complex decisions may make use
of the whole range of individual subsystems.

Throughout the system there is a continual and cumulative
loss of information. One important aspect of the response of
biological systems, as both Gerard (2) and Piatt (3) have recog-
nized, is amplification. That is, the energy in the signal is very
small compared to the energy in the response. At the same time
there is a loss of dimensionality from input to output in all am-
plifiers, which must select in order to amplify, since they have
limited power available. There is distortion of information at
each boundary that is crossed, and furthermore, noise alters the
signal. The sense organ reacts only to part of the information
present in the environment. The perceiver screens and organizes
the input further, and in the process ignores that part of it which

306 Information Storage and Neural Control

seems irrelevant. Channel capacity may be lower than the capacity
of the components. When the behavior is organized the central
decision represents only a small part of the original input in-
formation.

OVERVIEW OF RESEARCH ON SUBSYSTEM FUNCTIONS

For an input to cross the boundary into the system its energy
must be great enough to cause the external transducer to fire.
The signal-to-noise ratio also must be sufficiently high. Other
environmental conditions which may influence the permeability
of a boundary to an input are competing signals, and the simi-
larity of the background to the signal — for example, a white
stimulus on a white ground may not be detected. McCulloch gave
an example of this in the experiment he mentioned in his paper
on detecting a signal against monaural and binaural background
noise.

Classical psychophysics in its study of the threshold has tended
to ignore some of the important aspects of signal detectability
or to assume, sometimes incorrectly, that these other things are
held constant. Swets, Tanner and Birdsall (4) have pointed out
that this classical concept of the threshold is unreasonable because
it ignores the control which is exerted by sensory and psychological
variables. That is, it neglects the participation of subsystems other
than the boundary.

The characteristics of human sense organs as input transducers
or internal transducers may be specified just as the characteristics
of electronic transducers: by transfer function, band width, phase
shift, and signal-to-noise ratio. In these respects various sensory
subsystems or modalities perform quite differently.

The transfer function of a transducer refers to its ratio of output
to input. In the visual system this is the relationship between
intensity of light and reported brightness. In the auditory system
it is the relationship between intensity of sound and reported
loudness. Some engineers in designing apparatus for man-machine
systems have mistakenly assumed that the cu'-ve of perceived in-
tensity rises linearly with the increase in strength of the stimulus.
As Stevens has pointed out, the subjective intensity increases as a

The Individual as an Information Processing System 307

power function of the stimulus magnitude. The exponent of this
function for loudness is about 0.3, while it is about 3.5 for the
apparent intensity of electric current applied to the fingers.
Stevens (5) notes that: "In three modalities investigated . . .
transducers . . . have three radically different operating charac-
teristics. The slow growth of loudness (exponent less than one)
suggests that the ear behaves as a 'compressor' . . . This com-
pressor action probably helps to make it possible for the ear to
respond to an enormous range of sound pressures — range of
millions to one. The apparent intensity of vibration on the finger
tip grows almost linearly with vibration amplitude — as though
the transducer were approximately linear. The effective range of
vibration amplitudes to which the finger is sensitive is of the order
of hundreds to one. (Incidentally, vibration on the arm does not
follow a simple power law.) The steep operating characteristic
for electric shock suggests the action of an 'expander' of some
sort; doubling the current increases the sensation about tenfold.
And correlated with this rapid expansion is a narrow operating
range of stimuli of the order of only tens to one."

In input transducers the output signal usually differs from the
input signal in bandwidth characteristics. For instance, light of
different wave lengths and sound of different frequencies are
subjectively reported as various colors and pitches. Sensitivity
over the range of light waves and sound waves is not uniform.

Also input transducers are active over only a limited range.
There are light waves above and below the visible spectrum and
sounds which the human ear cannot hear.

Phase shift refers to the lag in phase of the output signal over
the input signal. Input transducers differ in speed of transmission.
For example, sound waves travel through the atmosphere quite
slowly but are transmitted rapidly through the auditory organ,
while light waves, which reach the eye very speedily, are processed
through a slow input transducer. Input transducers also differ in
the amounts and kinds of noise they insert into the signal.

Channel and Net

Broadbent (6) suggests that the whole individual may be re-
garded as a single channel which performs a selective operation

308 Information Storage and Neural Control

upon the input, stores part of it, filters it, and transmits it over a
limited-capacity channel to long-term storage, to the output trans-
ducer, or to both. Here Broadbent includes all the components of
the individual system in one channel, which is one possible way
to view the system. This results, however, in ascribing to channel
activity some things which we have analyzed as subsystem functions.
Within the channel he analyzes components which filter, store,
decide, and so forth.

Quastler (7) analyzes the activity of specific channels in terms
of speed, diversity, order of complexity, range, and other factors.
Electronics engineers measure in channels the variables of process-
ing time, channel capacity, bandwidth, signal-to-noise ratio, and
phase shift or lag. These can all be usefully applied to the animal
or human being.

The processing time through neurons is brief compared to the
total response time. The duration of neural propagation of an
impulse differs with the length of the channel and the type and
size of the neuron. Longer transmission delays occur at the per-
ceiver and the decider.

Channel capacity is a valuable concept in behavior theory.
Broadbent (8) says: "... perhaps the point of permanent value
which will remain in psychology if the fashion for communication
theory wanes, will be the emphasis on problems of capacity. The
latter, in communication theory, is a term representing the limiting
quantity of information which can be transmitted through a given
channel in a given time . . . the fact that any given channel has
a limit is a matter of central importance to communication engi-
neers, and it is correspondingly forced on the attention of psy-
chologists who use their terms."

Quastler (9) was interested in finding how much information
man can process at best. His research, therefore, was designed so
that neither the visual input nor the muscular output were in
any way hampered. In these tasks all inputs came from a single
source, all output choices were mechanical, and all displays and
operations were thoroughly familiar. He studied rates at which
information is transmitted by reading, typing, playing the piano,
doing mental arithmetic, or assimilating by glancing at displays

The Individual as an Information Processing System 309

of letters, playing cards, scales, or dials. His research was designed
to establish the principal factors limiting performance.

With these experimental conditions, the performances which
were obtained were at peak rates which could have been achieved
only under favorable conditions. Quastler (10) says: "We find
that people can make up to five to six successful associations per
second, can transmit about twenty-five bits per second, can operate
efficiently over a range of about thirty possible values and can
assimilate some fifteen bits at a glance. We do not expect that
they will reach such perfonnance levels with every kind of activity;
in fact, we know that they usually do not.'' In bits per second, he
and his colleagues found peak performances for piano playing of
twenty-two bits; for reading aloud, twenty-four bits; and for
mental arithmetic, twenty-four bits. They concluded that the
peripheral input mechanisms were not responsible for limitations
upon information processing. Quastler (11) notes: "The capacity
of the optic nerve is many orders of magnitude higher than twenty
or forty bits per second ; a much wider range of symbols could be
accommodated with the resolving power of the retina. As to speed
limitations, it is known that about three symbols are grouped in
the act of reading, and that about four such groups can be assimi-
lated in a second; this gives twelve syinbols per second, con-
siderably more than the highest useful speed in typing or piano
playing. On the output side, it is easy to see that the limitations
of the actual speed, both alone and in combination with precision,
cannot be attributed to mechanical difficulties. In all tests, ob-
served speeds would have been much improved by rehearsing.
Thus the mechanisms which limit the observed performance must
be connected with the speed of processing information."

Signal-to-noise ratio can be important in the specification of
channels where minimal energies are involved. Barlow (12) has
shown that the limiting factor in the absolute threshold for vision
is fluctuation in the noise in the visual pathways.

The Decoder

If information is to be used by the individual, it must be suitably
coded. That is, it must be in a language or signal system which
he can understand. Deininger and Fitts (13) have experimented

310 Information Storage and Neural Control

upon the relationship between the input code and performance.
They found that an inefficient or inadequate code can retard the
transmission of information in perceptual-motor performance.
Decoding time, therefore, can make a measurable difference in
information-processing rate. Coding, of course, is important in
the formation of concepts since this involves the classification of
various things under categories which ignore differences among
them and emphasize similarities. Brown and Lenneberg (14) found
that when subjects were asked to name colors as quickly as possible,
the average reaction time was shorter and the degree of agree-
ment among subjects was higher when there was a word which
described the color. When the color had no special name but
had to be called "greenish-yellow," or something like that, there
was hesitation and inconsistency. Their matrix of intercorrelations
yielded a general factor which they called codability. There is a
large literature on semantic problems of coding.

The contributions of the learner to information processing are
both more familiar and less easy to distinguish from other functions
than the more peripheral processes. Some have tried to make
learning theory cover nearly all of psychology. There has been
much research on learning, but little strictly in terms of infor-
mation theory, in which it should be viewed as the associating
of two or more signals.

Competing theories about the memory have been treated in
detail by other speakers in this symposium. Just how information
is stored over time, and how it is searched for, still is not known.

Deciding

Deciding, as we have said, goes on in each subsystem, as well
as at the system level. Much of psychology concerns choices and
judgments of various sorts — psychophysical judgments, sociometric
choices, economic and social decisions, and so forth. Recent work
in game theory, utility theory, statistical decision theory, and group
effects on judgments of their members is clarifying the processes
of complex decisions. In complex reaction-time experiments it is
possible to calculate accurately the amount of time which is added
to the response time when a choice of behaviors is involved. This
time falls to zero as the task is better practiced and the choice
becomes automatic (15).

The Individual as an Information Processing System 311

We have been interested in one aspect of channel capacity
which can be studied at five levels of living systems. What happens
at each level when a channel is overloaded?

INFORMATION INPUT OVERLOAD

From a review of the literature we were able to draw a curve
which appeared to apply at each level. The general shape of
this performance curve shows the output (in bits per second) rising
as a more or less linear function of input until channel capacity
is reached, then leveling off and finally decreasing in the con-
fusional state. This cross-level generality appeared fairly con-
vincingly in the empirical work of others, even though it was not
recognized as such by them. At the same time, we also found
suggestions as to hierarchical differences among the levels. The
overall impression of the findings is that channel capacity decreases
from cells to organs, to individuals, to groups, to social organi-
zations. Processes of adjustment appear to be comparable at
different levels.

We have hypothesized that there are limited numbers of such
adjustment processes which behaving systems can enlist as stresses
on them increase. The following adjustment processes, or mech-
anisms of defense, seein to be used by living systems against the
stresses of information input overload. Not all living systems have
all these inechanisms. The smaller systems, like neurons, appear
to have fewer than the larger systems, like societies, which not
only have all of them but also have complicated variations of
them. These appear to be the fundamental mechanisms, but this
may not be an exhaustive list:

1 ) Omission, which is simply not processing information whenever
there is an extreme of overload;

2) Error, which is processing incorrectly, then not making the
necessary adjustment;

3) Queuifig, which is delaying responses during peak load periods
and then catching up during lulls;

4) Filtering, which is systematic omission of certain categories
of information, according to soine priority scheme;

312 Information Storage and Neural Control

5) Approximation, which is an output mechanism whereby a less
precise or less accurate response is given because there is no time to
be precise;

6) Multiple channels, which parallel transmission subsystems that
can do comparable tasks at tiie same time and consequently to-
gether can handle more information than a single channel can
transmit alone;

6a) Decentralization, which is a special case of this; and, finally
there is

7) Escape, which is leaving a situation entirely or taking any
other steps that effectively cut off the flow of information.

Thus we have searched for quantitative similarities and differ-
ences among living systems at all levels in the way they react to in-
formation input overload, and have given special attention to a)
performance characteristics of a system as an information processing
channel; and b) associated adjustment processes used to relieve
stress on the information processing subsystem and maintain per-
formance.

Our original intention in approaching the problem of over-
loading living systems with information was to study a single
variable — the input-output rate relationship — postulating a formal
identity of this function in channels at all levels of living systems.
But this proposition turned out to involve numerous others about
many variables representing other aspects of systems. The whole
problem ramified in a fascinating way.

We built apparatuses and designed procedures which we hoped
would provide stable conditions for collecting performance data
from the systems we selected for study, attempting to hold con-
stant as many of the variables not under investigation as possible.
We were not concerned primarily with obtaining the maximum
possible transmission rates from our systems, but rather attempted
to create a stable situation in which we could test our overload
proposition and be sure we knew when overload occurred. Later
we could study as independent variables those functions which
change a given system's maximum channel capacity.

Since information bits per second had been used by others in
researches at all five levels, we believed this to be a suitable measure

The Individual as an Information Processing System 313

of performance. We realized that at each level we would encounter
a complex statistical problem if we used limited sequences of
inputs. We also met other problems in calculating" bits, particularly,
in knowing what code was employed at the cell and organ levels,
and in knowing the exact size of the implicit ensemble at all levels.
We hope our methods at least begin to cope with these issues.

Cellular Research

A stimulator was constructed which could administer pulses to
a neuron at various average rates, and at various intensities at
each of these. A single fiber in the sciatic nerve of the frog was
isolated by microdissection, and was stimulated at the rates of
100, 200, 400, 600, 800, and 1,000 pulses per second, using four
different values of stimulus voltage (1, 5, 2, 0, 2.5, and 3.0 times
the threshold value). We recorded the output of the fiber thus
stimulated from microelectrodes in the same cell and across a
synapse in the next cell.

As the input rate was increased, the fiber eventually ceased to
follow every input and started missing some. Among the fibers
which we have studied, three different types of responses have
been observed. Some fibers, when they reach the point at which
they can no longer follow every stimulus, start skipping every
other stimulus. As the rate is further increased they respond only
to every third or fourth stimulus in a regular fashion. Other fibers
skip in a perfectly random manner, so that at a given rate the
number of pulses skipped will have a Poisson distribution. Still
other fibers transmit several adjacent stimuli and then fail to
transmit any stimuli at all for a long period, after which they
again fire repeatedly. Sometimes all three types of functions are
found in the same fiber at different times and at different rates of
stimulation.

Two other phenomena were also noted. As the rate of stimu-
lation was increased, there was a fall in the amplitude of the
response and a decrease in the lag between the occurrence of the
input and the start of the response pulse. The amplitude decrease
is probably related to the energetics of membrane recovery; the
lower recovery time leads to a lower potential. The decrease in
latency must have a similar explanation; it makes the fiber able

314 Information Storage and Neural Control

to cope with a greater overload, enabling it to follow at much
higher rates than would otherwise be possible. Our findings are
in harmony with others who have worked in this field.

In order to measure the maximum information transmission
capacity of a nerve fiber which employs pulse-interval coding,
we must be able to stimulate the neuron with an input source
which can deliver trains of two or more pulses at diff^erent intervals.
This follows from information theory, since in an evenly spaced
pulse train there is no uncertainty about the time of arrival of the
next pulse, and hence no information. Maximum uncertainty is
available only in a random source in which the pulse is equally
likely to occur at any time. It is also necessary to determine the
minimum interpulse interval which can be discriminated by the
neural system. We can determine this by measuring the standard
deviation of the latent period in a fiber, or its "jitter."

We proceeded, therefore, to use an electronic timer, accurate
to 1 microsecond, to measure the jitter of single sciatic nerve
fibers of the frog, studying variation as tiie time between two ad-
jacent pulses was reduced. This turned out to be of the order of
2-5 microseconds, and adding a third pulse before the other two
did not aff^ect this value. Using a mathematical model developed
by Rapoport and Horvath (16), we were able to calculate the
curve of maximum channel capacity of such a neuron at various
input rates. We found that the output increased as a function of
the input up to 4,000 bits per second (an astonishingly high
capacity for such a small system — assuming optimal pulse-interval
coding); then leveled off" and decreased, thereafter, as the input
rate increased. This performance curve is shown in Figure 1.

As for adjustment processes, the skipping of pulses which we
found at the higher input rates was, of course, omission. The
lower output intensities could be called erroneous processing if
they were not intense enough to cross the threshold of the neuron
on the other side of the synapse. That threshold, incidentally,
can be considered a sort of filtering. For other neuronal adjustment
processes to information overload, we have no evidence.

Organ Research

We used the same electronic timer to stimulate the optic nerve
of the white rat, recording the output from a macroelectrode on

The Individual as an Information Processing System

315

O T3

Fig. 1.
mation

0 5 10 15 20 25 30 35 40 45 50

AVERAGE INPUT RATE PULSES PER SECOND X 10^

The channel capacity of a model neuron calculated by continuous infor-
theory for a Gaussian noise distribution with a standard deviation, <x,
or jitter = 5 ^ sec. Refractory period taken to be 1 msec.

^ 00 200 300 400 500 600 700 800 900 1000

2 AVERAGE STIMULATION RATE IN PULSES PER SECOND ( Poisson Input)

Fig. 2. The channel capacity of a model organ system calculated by continuous
information theory for a Gaussian noise distribution with a standard deviation, cr,
or jitter = 1 msec. Refractory period taken to be 50 msec.

the optic cortex. Similar calculations from this experiment gave
us a curve of comparable shape. The channel capacity, however,
was of the order of fifty bits per second (Fig. 2).

316

Information Storage and Neural Control

lllllllll
■ llllltii

■ I III 111 I*
■■lillllll

f ii!!:iiif

Fig. 3. Subject at IOTA Apparatus.

The same adjustment processes appear at the organ level,
except that multiple channels, of course, are also used.

Individual Research

For this study we designed and built an IOTA (Information
Overload Testing Aid) apparatus (Fig. 3).

This is a piece of equipment by which stimuli are presented
to a subject on a transparent ground-glass screen, about 3x4 feet
in size. The apparatus is placed on a table in front of the subject,
who responds by pushing appropriate buttons arrayed before him.
Stimuli are thrown on the back of the screen by a Perceptoscope,
which is a projector capable of showing movie film at rates of
from one to twenty-four frames per second. The film contains a
program which presents black arrows on a white background,
which can appear in from one to eiglit of the eight two-inch wide
vertical slots which run down the screen. Arrows can assume any
one of eight angular positions, like clock hands. Before the subject

The Individual as an Information Processing System 31 7

is a set of eight buttons for each of the slots being used. Since he
can see stimuH in a maximum of four slots at once, altogether he
has thirty-two buttons, four sets of eight buttons each. If an arrow
in Position B appears in Slot 3, the correct response is to push
Button B of the set for Slot 3. Any other response is an error.
If the subject pushes none, that is an omission.

Queuing is also possible. The subject has a foot pedal with
which he can lower or raise opaque strips behind each of the slots.
At the beginning of each test, only the top square in each of the
slots being used is open so that light can come through. If the
subject pushes the pedal, he can move the opaque strips to open
as many as eleven more squares, a maximum of twelve; or by push-
ing the pedal in the other direction he can close these up again, as
he wishes. The moving picture film is made so that if an arrow
appears in Position B in Slot 3 in Frame 1 of the film, it goes to
the next lower position in that slot in Frame 2, and to the next
lower position in Frame 3, until having gone through all twelve
positions, it finally disappears from the screen. In the meantime
other stimuli may be appearing higher in the same slot, or in
other slots. Therefore, when the subject pushes his queuing pedal
he gives himself more time to respond to the stimulus before it
disappears. He can filter by paying attention only to the arrows
pointing up, or to those pointing to the left, rather than to those
pointing to all eight positions. He can approximate by pushing
all four left buttons in Slot 3, if he is not certain in which of the
four left directions the arrow pointed, but knows it pointed toward
the left; or by pushing all eight buttons for Slot 3 if he simply
saw an arrow but has no idea of its direction. On occasion, he
can use multiple channels by working with both hands at the
same time. Finally, escape is possible, if he gives up and refuses to
continue the task. So all the mechanisms of adjustment that we
have mentioned are possible on the IOTA.

This apparatus can increase the amount of information per
second in several ways: 1) by increasing the ensemble, or the
number of alternate positions for the arrows from two (1 bit) to
eight (3 bits); 2) by speeding the movie; 3) by increasing the
range, or raising the number of slots used simultaneously; or 4) by
altering the degree of regularity or randomness of the presentations.

Information Storage and Neural Control

INPUT RATE (BITS/SEC)
Fig. 4. Performance curves for Subjects A and B

Two male college students were used as subjects in this study.
Before being tested, they were thoroughly trained in the procedure,
including the use of all the adjustment processes.

The button-pushing performance of each subject was recorded
on a kymograph and was compared with the program of stimuli
presented. These raw data were fed into a computer programmed
to calculate the input (stimulus presentation) and output (subject's
response) rates in bits of information per second, using the Shannon
information statistic.

Data obtained with this equipment (Fig. 4) produce a curv^e of
the same general shape as do data at the level of cell and organ when
input in bits per second is plotted against output in bits per second.
Within the range tested, there is some question as to whether the
output falls below channel capacity at high input rates, or whether
some cut-off mechanism prevents this from happening. It is prob-

77?^' Individual as an Information Processing System

319

INPUT RATE (BITS/SEC)
Fig. 5. Average utilization of adjustment processes by both subjects at various

input rates.

able, however, that uhimately, at very high input rates, output
does falL The maximum channel capacity for an individual
operating the IOTA was determined to be about six bits per
second. Other experimenters have found maximum channel
capacities for random material up to about thirty bits per second.
With the IOTA, however, output rate is limited by partial in-
compatibility between the nature of the stimulus and the organiza-
tion of the response mechanism, by the difficulty of making the
response, and by many other factors.

Our subjects were trained regarding the possible mechanisms
of adjustment available to them, and were free to select them as
they saw fit. They used few or none of the mechanisms at slow
rates of transmission. They tended to attempt them all at medium
rates. At higher rates, under our experimental conditions, the
subjects showed preference for filtering and, particularly, for
omission (Fig. 5). Whether this preference is genetically deter-
mined or learned, we do not know.

320

Information Storage and Neural Control

3.0

- 2.5
<_)

<
ir

2.0

1.5

z) 0.5
O

O— O GROUP A
•— • GROUP B

I 2 3 4 5 6 7

INPUT RATE (BITS/SEC.)

Fig. 6. Performance curves for Groups A and B.

Group Research

We also used the IOTA apparatus with two four-man groups.
The procedure was as follows: Three members of the group, A,
B, and D, face the screen. A calls out the number of the slot in
which an arrow appears, and B calls out a letter representing the
position. C, who is facing the buttons, but whose back is turned
to the screen, then pushes the button indicated by the information
he got from A and B. When C pushes a button a small red light
appears over one of the slots, indicating which button he pushed.
If his push is correct, D says nothing. If the push is incorrect, D
corrects C and C tries to push the right button. The performance
curves from our pretest runs with two groups have the same
general appearance as the performance curves of the individual
subjects, though at lower channel capacities — about 3 bits per
second (Fig. 6).

This is, of course, a very specialized sort of small group in which
roles are strictly differentiated. Only some members can receive
sensory inputs, while others make responses or perform other
tasks. There are structural similarities to certain lole-diflfeientiated
groups in military life, like tank crews, bomber crews, or sub-

The Individual as an Information Processing System

321

8.0
7.5

7.0

ADJUSTMENT PROCESSES

• • OMISSIONS

o o ERRORS

* « FILTERING

* * APPROXIMATION

I 2 34 56 789 10 II

INPUT RATE (BITS/SEC)

Fig. 7. Average utilization of adjustment processes by both groups at various

input rates.

marine crews. We recognize this as only one type of group, just
as we used only one type of individual in our individual-level study.
The use of adjustment processes by groups was comparable to
their use by individuals, although queuing was not employed. The
reason for this is not clear. Figure 7 presents these findings.

Social Institution Research

My colleagues and I have conducted two investigations on
informational overload of social organizations. In one project,

\77

Information Storage and Neural Control

much of which was carried out by Jay and McCornick, we studied
a simulator of the air raid warning system of the United States
and Canada. The simulator, at System Development Corporation
in California, which cooperated in the study, consisted of three
groups of three men, each in three separate rooms. Because of
these three echelons, and because all members were not face to
face, we called this an institution rather than a group, though
it was a very small unit. The first room simulated a radar station
in the air raid warning network; the second room simulated the
room at headquarters in which the message from the local station
was received; and the third room simulated a plotting board on
which the location of planes was indicated at headquarters. Dots,
presumably representing airplanes in geographical sectors, ap-
peared randomly on a 21x21 matrix (Fig. 8). These dots, each
with an associated message number, were thrown on a board by

•iff!'!1WRfT.^'!n«!Pf?!fS"!l'flR '32SS5-S

Fig. 8. Display board for Air Raid Warning Simulator.

The Individual as an hiformation Processing System

323

5.0

4.0h

OUTPUT
RATE 30
IN BITS

PER 2.0

SECOND

I.Oh

2 3 4 5 6 7 8

INPUT RATE IN BITS PER SECOND

Fig. 9. Average performance curves for teams in Social Institution Experiment.

a movie projector. Each of three readers in the first room was
responsible for one-third of the total board. When a dot and its
number appeared in his sector, the appropriate reader wrote down
on a card the coordinates of the cell in which the dot appeared
and also the number of the dot. He then presented the caid to
his corresponding teller in the next room by passing it through a
slot. The teller in turn read the card by telephone to the cor-
responding plotter in the third room, who wrote the message
number in the proper cell on the plotting board. This board was
photographed automatically at 6-second intervals, so that a con-
tinuous record of the appearance of numbers on the plotting board
could be obtained. Thus, there were three entirely separate chan-
nels in this system, since Reader A always gave his information
only to Teller A, who passed the message only to Plotter A, and
so on for Team 2 and Team 3. The performance curves for these
teams had shapes similar to those curves obtained at the individual
and group levels when input in bits per second was plotted against
output in bits per second. Maximum channel capacity was about
four bits per second, approximately in the range of the group,
probably because information passed through about as many com-
ponents as in the group, rather than through more, as would
have been the case in a larger social institution (Fig. 9). These
subjects also had much more practice than those in our group

324

Information Storage and Neural Control

100

90-
80-
70

UTILIZATION

ADJUSTMENT ^°

PROCESSES

40+18

• • OMISSIONS

O— O ERRORS

A A QUEUING

>> K FILTERING

AVERAGE
SECONDS OF
QUEUING

50 + 21

123456789
INPUT RATE IN BITS PER SECOND
Fig. 10. Average utilization of adjustment processes by teams in Social Institution

Experiment.

research and had greatly improved their transinission rates since
their earher trials.

Four adjustment processes were used by the teams in these
studies — omission, error, queuing, and filtering. The experimental
instructions prevented use of approximation and multiple channels.
Utilization of all adjustment processes was measured in percent-
ages, except for queuing, which was measured in average number
of seconds of delay (Fig. 10).

An associated study directed by Meier (17) dealt with the
effects of overloads of demands upon the Undergraduate Library
of The University of Michigan at periods of peak use. The inflow
of students and faculty into this library, each person with special
needs, is not an overload of energy or matter, for the library is
never actually physically unable to hold them. The demands upon
members of the library staff for service, however, can constitute
what is essentially an information overload.

Participant observation and other operations research procedures
were employed to find how much the library was used at top load
periods and what changes occurred in library functions at such
times. Since no significant difference in average time of getting

Thf Indii'idiial as an Information Pmcessing System 325

a book was found between periods of light and of heavy use, the
library may not have been under real performance overload at
any time. Rough efforts were made to calculate the number of
bits of information flowing through the library. It was determined
that the average book title in the card catalog contains about
135 bits of information, and that the average reader processes
between 50,000 and 90,000 bits per hour of reading.

Perhaps the most significant finding by Meier and his colleagues
was that a series of adjustment processes occurred, or could occur,
in the library to cope with the overload. He recognizes the simi-
larity of his list to the one presented earlier in this chapter. How-
ever, he found more complex forms of these adjustment processes,
or "policies" as he calls them, in this complicated social institution
with its many subsystems carrying out numerous activities. His
list follows: Queuing; priorities in cjueues and backlogs; destruction
of low priority inputs (filtering); omission; reduction of processing
standards (approximation) ; decentralization (a special case of use
of multiple channels) ; formation of independent organizations near
the periphery (multiple channels); mobile reserve (multiple chan-
nels); rethinking procedures; redefinition of boundaries of the
system; escape; retreat to formal, ritualistic behavior; and dis-
solution of the system with salvage of its assets. Whether there
are new adjustment processes here, or simply special cases of those
we have listed is a question for debate; but that such adjustment
policies are used, there can be no question.

Summary of Our Research

For five levels of organization, or systems, viewed as information
processing channels, the following propositions appear to have
support:

a) When information input in bits per second is increased, the
output at first follows the input more or less as a linear function,
then levels off at a channel capacity, and finally falls off" toward
zero. We have yet to deteimine whether the larger systems have
a cut-off mechanism which prevents the final fall in output.
Though such a mechanism may delay this fall, the weight of
evidence suggests that it must finally occur.

326 Information Storage and Neural Control

This decrease of information output rate in living systems is
not the result of destruction of the system by an overload of the
energy which conveys the information because 1) the process is
reversible — decrease of input rate immediately raising output rate
back to channel capacity, and 2) final irreversible change of such
systems by energy input undoubtedly occurs when the energy is
orders of magnitude greater than that involved in informational
overload.

b) There is a hierarchical, cross-level difference in maximum
channel capacity. Assuming pulse-interval coding, we found this
to be of the order of 4,000 bits per second for neurons in the
frog sciatic nerve, and about fifty bits per second for a single
channel in the visual nervous system of the rat. It was six bits
per second for the individual, three bits per second for a single-
channel group, and three to four bits per second per channel in
a small social institution with about the same number of com-
ponents in each channel as there were in the group.

Apparently the more components there are in an information
processing system, the lower is its channel capacity. There are
several reasons for this. Two of the most obvious are that recoding
of information is necessary at the border between each component
and the next, and that such recoding always results in loss of a
certain amount of information. Moreover, if there are n com-
ponents in any system, one must have a lower channel capacity
than the others, and the statistical probability of there being such
a slow component is always greater as n increases. This sluggish
component constitutes a bottleneck, since no channel is faster than
its slowest component.

c) Several of the adjustment processes are used by all of these
systems, the use increasing as input rate rises.

d) Fewer adjustment processes seem to be available to the
systems at the lower levels. Those employed at the higher levels
appear to be more complex as well as more numerous, although
their fundamental similarity to the lower level processes is clear.

Of course the findings for other types of systems at each of the
levels might be difTerent in significant ways from our findings in
the particular systems we chose to study. The goal of these projects

The Individual as an Information Processing Sy stein 327

was to determine whether a cross-level formal identity could be
confirmed for any examples of systems at different levels.

It is apparent that interesting insights arise when not only
individuals, but all living organisms and organizations are viewed
as information processing systems.

REFERENCES

1. Barlow, H. B.: Sensory mechanisms, the reduction of redundancy

and intelligence. In: Mechanisation of Thought Processes, Proceedings
of Symposium at National Physical Laboratory, Teddington, Eng-
land. London: Her Majesty's Stationery Office, 1959, p. 542.

2. Gerard, R. VV.: Organism, society and science. Sci. Monthly, 50:

340-350, 403-412, 530-535, 1940.

3. Piatt, J. R.: Amplification aspects of biological response and mental

activity. Arner. Sci., 44.- 180-1 97, 1956.

4. Swets, J. A., Tanner, W. P., Jr., and Birdsafi, T. G.: The evidence

for a decision-making theory of visual detection. Technical Report
JVo. 40, Electronic Defense Group, LIniversity of Michigan, Ann
Arbor, April, 1955.

5. Stevens, S. S.: Cross-modality validation of suljjective scales for

loudness, vibration, and electric shock. J. Exp. Psychol., 57:201-
209, 1959.

6. Broadbent, D. E.: Perception and Communication. New York, Pergamon

Press, 1958, p. 297.

7. Quastler, H.: In Human Performance in Information Transmission. Con-

trol Systems Laboratory, Report No. R-62. Urbana, University
of Illinois, 1955.

8. Broadbent: op. cit., p. 5.

9. Quastler: op. cit.

10. Quastler: ibid., p. 62.

11. Quastler: ibid., pp. 62-63.

12. Barlow, H. B.: Increment thresholds at low intensities considered as

signal noise discriminations. J. Physiol. (London), 141 :?>'il-?)SO,
1958.

13. Deininger, R. L. and Fitts, P. M.: Stimulus-response compatibility,

information theory, and perceptual-motor performance. In, H.
Quastler (Ed.), Information Theory in Psjchology. Glencoe, The Free
Press, 1955.

14. Brown, R. and Lenneberg, E.: A study in language and cognition

J. Abnorm. Soc. Psychol., ^P.-454-462, 1954.

328 Information Storage and Neural Control

15. Mowbray, G. H. and Rhoades, M. V.: On the reduction of choice

reaction times with practice. Qiiart. J. Exp. Psychol., 7 7; 16-22, 1959.

16. Rapoport, A. and Horvath, W. J.: The theoretical channel capacity

of a single neuron as determined by various coding systems.
Inform, and Control, 3:335-350, 1960.

17. Meier, R. L.: Social change in communications oriented institutions.

Mental Health Research Institute, Preprint No. 10, March. 1961.

CHAPTER
XIV

INFORMATION PROCESSING IN THE
TIME DOMAIN

Neil R. Burch, M.D. and Harold E. Childers

T«

HIS paper briefly outlines the work we are conducting in
the Department of Psychiatry, Baylor University College of Medi-
cine, and in the laboratories of the Houston State Psychiatric
Institute. The basis for this research is the theory that a special
case of analysis in the time domain has something to offer both in
time resolution and in economy of information processing that
cannot be readily obtained from frequency analysis or from more
conventional time sampling procedures. The analytical process to
be described we have called period analysis (1).

Given an amplitude function distributed in tiine, there are a
limited number of questions that may be asked of the function
to yield an analysis or to undertake data reduction. Consider the
following four cases: 1) One inay focus on the amplitude and ask
the question "how much" over a time, T; one may focus on both
time and amplitude and ask the question "how much" at par-
ticular points in time, either 2) points at fixed intervals or 3) points
related to an event; finally, 4) one may focus on selected events
and ask the question "when."

A theorem in information theory tells us that if we take this
amplitude distribution in time and sample it every so often, we
will retain complete information about the signal. Presented more
formally, the theorem reads, "If a function G{t) contains no fre-
quencies higher than W cycles per second, it is completely deter-
mined by giving its ordinatcs at a series of points spaced jrr
seconds apart, the series extending throughout the time domain"
(2) (Case 2). A corresponding theorem for sampling in the frc-

329

330 Information Storage and Neural Control

quency domain* requires exactly the same number of sampling
points plus one, or:

2TW + 1

where T is the duration of a signal, W is the spectral band width,
and the sampling points are spaced at fixed intervals.

If we can say in the electroencephalographic (EEG) signal, as
an example, that the highest frequency which carries neuro-
physiological information is 100 cycles per second, we know that
we must sample at least 200 times a second — perhaps much more
often if there is considerable noise in the system — in order to
retain all of the information. While we are now satisfied that in
both the time and frequency domain we may ask of the EEG
signal "how much" at 200 points per second, we have also posed
ourselves a massive problem in data handling. Further, we have
not learned anything of the optimum analytic procedure, since
to retain all information in the original signal "defeats the very
purpose of analysis, which is to abstract and emphasize only
significant changes." (4).

There remain the case of coding "how much" related to a
selected event and the even simpler case of asking only "when"
the event occurs. In both of these remaining cases, the coding
event must be defined and the assumption made that all 200 points
per second in the EEG do not contain the same amount of infor-
mation. Let us for a moment suppose that some of these points
contain ten times the information of other points. Then we may
drop the low information points, retain the high information
points and sacrifice a unique characterization of the wave for a
good approximation. Such a process would be highly economical
in terms of handling the data.

The critical problem is, of course, the generation of the coding
event which acts as a "metasignal" in the sense in which Gregory
Bateson used the term for us earlier. In the first paper of this
symposium, Bernard Saltzberg introduced you to Maxwell's
demon. I would like to propose another hypothetical information
demon, one that might look at each of our points and say, "We'll

*If fi(w) represents the spectrum of a function G(t), which is zero everywhere
except in the range Tj < t < T2, then i2(w) is exactly determined for all values of
w by giving its values at a series of points ^ / {Ti — T2) cycles per second apart in fre-
quency, the series extending throughout the frequency domain. (3) (Case 1).

Information Processing in the Time Domain 331

take those," or "No, that's low information, drop that." If we
speculate that our demon is extremely conservative and expects
the signal to be linear as a function of time, a straight line deter-
mined by two or more points, then any point that agrees with this
assumption is a low information point. The demon has predicted
that the signal will not change from positive to negative values,
will not change its sense of positive-negative direction, will not
even change its sense of curvature. The high information points
now become zero points, minimax points and points of inflection
in the primary signal. The coding points generated are at the
baseline cross of the primary signal, of its first derivative and of
its second derivative. We might have a second type of demon,
a neurophysiological demon, that can tell us when a significant
neurophysiological event is reflected in the signal. This demon
identifies our semantic information as contrasted to statistical
information. It would be nice, of course, if both these demons
were the same. In order to "twin" our two friends, we would be
forced to assume that the brain sees change and rate of change of
the electrical potentials in its subpopulations as highly significant in-
formation. We would also conclude that the wave shape of our EEG
signal is rich in semantic information as compared to characteri-
zations in the frequency domain such as frequency or power spectra.

Defining the coding points for amplitude sampling as the baseline
cross of the primary and its first and second deri\^atives allows us
to take discrete data in a definite but not uniformly spaced pattern
(Case 3). This, on the average, should result in fewer sampling
points than the folding, or Nyquist, frequency requirement (5)
discussed previously. Period analysis is a further simplification of
this general process in that the amplitude of the function is not
sampled at all. The theoretical justification for this approach has
been developed in terms of the Gram-Charlier series (6). The
remainder of this paper will explore period analysis as a special
case (Case 4) of information processing in the time domain. The
questions to be asked concern retention of both statistical and
semantic information during period analysis of several bio-
electronic signals.

Figure 1 illustrates the characteristics of the first and second
derivatives. The function /(.v) in the upper right hand corner of
the figure represents an evoked potential which feeds into a

332 Information Storage and Neural Control

DERIVATIVE CHARACTERISTICS

"to 56 66

TfMSSSKt (CTCIXB m SBXMD)

Fig. 1. Electronic Parameters of the Mathematical Derivative. The 90° phase shift and
linear doubhng of ampHtude per octave is illustrated as the electronic definition
of a first derivative. The sharply increasing amplitude of the second derivative
with increase in frequency emphasizes the accentuation of high frequency com-
ponents. The three functions on the right of the figure graphically illustrate the
eff'ect of derivative processing.

differentiating network to yield the first derivative, / (v) . The first
derivative, through an identical differentiating network, gives the
first derivative of the first derivative, or second derivative, /"(v),
of the primary evoked potential. These functions, after Lorente
de No (7), illustrate the external action potential of bullfrog
alpha fibers and its first two derivatives. It is clear that the high
frequency components of the primary evoked potential are greatly
accentuated by double differentiation. The electronic definition
of a derivative is the same as the mathematical definition except
that it is couched in different parameters. The phase shift required
in a sine wave is 90° for the first derivative and 180° for the second
derivative. The important parameter for our purpose is the
amplitude characteristic as illustrated in Figure 1. Given a mixed
sine wave made up of equal amplitude twenty cycle per second
and forty cycle per second components, the first derivative will
yield twice as much amplitude for the forty cycle per second
component because it is twice the frequency of the twenty cycle

Information Processing in the Time Domain

333

per second component. This linear relationship holds throughout
the band pass range. The second derivative multiplies the forty
cycle component by a factor of 4, the eighty cycle component by
a factor of 16, etc., in this example. Figure 2 illustrates this deriva-
tive processing as it is applied to the electroencephalogram. The
faster frequency components present in a complex primary wave
become full-fledged baseline crosses because of the relative accentu-
ation of the faster frequencies. Period analysis proceeds by generat-
ing square waves at the baseline cross of the primary, the first
derivative and the second derivative. As can be seen in Figure 2,
the square wave train designated as major period reflects the domi-

Fig. 2. Pulse JVidth Conversion: EEC. The process of period analysis applied to
the left parieto-occipital electroencephalogram. The 60 cycle per second artifact
superimposed on the original primary trace is markedly reduced by the rejection
notch of the selective frequency amplifier, as seen in the filtered primary. The
"fragmented" appearance of the second derivative minor period results from the
high inertia pen system which cannot foUow^ a true square wave at these fre-
quencies. Paper speed 60 millimeters per second.

334

Information Storage and Neural Control

nant rhythm of the analog primary. The second derivative square
wave train, referred to as the minor period, carries information
reflecting superimposed fast activity, desynchrony, and waveshape.
It is of particular importance to know how much wave shape
information is retained or lost in this processing, because it is
probably the wave shape which triggers recognition in the human
computer in clinical electroencephalography. We propose that
much of the wave shape information is retained in the three square
wave trains as they relate to one another in time, as we have
attempted to illustrate in Figure 3. The top trace is a synthetic
function made up of a "dominant" nine and one-half cycle per
second sine wave mixed with a lower amplitude "superimposed"

PERIOD RECONSTITUTION
SYNTHETIC FUNCTION

\nj^j\r'iS\j\s^v^'\rvriP^^

/Wu^^

mmmmmm

\\m\mw

nfunnn^n,

wmW'

Fig. 3. Mixed Sine Function. K^Vi cycles pei- second sine wave niLxed with a lower
amplitude sine wave of approximately 36 cycles per second simulates a "dominant
alpha"||with "superimposed fast frequency components." Smoothing, mixing,
and smoothing of the tliree square wave trains result in tlie reconstituted signal
of tlie bottom trace. Similarity between reconstituted and original signal suggests
that wave shape information is retained by the processing.

Information Processing in the Time Domain

335

fast frequency of approximately thirty-six cycles per second. Again
we see the primary square wave, or major period, reflecting the
"dominant rhythin" and the second derivative scjuare wave or
minor period rather clearly reflecting the fast component. If these
three trains of square waves are smoothed individually by an
integration filtering operation, inixecl, and smoothed once more,
the reconstituted analog signal may be written out as shown on
the bottom trace. In a way, this reconstitution is an inversion of
the operations which generated the square waves in the first place.
There is a rather striking resemblance between the reconstituted
primary and the original signal, although careful inspection will
reveal some discrepancies in both phase and amplitude. However,
the wave shape, by and large, has been retained. If the process

PERIOD RECONSTITUTION
ELECTROENCEPHALOGRAM

PRI«UC WAV?

'JFJ^lTi

v^/^

•jn; LTUir^ JV" Jin^a nf L

Wl*UW SQUARE WA7E

SECt-i

\^V

tEcyi

'A:^s\jur[mirTKf\^

^IR3T

'^rwrus^AT

nfi/n4Mi4ifiAaa

isfUJW/lM^LJUU

\iWv/\

Fig. 4. Left Parieto-Occipital EEC. Period analysis of the electroencephalogram
yields the three square wave trains shown. The three square wave trains are
smoothed, mixed and smoothed to reconstitute the wave forms in the bottom
trace. The complex EEG signal retains enough information in the processing
to allow clinical interpretation.

336 Information Storage and Neural Control

does this well on a simple wave, what may be expected from the
rather more complex signal of the electroencephalogram?

Figure 4 shows that the electroencephalogram is not recon-
stituted as successfully as the simple mixed sine waves. Some of
the amplitude modulation features are lost, the envelope is not
as clearly evident on the reconstituted signal, and some phase
shift is apparent as distortion in a number of the waves. Here again,
however, the resemblance of the reconstituted wave to the original
one is rather striking. The clinical electroencephalographer would
probably interpret the reconstituted EEG in much the same way
as he would the original, and would render much the same clinical
impression after reading the reconstituted forms.

Interpretation of the major and minor periods may be accom-
plished in the same way as interpretation of an EEG but with
less equivocation. Anyone who has attempted to reduce, quantita-
tively, long stretches of EEG record by any form of hand analysis
will appreciate the significance of this. The square waves may be
further processed and displayed in several difTerent ways, depending
on the physiological event under investigation. One system we
have used quite extensively distributes the major and minor
periods in a ten second epoch over ten bands in the major period
and ten bands in the minor period. Table I defines the bands we
are currently using in terms of equivalent frequency. A square
wave of the same duration as the square wave generated by an
eight cycle per second sine wave falls into band 4 of the major
period and band 1 of the minor period. The major period bands

TABLE I

Band Distribution (As Equivalent FREquENCv) Currently Being Used

IN "Spectral Display" of the Square Wave Trains Generated by the

Process of Period Analysis

Major Period

Minor Period

{Eq

uivalent Frequency)

{Equivalent Frequency)

Band

in cps

1.5-3.5

1.5-10

3.5-5

10-20

5-7.5

20-30

7.5-10.5

30-40

10.5-13.5

40-50

13.5-18.5

50-60

18.5-30

60-70

30-50

70-80

50-80

80-90

80-100

90-100

Information Processing in the Time Domain

337

SPECTRAL ANALYSIS

BPINEPHRINE EFFECT

Fig. 5. Histogi am-like Display Resulting from ''Band Breakdown'' of Square Wave
Trains. Small downward spikes indicate ten second epochs. The upward spikes
are proportional in their amplitude to the percentage time occupied in tlie
previous ten seconds by square waves which fell into a particular band (see

Table I).

approximate the frequency breakdown employed in clinical electro-
encephalography. Major period band 1 covers delta activity, band
2 is a "slow" theta, band 3 is a "fast" theta, etc. Minor period is
distributed as ten cycle increments in each of the ten bands.

The histogram-like display resulting from this band breakdown
process, which we call spectral analysis, is illustrated in Figure 5.
In both traces the small downward spikes are ten second epoch
markers. The upward spikes are proportional in their amplitude

338 Information Storage and Neural Control

to the percentage time occupied during the previous ten seconds
by square waves which fell into a particular band. Reading" from
left to right between epoch markers, the first band of the minor
period is the percentage time of all minor period square waves
from one and one-half to ten cycles per second (equivalent fre-
quency). The second band of ten to twenty cycles per second has
been reduced from as high as 30 per cent time (full scale equals
50 per cent of full time) in portions of the pre-drug record to an
insignificant percentage during the epinephrine effect. The higher
frequency bands on the right of the spectrum have increased in
amplitude some 30 to 50 per cent. This sort of change we refer
to as a "shift to the right." It is characteristic of mild arousal
such as may be simulated by five micrograms intravenous epi-
nephrine per minute. The major period clearly shows a decrease
in band 4 as the alpha activity is suppressed and replaced by
higher frequencies and some delta activity.

We suspect that level of sleep can be followed quantitatively
by a simple measure of the percentage delta time as well as more
precisely by the spectral epochs. We say "suspect," since to prove
that sleep can be fractionated into, say, fifty distinct levels would
require an independent measure of the state of consciousness having
the same order of resolution as the variable we are trying to
demonstrate. Unfortunately, we are unaware of a performance
measure or any other measure that allows quantitation of the
state of consciousness or state of arousal with as high resolution
as we think is possible with period analysis of the EEG.

Several bioelectronic measures other than EEG may be amen-
able to period analysis or to some modification of the process.
Figure 6 shows the first derivative of the galvanic skin response
(GSR) signal as it is employed to generate square waves coinci-
dent with the onset-to-peak-amplitude time in the primary wave.
We regard the onset-to-peak-amplitude time as "active GSR
time" since it is the time of depolarization of the membrane which
is the effector site of this phenomenon. Automatic analysis of the
GSR produces two parameters of real importance in psycho-
physiological interpretation. The number of square waves gen-
erated per epoch, perhaps ten seconds or perhaps five minutes,
and the duration of the active GSR time for the given epoch are
partially independent parameters which seem worth considering

Information Processing in the Time Domain

339

AtTIVi

■, GS8 SIGl

Shh

^''-^^

~ '

DERIVATIVE

l! High Frequency Noise

ii Rejected by Pulse

11 Width Dis-'-criraination

IjOW Frequency, Low Level
Noise Not Converted to
Rectangular Pulses

Effective Response Level

Fig. 6. Period Analysis of the Galvanic Skin Response. The first derivative of the
primary GSR clearly shows the accentuation of "noisy" fast components. The
effective response level or threshold for square wave generation "filters" out
slow components of insufficient amplitude. Square waves of less than one second
are filtered out by the "period filter" (see text).

340

Information Storage and Neural Control

in the interpretation of states of arousal. Figure 6 also illustrates
the use of a "period filter" (digital filter), which is analogous to a
resonant frequency filter in the frequency domain, but which does
not have the inherent disadvantages of time lag for energy buildup
and decay. If the square wave is of less than five milliseconds dura-
tion in the case of EEG and of less than one second duration
for the GSR, the period filter will not "pass" it for further proces-
sing. The GSR recording is often plagued by relatively high
frequency noise from movement artifact. In a noisy record this
high frec^uency artifact may produce as much as 85 per cent of
the square waves. It is a great convenience to be able to "filter"
them out.

While the system just described is of real practical value in the
analysis of the GSR activity of a single subject, it is indispensable
to the "coincidence" analysis of GSR's from four subjects in group
interaction. Figure 7 is a record of this type of analysis in which

The ESToajNE-ANGus Co

Fig. 7. Coincidence Analysis of Group GSR. Lines two through five show the square
wave trains generated by the baseline cross of the first derivative of the GSRs
recorded from Subjects A through D. The coincidence, or overlap, of "active"
GSR time between all pairs of subjects is shown in lines six through twelve.
Three of a "kind" and four of a "kind" can be seen in lines thirteen through

seventeen.

Information Processing in the Time Domain 341

a special purpose digital computer is employed to measure the
amount of coincidence between the active GSR time of the in-
dividual subjects in various combinations. The square wave of
Subject A is compared for coincidence or overlap in time with the
square waves from the GSR of Subject B, C, D, etc. In a four-
man group, the coincidence of all four GSR's at one time is a
relatively rare event. Such a ''four of a kind" coincidence is
usually the result of a rather strong stimulus which has been
experienced by all four members.

Consideration of coincidence analysis has led us tentatively tc
formulate a model of group interaction predicated on "overlap
of value systems'" among the individuals of the group. The specific
GSR represents perception and reaction to a specific stiinulus.
Generally, it may be assumed that a stimulus has meaning or an
"aflfect investment" for the individual if it produces a GSR. The
GSR, as an indicator of "investment," is taken as a "yes-no"
index without regard for the afi'ect polarity. That is to say, the
occurrence of a GSR reveals that the stimulus is "invested" but
does not reveal whether the stimulus produces a positive aflfective
response or a negative affective response. We are aware of some
of the diflficulties implicit in this rather simplified interpretation
of the GSR in relation to the psychological variables of affect
and investment. We suspect that under certain circumstances
extreme high negative affect may "freeze" the GSR and wipe
out all response. It may be that this sort of inhibition effect is an
idiosyncratic response of the individual or that such a phenomenon
may be seen more often in the schizophrenic than in the normal
patient. The group interaction is seen as a continuously moving
field which presents a sequence of stimuli to all individuals in the
group. Some individuals may not perceive a given stimulus or
may derive no meaning from it. When two or more individuals
perceive and are invested in a given stimulus, it is postulated
that each will produce a GSR and that these GSR's will be
approximately coincident. Insofar as two individuals have coinci-
dent GSR's to a finite but large stimulus array, our hypothesis
would suggest that they have "overlap of value systems."

In clinical group therapy we might ask the following question:
"In the course of group therapy, will two patients in the same diag-

342 Information Storage and Neural Control

nostic category show more GSR coincidence as a pair than would
one of tiiese patients and a third patient in a different diagnostic
category?" Also of interest is the total number of overlaps for a
particular group. At present we are able to analyze only four
people at a time, even if the group is composed of 8 to 10 individ-
uals; but unfortunately in a four-man group or in a four-man
subgroup there are various degrees of "coupling" and communi-
cation between members that may change the number or degree
of coincident GSR's. For such interpretation the total GSR
population should be taken into account because the number of
overlaps must be soine function of the total number of GSR's
generated. In a very loosely coupled group, such as four people
in four different rooms without communication, there is a certain
probability of overlap that can be computed theoretically. A
somewhat more difficult theoretical problem is that of the expected

1500

VBU. <»OUP OW TWW8 O* OVMUr n MTO

•

1300

•

1100

•

900

1'°°

. 1

' • . •

500

200

...

100

•

1 1 1

i 1

1500
Sl«tt 1

Fig. 8. Scatter Diagram Representing Approximately 50,000 GSR's Recorded in Group
Therapy. The number of coincident GSR's between pairs of subjects per group
(Sigma 2) plotted against total number of GSR's per group (Sigma 1) shows a
linear relationship which may be used as a baseline for interpretation and cor-
rection for overlap expected on a probability basis.

Information Processing in the Time Domain

343

overlap value in the moderately coupled group of four people in
the same room in therapeutic group interaction. In an empirical
approach to the problem of moderate coupling, we have plotted
the total number of GSR's generated by a group against the
total number of overlaps for that group. Figure 8 is the scatter
diagram of two different groups in therapy. These data represent
approximately 50,000 GSR's. The rather good linear relationship
in a fair-sized population, with respect to number of subjects and
hours of interaction, suggests an expected value of GSR coinci-
dence which may be used as a baseline for the interpretation of
overlap between subjects for small increments of time. This tech-
nique may allow us to reconsider group process studies in terms
of this new approach.

The final application of period analysis which we would like
to describe is its use in connection with the electrocardiogram
(EKG). Figure 9 summarizes some of the parameters, relationships
and cjuestions which are of interest to us in reduction of the EKG.

D/iTA OBTAINABLE FROM PERIOD ANALYSIS

PRIMARY EKG

Q S

f, FIRST DERIVATIVE

ANALYSIS
OF

PRESENT
MEASURES

OTHER MEASURABLE
PARAMETERS

QRS

PR INTERVAL

PR SEGMENT

QT INTERVAL

ST SEGMENT

ST INTERVAL

FIRST
DERIVATIVE

PR SEGMENT

PR INTERVAL

QRS

ST SEGMENT

SECOND

DERIVATIVE PP" SEGMENT

PR INTERVAL

QRS

ST SEGMENT

PRIMARY a

FIRST
DERIVATIVE

RO-T INTERVAL
RO-U INTERVAL
P-RO SEGMENT

a TIME DURATION OF

ANY WAVE
bTIME DURATION OF

ANY INTERVAL
( ZERO CROSSING )
c SIGNATURE

RECOGNITION

a TIME DURATION OF

ANY WAVE
b TIME DURATION OF

ANY INTERVAL
c SIGNATURE
RECOGNITION

ANALYSIS QUESTIONS

I. IS THE P WAVE INVERTED?
2 IS THE R WAVE INVERTED?
3,IS THE T WAVE INVERTED?
4 IS THE MAGNITUDE OF P,Q,'

R.S.AND T GREATER THAN

SOME CONSTANT?
5.Q = S? (TIME)

1 IS THE RATE OF CHANGE
IN p Q.R.S.T, AND U WAVES
GREATER THAN SOME
CONSTANT?

2 IS THE P WAVE SYMMETRICAL?
3. ARE CERTAIN WAVES

INVERTED ?

1 IS THE ACCELERATION OF P,
Q,R,S,T, AND U WAVES GREAT-
ER THAN SOME CONSTANT?

2 HIGH FREQUENCY ACTIVITY?

1 SYMMETRY OF T ?

2 ARE THERE NOTCHES IN P,R,
AND T WAVES ?

Fig. 9. Classical EKG Wave Shape and Derivatives. Parameters employed in clinical

interpretation are related to other parameters not usually considered and to

questions which might be posed in the analysis.

344

Information Storage and Neural Control

PERIOD ANALYSIS OF EKG

PRIMARY WAVE

i-ST

»tc—

$T INT

BASE LINE

1 m [

BASE LINE CROSSINGS
OF PRIMARY WAVE

m rri

POSITIVE
WAVE

NEGATIVE
WAVE

FIRST DERIVATIVE

BASE LINE CROSSINGS
OF FIRST DERIVATIVE

_ra R R r

m Ri R EZi

BASE LINE

POSITIVE
WAVE

NEGATIVE
WAVE

SECOND DERIVATIVE

BASE CROSSING

OF SECOND DERIVATIVE

>"^..,/\j — BASE LINE

nn 13 f? n n positive

' ' ' ' wave

nn nw\n nn nega^t^.ve

Fig. 10. EKG Positive and Negative Square Wave Trains. The square wave trains
generated by baseline crosses of the primary, first derivative and second deriv-
ative of the EKG are detailed in this ilkistration. All durations and intervals
are available for computation.

Information Processing in the Time Domain

345

Figure 10 presents the square waves generated by both the positive
and negative portions of tlie EKG and its derivatives. The physio-
logical information contained in these square waves and in their
relations to one another is still largely unknown. Reports of re-
cent studies employing general purpose computers and utilizing
coding points similar to period analysis indicate success in charac-
terizing and classifying normal and pathological subjects (8). We
would like to expand one particular problem of EKG analysis
as we have approached it in our laboratories. Both low wave

Fig. 11. Recognition of a 'fat-thin-faV' Square Wave Set. The synthetic function of

mixed sine waves slowly changes wave shape over time. If, and only if, the wave

shape generates a square wave sequence within acceptable limits, the complex

is "recognized," as indicated by the spike in the recognition pulse trace.

346

Information Storage and Neural Control

y^^

Jl/^aJL/^^J

F:ltai*d irljirx IID

MiJor

Riieasiiltlia FlIm

Fig. 12. Artifact-Contaminated EKG. Slow wave artifact distorting wave shape
and high frequency "pop" artifact are "filtered" out by absence of "recognition."
Only those complexes within acceptable limits are held in intermediate storage
for further computation. Sixty-cycle artifact is filtered by conventional resonant

rejection circuits.

sway artifact and high frequency movement artifact demand,
for practical analysis, automatic rejection of the contaminated
complex. The "filter" we have employed is a system of signature
recognition or pattern recognition. A somewhat different type of
pattern recognition, as defined in the recent work of Steinberg,
et al. (9), is a hybrid combination of Cases 3 and 4, and again
utilizes several coding points of period analysis.

Figure 11 again presents a synthetic function of mixed sine
waves. The two oscillators drift in relation to one another over
time, and the wave shape pattern changes with this phase shift.
The square wave train generated by the baseline cross of the
primary, the major period, is presented to digital circuitry which
"recognizes" a complex if, and only if, it is made up of a "fat-
thin-fat" square wave set. The definition of "thin" and "fat"

Information Processing in the Time Domain

347

square waves and the combination of these square waves may be
set up with any desired hmits or sequence; we adjust them for
a given EKG signal. The hmits for this particular example are
320 to 66 milliseconds for a "fat" square wave and 88 to 2.7
milliseconds for a "thin" square wave. The trace designated
"recognition pulse'' in Figure 11 illustrates by the absence of a
pulse the rejection of an improper sequence and individual square
waves not falling within the defined acceptable limits.

Figure 12 presents signature recognition as applied to the EKG.
Both high frequency artifact and baseline sway distort this signal
and are rejected, so that in this figure only three complexes have
been "recognized." Figure 13 is the identical EKG signal taken
at a slower paper speed to display more clearly the rejection of
sway artifact.

ii)iwffiiHfH'H'H'y|i'iiff!|i"''!"|'<!'fH'i'!>Hiif^iH''tti^ir^-vt^ ')'^^ll^| n ''''t^jWf^ni'^

Major Period

Recogr.ltlon pjlse

Fig. 13. Slow Writeout of Artifact Contaminated EKG. Rejection of those portions of
the record distorted by sway artifact is demonstrated by the absence of recog-
nition pulses in die lower trace.

348 Information Storage and Neural Control

SUMMARY

Period analysis has been described as a special case of informa-
tion processing in the time domain. Illustrations have been offered
of the application of period analysis to the electroencephalogram,
the galvanic skin response and the electrocardiogram. The period
filter, coincidence analysis of GSR, and signature recognition of
EKG have been detailed as special techniques appropriate to
information processing in the time domain.

ACKNOWLEDGMENTS

The authors would like to thank Messrs. W. A. Spoor, A. J.
Welch, and R. J. Edwards for their creative contribution in
relation to the work reported.

REFERENCES

1. Burch, N. R., and Childers, H. E.: Physiological data acquisition.

In. Psychophysiological Aspects of Space Flight, ed. by Col. Bernard
E. Flaherty. New York. Columbia University Press, 1961.

2. Goldman, S.: Information Theory. New Yoik, Prentice-Hall, 1955, p. 67.

3. Goldman, S.: Information Theory. New York, Prentice-Hall, 1955, p. 73.

4. Burch, N. R.: Automatic analysis of the electroencephalogram: A

review and classification of systems. EEC & Clin. Neurophysiol., 71:
827-834. 1959.

5. Blackman, R. B., and Tukey, J. \V.: The Measurement of Power Spectra,

New York, Dover Publications, 1958.

6. Saltzberg, B., and Burch, N. R.: A rapidly convergent orthogonal

representation for EEG time series and related methods of auto-
matic analysis. IRE WESCON Convention Record, Part 8, 1959.

7. Lorente de No, R.: A study of nerve physiology. Studies From the

Rockefeller Institute for Medical Research, 752.- 384-482, 1947.

8. Rikli, A. E. et al.: Computer analysis of electrocardiographic measure-

ments. Circulation, 24:643-649, 1961.

9. Steinberg, C. A., Abraham. S., and Caceros, C. A.: Pattern recog-

nition in the clinical electrocardiogram. IRE Trans, on Bio-Med.
Elect., 9:23-30, 1962.

Information Processing in the Time Domain 349

DISCUSSION OF CHAPTER XIV

H. W. Shipton (Iowa City, Iowa): May I ask two questions
please. First, have you used the advantages of your period analysis
system to study the so-called "squeak" effects that were reported
by Storm van Leeuwuen about two years ago? Second, what is
your approach to the inherent difficulty with all these systems of
analysis of presenting multichannel displays? Have you, for
example, written out the records for two channels recorded
simultaneously?

Neil R. Burch (Houston, Texas): The answer to your first
cjuestion is no. We have not investigated the '"'squeak" effect
reported by W. S. van Leeuwuen. The answer to your second
question is that the single-channel processing" we have been doing
for a number of years has been directed toward trying to quantify
changes in the state of consciousness. We are particularly interested
in minimal shifts in the state of consciousness rather than in con-
ditions when a man is in coma or in a state of panic. The work
we have done in the last year and a half has been directed toward
the problem you ask about. For the display of multiple channel
information and for better display of the single channel, we are
using a type of analysis that is the inverse to the overlap analysis
of the group GSR. We generate a train of square waves with
signal A. These square waves are minor period square waves
gaited by the major period. This yields a burst of minor period
square waves, a blank space, a burst of minor period square waves,
a blank space, etc. The duration and positioning of these waves
are characteristic of the wave shape in this signal. We then take
signal B and do exactly the same thing. Now we have two trains
of square waves. We put them into norlogic circuits and ask the
question: "How much anticoincidence is present?" If these are
identical waves, we get no readout at all. If there is dissimilarity
between signal A and Signal B, even in very minor phase shifts,
then this system reads out either the exact amount of instantaneous
anticoincidence or the sum over one second or more. We also
plan to display this information toposcopically, and hope to be
able to handle up to 10 channels in this way.

PART V — SUMMARY AND GENERAL DISCUSSION

Moderator: Ralph W. Gerard, M.D., Ph.D.

CHAPTER
XV

SUMMARY AND GENERAL DISCUSSION

Ralph W. Gerard, M.D., Ph.D.

AM not confronted here with the problem that so often emerges
in trying to summarize a symposium of this kind, because Drs.
Fields and Abbott have so clearly exhibited the logical bones of
the organization. I think it has been beautifully planned and, on
the whole, beautifully executed. There have been many good
talks and many interesting lines of thought developed, not all of
which, obviously, can I allude to; nor shall I attempt to mention
all the participants in the course of my discussion, although I
shall refer to things said by practically all. A few items to start
us off.

Dr. Lindsay, in the opening theory session, made rather a point
of distinguishing product theories from process theories. I had not
previously heard the dichotomy in that particular form, but I
liked it. It is equivalent, I should think, to molar and molecular
theories and to the term introduced by Mainz, order-analytical
interpretations and cau.sal-analytical interpretations; and it does,
as Lindsay suggested, imply a progressive reduction from one
level to another. He seemed to think this is primarily because
psychologists are reaching out hands toward neurophysiologists.
I think the hands are coming from both sides of the gap; and,
indeed, still partly an act of faith, I am quite convinced that the
hands have about touched.

At the level of genes, Kit and Echols, gave the beautiful evidence
showing that the genetic code is about to be broken; and, as I
listened, it seemed that here, also, interest was moving from one
level of thought to another. There was again reductionism; prob-

353

354 Information Storage and Neural Control

lems that started pretty clearly as biological ones have now
become of interest almost entirely at the level of pure chemistry.
The problems here are very sharp and, therefore, will very soon
become dull; because, when it is possible to formulate the issues
as clearly as it now is, getting the answers is a matter of hard work
but often lacks major intellectual excitement. I think the great
epoch of the nucleotides is rapidly drawing to a close, although
several Nobel prizes are still lurking there; I am not denigrating
it, I assure you. I think the most exciting area for the future is
rather in reducing behavior to neurophysiology. The questions
here are still fuzzy enough so that almost any kind of answer is
likely to be exciting.

Going on with the group, Bateson gave us his charming presen-
tation as raconteur and experimenter. He exemplified beautifully
the story that psychologists love to pass around: One rat says to
another, "By golly, I've got my experimenter trained now! Every
time I push the lever, he feeds me." He discussed the fact that one
deals with metasignals for information as to the kind of world
one is facing, and, in this connection, there are several points
that I cannot resist making.

There is an obvious experimental prediction, which perhaps
has been checked. (I understand such experiments do give the
predicted results.) Bateson compared the classical conditioning
experience of one rat with the instrumental conditioning experi-
ence of another, and said that each rat then allowed free experience
in the world would find his experiment-induced expectations more
or less reinforced. This is part of establishing a particular learning
set. An animal given a learning set in terms of experience with
classical conditioning should learn an instrumental conditioning
situation less easily than would a naive animal, and vice versa.

At the human level, we at Michigan have an interdisciplinary
study on schizophrenics, attempting to break them up into sub-
categories. Our social scientist came to the interesting conclusion
that the social space in which a schizophrenic subject lives (in
contradistinction to the non-schizophrenics in the same hospital
and under the same conditions) — his social world — is different
from that of non-schizophrenics and that the behavior of the
schizophrenic, so abnormal relative to our world, may not be too

Summary and General Diseussion 355

inappropriate to his. This is closely related to what Mr. Bateson
was saying".

In the section on the nervous system, Dr. Brazier gave us an
excellent picture of the whole field, with some emphasis on how
spontaneous wave generation might give an internal comparison
standard. Dr. John picked this up in his research report, then he
and Dr. Morrell had a good discussion on the mechanism of
fixation, to which I shall return. At the human level, Dr. Miller
contrasted the problem of energy and information flow and intro-
duced the concept of levels, and Dr. Burch discussed similar
problems in connection with his technicjue of extracting informa-
tion from a complex temporal signal.

Now, what can one do to integrate all these fine materials?
I should like to conduct this discussion in terms of four major
headings: l)the question of order and information in general, and
as applied to organisms; 2) the role of the environment; 3) the
problem of malleability; and, 4) the problem of fixation. At the
end I shall say a word about our own work on fixation.

I am not an information theoretician, but it seemed to me when
I began to put this summary together that organizing the material
as follows gave me further clarification of the session on information:
Think of a deck of cards in any particular order; obviously the
energy in it is exactly the same for any order. If you burn the
deck, the calories obtained are the same whatever the order.
Furthermore, any particular order in a well-shuffled deck is just
as probable as any other particular order. Certain orders are of
more interest than others, but any order would be of great interest
to a player for it determines the hands that are dealt.

I think it is useful to distinguish a structural order such as the
kind of order in which the cards come from the manufacturer
(ace through king and one suit after another). Such structural
order we easily recognize in architecture. Usually it implies some
regularity and symmetry and repetitiveness, and ordinarily we
are likely to call this "order." But I can easily demonstrate to
you another very diff'erent order which I might call functional
order — an apparent "disorder"' in arrangement that emits ordered
behavior. You may have played this little trick as a child: Organize
the cards so that by moving the top card to the bottom at each

356 Information Storage and Neural Control

letter and turning up one at the word you spell out o-n-e — one;
the ace appears: t-w-o — two, the two is turned; and so on, right
through the deck, ending with the last two cards of the last suit.
Examination of the cards as they have been ordered in the deck
so as to give this functional output, which recreates the structural
order of the original package, reveals nothing" at all; the deck
seems to be completely messed up.

Either kind of order is produced by some operation of the
environment on tlie system, on the deck of cards; and the amount
of information contained in it, in the technical sense, is a matter
of how well we know the rules that produced that particular
order. If, for example, one gives the value of tt to many hundreds
of digits, the number of bits needed to transmit it would increase
without limit at the rate of over three bits per digit. But if the
formula for calculating tt is given, very few bits are needed for
a limitless number of digits.

I suggest that one sees structural order quite easily and recognizes
the rule almost intuitively; whereas, one does not see functional
order nearly so easily nor tumble at once to the rule. But when
we do find the rule, the information collapses and we no longer
have the element of surprise. Certainly the whole history of scien-
tific development has followed such lines. In every area we have
recognized structural elements, structural entities, and regularities
long before we have paid attention to functional ones.

Turning now to organisms in this connection, stored information
need not require any expenditure of energy. It may, of course,
if storage is dynamic, but it need not, as in the structural storage
of books or pictures. Information flow does take energy, but
negligible amounts will ordinarily suffice. One can think, in
organisms, of an overall structural information, seen in the total
morphology that has been built up. This is what Patten was
concerned with in his study of the morphology of an ecosystem,
a kind of epiorganism. This is of interest per se to the anatomist,
the structuralist; but to the behaviorist, the physiologist, it is of
interest more in terms of what it can yield as patterned behavior.
If the system is suddenly made unable to behave, if it is killed,
most of this information remains present, at least for a time, but
it is no longer of any functional use or interest. In a way, what

Summary and General Discussion 357

I have just called structural information is the same as stored
information; but we tend to think of these gross structures a little
differently from the micro ones of ordinary memory, to which
I shall return. In all, of course, storage of information is a matter
of past experience, either of the race, with phylogeny and ontogeny
laying down structures that ate essentially uniform from individual
to individual in the species, or of individual experience and
learning, with the attendant high variance.

The flow of information was discussed fully by Dr. Miller, but
I shall add a few general comments. First, all the informational
aspects of organisms are induced originally by the environment
acting upon the system, and changes in these aspects are over-
whelmingly the result of continued environmental influence. There
are, therefore, two extremely interesting questions to raise about
such influence. The first concerns the sensitivity of the system
to environmental influence; the second, the establishment of an
enduring change. Sensitivity can be of two kinds: 1) quantitative —
what threshold of an environmental disturbance or alteration is
necessary for the system to recognize it, so to speak; and 2) quali-
tative— what specificity exists, what discrimination is made be-
tween different kinds of environmental influences — which is per-
haps even more interesting. So we have the subquestions of
threshold and of specification.

The other large question has to do with the conditions under
which a transient action of the environment leads to a response
of the system. The environmental action, although originally
ephemeral, may become irreversible and lead to a permanently
altered system. When and how does a reversible response of the
system become an irreversible change? This is the essential prob-
lem of evolution, of individual development, of group history, and,
of course, of individual learning; and I have liked the term
"becoming" for this collectivity of irreversible change of the
system over time — the "becoming" of the system. The architecture,
essentially constant in time, is its "being," the reversible changes in
time, its "behaving," and the irreversible changes its "becoming."
Let us look at the environment system in a little more detail.

The environment alone is able to induce inhomogeneities in a
homogeneous system; and if the latter is appropriately responsive

358 Information Storage and Neural Control

to particular inhomogeneities, there will be a morphogenetic
action and internal structure will result. Some of you may not
remember the vast argument that occurred near the turn of the
century when the German zoologist Driesch shook apart the two
half cells of a fertilized egg. Normally, of course, one would become
the right side, say, of a frog and the other the left side; but after
separation, each became an intact frog with perfectly good right
and left sides. The outer cell surfaces exposed to pond water
developed skin in the proper fashion, but the medial surfaces,
which became backbone and nervous system when left stuck
together, now also developed skin. This phenomenon caused
Driesch to turn vitalistic and invoke guiding entelechies, but it
was explained decades later by the American zoologist Child in
terins of concentration gradients from outside to center. In the
intact embryos the medial cell surfaces are at the low or high
end of a gradient of oxygen, carbon dioxide, or any other sub-
stance that must diffuse from or into the environment; but in the
separated cells the end of the gradient has moved to the center
of each cell instead of the center of the double cell mass. So,
provided the cell is more than a sac of water and is able to respond
to different oxygen concentrations by different morphological
responses, the organized morphology results from these quantita-
tive changes imposed by the environment.

The same sort of thing operates throughout embryonic develop-
ment. With further cell divisions the germ layers become differ-
entiated and then organs are specified. Often it is only a matter
of minutes between the appearance of the endoderm and the
irrevocable commitment of a given endoderm cell to become a
bit of liver or of gut. In tliis particular case we know what the
environmental determiner is: if the cell is near heart, it becomes
liver; if not, it becomes gut. So environmental influences operate
all the way through ontogenesis, in gated time periods, to produce
firm outcomes.

We are thoroughly familiar with this in many other areas as
well. We can tell what kind of environment a person has lived in
if he has thick soles or horny hands or a weathered face. Frown
or smile wrinkles are inorphological consequences of oft-repeated
behaviors. In this case, the environment of the skin is internal to

Summary arid General Discussion 359

the system (the facial muscles), but this does not alter the principle.
The ontogenesis of an ecological community, i.e., the evolution
of the group roles and structures that form during community,
is similar. Such roles and structures can form only in certain
sequences and at certain stages in the interactions of the indi-
viduals that constitute the "cells" of society, and in time can
become irreversible. These include customs and rules, libraries,
and all sorts of appurtenances that form a morphological substrate
and channel social behavior. And, of course, the engram in the
brain is entirely comparable to horny skin or to bowed legs or
to wrinkles. It is interesting that a time-gated period of specifica-
tion has more recently been found not only in differentiation of
cells but in "imprinting" the nervous system and in fixation of
experience in still other areas. One is inclined to raise the question
of whether the units involved are in a sort of soft-shelled state,
like a molting crab, all at the same time, or whether different
units, particular neuron groups, become impressionable in separate,
temporally ordered periods. This also relates to the earlier argu-
ment on memory, and I shall come back to it.

Now a word about malleability. This, you will remember, refers
to the sensitivity and the specificity of an organism relative to its
environment, particularly to the rain of information from the
environment. Over evolutionary sequences there develops greater
ability to respond, with greater chscrimination, to more kinds and
lesser amounts of such information. In fact, I would urge that
the major theme of organic evolution is what I have called the
epigenetic inode and is not just the ability to respond to the
environment, to learn, or to be molded by it; beyond that, it is
also the ability to be molded more and more easily — to learn to
learn. This learning to learn occurs, I think, at all levels and in
all systems in the course of "becoming," not only in evolution
and history but also in the individual, as psychologists well know.

Several major inventions of life have favored this successful
increase in the ability to learn. Perhaps the first, certainly one of
the very early and important ones, was the invention of an array
of molecules able to replicate themselves and to produce other
particular molecules (in other words, the invention of an array
of genes with sufficient stability and sufficient mutability). This

360 Injormation Storage and Neural Control

permitted very slow evolution. A great speeding up of modi-
fication of the system by environmental impact, i.e., an enhance-
ment of response to the information available, allowed a second
forward step — the invention of sex. This latter maneuver made
it possible to mix the genes in two individuals, to shuffle the cards,
and so get an almost infinite number of hands with the same small
array of individual items.

The third major landmark was the invention of multicellularity.
This made possible the setting off of groups of cells, tissues, and
organs for particular functions, including susceptibility to environ-
mental influences. Multicellularity made possible a meaningful
nervous system, the appearance and steady improvement of which
is the most important invention for us. This evolution over suc-
cessive epochs probably involved an initial improvement of the
individual unit neurons from decrementing to all-or-none con-
duction, from reciprocal to irreciprocal synapses, from lower to
higher speeds, from higher to lower thresholds, and all the rest.
Then there developed better circuitry between the neurons,
including such effective physiological devices as the simple reflex,
the reverberating loop, the negative feedback loop, etc. Two of
the circuits already mentioned are worth a moment.

Dr. Brazier, particularly, referred to one as the "inhibitory
surround." This term emphasizes recent work by investigators such
as Hartline, Hubel, and many others, dealing with the sensory
input, but the mechanism really goes back to Sherrington's
reciprocal inhibition. This mechanism not only cuts in a clean
group of motor neurons to give a shaiply integrated act, very
possibly via the feedback inhibition by Renshaw cells, but it also
operates all through the nervous system. I have suggested in
The Handbook of Neurophysiology that it functions in giving attention
to one or another sensory input or thought train and in shifting
mood sharply, as well as in selecting a behavior. This device
(active units blocking out nearby ones that could have become
engaged in the activity but are in this way kept inactive) is the
basic mechanism for dissecting a graded continuum into sharp
classes. "Nature doesn't come as clean as we can think it," as
Whitehead said, but our whole nervous system and our sense
organs are designed to clean it up for our thought processes.

Summary and Ge?ieral Discussion 361

Perception of an object comes through clean and sharp, and an
act comes through clean and sharp without conflict or blurring
by opposing" elements. Sometimes we err grievously by over-
commitment to a typology, as did the scholastic philosophers; but
without such a commitment we could not think at all, and with
sophistication we can return to graded or probabilistic thinking.
The mechanisms are standard orthodox neurophysiology; their
behavioral consequences are still being explored.

The second neural circuitry worth mentioning — it has received
much attention here — is the double system, discrete and diffuse.
The diffuse system gives the metasignals which are the set. It acts
like the basic adjustments of the television set that make a picture
possible: adjusting brightness and discrimination, locking in the
vertical and horizontal, etc., but not giving the actual picture.
The discrete system presents the picture, the particular pattern
that receives our attention. I have probably oversimplified this
(an example of oversharpening nature) but there is much evidence
for it. The diffuse system can modulate thresholds and responses
of the cortical neurons that are thrown into action initially by
the discrete system; and the diffuse system does affect mood, set,
emotional background, even level of conscious awareness and
attention. The whole question of novelty, stress, anxiety, and
performance has been discussed (Gerard, R. W.: Neurophysiology;
an integration, in, Handbook of Physiology — Neurophysiology III,
Victor E. Hall et al., eds., Amer. Physiol. Society, I960, p. 1919)
in relation to the interaction of the two systems in modifying the
size of a "physiological neuron reserve."

Returning to the overall evolution of the nervous system, the
third stage, after improved units and organized circuits, is increase
in number. The great rise in capacity of the vertebrates, and
particularly of the mammals, is attended, so far as I know, neither
by improvements in the neurons and their connections nor by
any better circuitry. It is a remarkable consequence of simply
adding more of the same. While this is surprising at first, a little
thought recognizes that more of the same can add entirely new
dimensions of richness in performance. In fact, I was struck by
the, I am sure accidental, parallel in the number of base pairs
in genes and of neurons in brains. The small virus has about

362 Information Storage and Neural Control

6,000 base pairs, the mammal close to 10'", according to Dr. Kit.
The simplest animals possess a few hundred or thousand neurons,
man about 10'". Adding more of the same does, indeed, multiply
richness and capacity.

The next major breakthrough in increasing overall malleability
of living things became possible only when the nervous system
had become large enough and sufficiently complex to generate
those new capacities of interaction which led to culture. Culture,
while not completely limited to man, is tremendously more
enveloping for this social animal, and I suggest four sub-epochs
in its development. The first stage of culture probably can be
dated from the invention of the symbol, the use of an arbitrary
sign for a thing, a communicable representation of the outside
world. Next came organized symbols, which are language, as a
tremendous advance, and tested organized symbols, which are
science, as a further great step. I strongly suspect that we are
just entering a fourth epoch in increased malleability of collective
man with the invention and rapid growth of the computer, a
prosthetic instrument for thinking, much as bulldozers are for
muscles and telescopes and microphones are for receptors.

In fact, perhaps the most interesting thing about present-day
man is that the world in which he lives, the one that matters,
that gives problems and satisfactions, is no longer very much a
material world of "things." These have been taken care of. We
have established homeostatic control of our physical and biological
environment so that these no longer present our primary problems.
We live as social beings in an ocean of information, information
that did not exist before we created it. Languages of all sorts,
pictures of all sorts, a great variety of communication means and
contents — these are the things that matter to us. Our interactions
with other human beings, mainly at the symbolic level, are what
we care about. Indeed, the storing, processing, and retrieving of
information at the machine level are undergoing such tremendous
advances that the entire transmittal and use of the information
which is the corpus of our culture will soon be revolutionized.

There is still another exciting aspect of the evolution of mallea-
bility that requires mention. In the earlier phases, this evolution
took place primarily by a biological, Darwinian kind of process;

Sumtnarv and General Discussion 363

later it continues primarily by an environmental social, Lamarckian
kind. I shall return to this shortly, but must first examine the
last major topic, the fixation of information.

For experience to be fixed or information to be stored, there
must be a material change of some kind. If a system is to retain
an enduring difference induced by the environment, not just a
relatively ephemeral change in dynamic state, as a spinning top,
the different responsiveness must rest on a morphological differ-
ence. Such a material change can be only in the number or kind
or position of units, suc'i as ions, molecules, organelles, cells, or
perhaps all of these. One is tempted to look at the macromolecules
because, at least at that level, they are the only units that have
considerable endurance in cells. It is by no means excluded that
the lipids, which endure very well (some of them, once formed,
apparently have no turnov^er during the life of the brain), or the
proteins might be involved; but most investigators interested
in this field have a strong predilection for the polynucleotides.
Moreover, as pointed out earlier in this symposium, there is
growing evidence that implicates them, and there is an especially
intriguing reason for interest in RNA and memory.

DNA molecules produce another generation of DNA, these
produce another generation, and so on. For a series of generations,
the important thing, of course, is that means of replication do
exist and that they are precise enough to give both great stability
and appropriate freedom for change. Change is produced very
gradually over generations with the environment acting primarily
by means of selection. The environment normally does not alter
the DNA molecules, although it is ultimately responsible for the
rare and random genetic mutations. Rather, it selects one or
another set of these molecules in terms of the phenotypes produced
and of the relative degree of their adaptation to the environment.
This is Darwinian evolution — natural selection of certain molecules
from an array of possible DNA molecules or groups of molecules.
But when a given DNA molecule starts to operate in a given
organism, it produces messenger RNA and ribosome RNA and
proteins and enzymes and all tlie rest; and somehow or other this
sequence is under pretty direct control of the environment. Indeed,
it looks as if there is here a Lamarckian kind of influence by the

364

Injormatwn Storage and Neural Control.

DNA

Darwinian selection by Environment

DNA — ^^ Messenger RNA — ^- RNA — ^- protein

Lamarckian modification by environnnent

DNA

Ontogeny

Figure 1

environment (Fig. 1). Just where in the sequence it acts, we do not
know; but a reasonable guess would be that it operates on the
messenger RNA, which is small in amount and relatively unstable,
to modify it in kind or amount or distribution.

This, I think, reveals the nub of the earlier discussion between
Dr. John and Dr. Morrell. The extremely basic question arises:
Must we assume, or is it better to assume, that the environment
operates here by modifying the RNA (or other) molecules, which
is Lamarckianism; or is it possible that, as in genetic selection,
there is a large array of molecules, say a gene-like array of RNA's,
on which environment operates by some kind of selection? I am
sure nobody knows the answer at the moment; the situation does
not have quite the feel of selection to a biologist, but feelings can
be very wrong. Moreover, I would point out that, if molecular
modification is involved, we have not solved the critical problems
when we recognize that this occurs. It is important to get this
far; but some workers have talked as if identifying a memory
trace with a change in RNA is essentially the solution of the
engram. Rather, we are then at the very beginning of our troubles.
Exactly the same problems face us here that faced Lamarck in
getting the giraffe's neck longer. Let mc point out what these
problems are. The environment leads the giraffe to stretch his
neck; somehow stretching the neck generates a substance, or
influence, which goes from the neck to the gonads and produces

Summary and General Discussion 365

a change in the sex cells; this change specifically favors the develop-
ment of a longer neck in the offspring giraffe — a truly formidable re-
quirement, which alone made Lamarckian inheritance improbable.

Our demands are no less. We require, also, transduction from
a process to a structure and back to a process, from information
fiow to information storage to information retrieval. Nerve messages
and events must be fixed in some kind of stable architectural
alteration which favors regeneration of comparable events from
the system. The flow of information is a matter, essentially, of
action at synapses where nerve cells junction. Synapses can vary
only in number, or intensity, which is really equivalent; position;
kind, to some extent, as excitatory or inhibitory; and, of course
the temporal phase of their activity. There are no other parameters,
for these synaptic attributes also express the patterns of neuron
connectivities.

The storage occurs during a period of fixation, as I have called
it, or consolidation, as Dr. Morrell called it, during which a
reversible change becomes irreversible and an enduring memory
is established. This engram probably includes a molecular change
and, as just discussed, may involve production of an altered mole-
cule or selection of a particular molecule from a pre-existent array.
Selection might be in position or in number as well as in archi-
tecture of molecules. Given the molecular change, still further
consolidation processes over time might well involve more gross
morphological changes, such as enlargement of end-feet or actual
sprouting of axon branches (there are many more in old neurons
than in young ones); but this is all guess work. Perhaps there are
only a given number of slots, so to speak, in which memories can
last, although any notion of one memory in one slot is untenable.
There is conclusive physiological and psychological evidence that,
at most, there are different arrays or patterns of neuron groups
which subserve different memories, with some spatial separation
as well as overlap.

Then, finally, we must account for the ability of the particular
morphological residue left by a given pattern of impinging im-
pulses in turn to make the neuron sensitive to just that pattern of
impulses, so that in the future this input can fire the cell more
easily than other inputs.

366 Information Storage and Neural Control

Dr. John made a noble effort to reduce all this to a single
quantitative picture by pointing out that an increase, say, in total
cellular RNA would bind more ions and thereby cut down intra-
cellular potassium which would slow the discharge of the neuron
membrane and the optimal frequency at which it would respond.
Explanations of this sort we eagerly welcome. Many workers are
engaged in such efforts to push understanding further. My own
feeling is that if one reduces the RNA change to a single overall
quantitative parameter, even if parceled out to different cell
regions or membrane areas, there does not remain the necessary
great specificity; but this is certainly a matter of opinion at the
moment. In any event, here are the active growing points of
experiment, as well as theory, in this field.

I shall take a final moment to add to those facts already before
you a few new ones regarding fixation. Dr. Morrell referred to
our earlier work, paralleled independently by others, of giving
an animal a certain learning experience and then, after different
intervals, stopping the activity of the brain. We found that if
brain activity was stopped early enough, either by abrupt cooling
or by massive electric shock, there had not been time for the
experience to become fixed in the nervous system. A hamster or
rat given an electric shock within a few minutes of an experience
had no recollection of the experience; the animal learned nothing,
much like the retrograde amnesia of man after a concussion. The
fixation time so established was fifteen minutes, although changes
continued for fully an hour. To grapple more firmly with the
engram, we wished a more localizing preparation, but without
encroaching on MorrelTs elegant mirror spot technicjue in the
cortex. There has been much argument as to whether the cord
can or cannot fix experience, or learn. Chamberlain, Haleck, and
I decided to follow a clue provided by an Italian physiologist,
Di Giorgio, relating to enduring" postural asymmetries after uni-
lateral lesions in the cerebellum or other cephalad structure.
Many mammals show the phenomenon. We have used rats mainly.

After an asymmetrical lesion, the right hind leg is, say, more
flexed, the left one more extended. Now, of course, if the cord is
cut, the asymmetric streams of descending impulses are stopped
and cord discharges should lapse back to symmetry. This is,

Summary and General Discussion 367

indeed, what happens if the cord is cut within three quarters of
an hour after the start of asymmetry. But if the asymmetry has
been allowed to persist longer than this, and the time discon-
tinuity at forty-five minutes is too sharp for comfort, then the
asymmetry remains for hours or days after the cord is cut. Clearly,
physiological activity has been fixed in cord neurons; and one
has an obvious place to look for shifts in DC potentials across the
cord, in unit activity of motor neurons, in RNA and enzyme
content in various cells in the cord, and the like. Further, we are
examining the influence on fixation time of drugs which speed or
slow the formation of RNA, and Rothschild is making comparable
studies on the learning abilities of rats and mice in various maze
and avoidance situations. It does look as if 8-azaguanine, which
slows RNA formation, slows learning and prolongs fixation time;
and that a malononitrile dimer (Upjohn U9189), which is reported
to speed RNA formation, may have the reverse eflfect. But results
are still coming in and all this is very preliminary.

In any event, inany workers are zeroing in on many prepara-
tions, including the flatworm, and we are really beginning to come
to grips with the problems of information processing" and storing
by the nervous system.

DISCUSSION OF CHAPTER XV

Ralph W. Gerard (Ann Arbor, Michigan): I would like to
invite questions and comments from those who participated in
the symposium. All those in the audience will wish to hear the
views of the participants on what some other speaker has said.

Frank Morrell (Palo Alto, California): Dr. Gerard, may we
ask you to amplify the details of this beautiful experiment. I
would particularly like to know the details of how the operation
to produce asymmetry was done, whether drugs influence this,
and whether, for exainple, the same relations exist if such an
operation is performed using anesthesia.

Gerard: The preparation is made using anesthesia, and time
is from the appearance of asymmetry, not from the time of the cord
cut. Anesthesia (ether or nembutal) is light, and the animal is ordin-

368 Information Storage and Neural Control

arily pretty well out when asymmetry appears. Before that, presum-
ably, impulses coming down the cord have not been effective.

E. Roy John (Rochester, New York): I would like to mention a
couple of experiments related to your remarks and ask if you
would react to them. I ain sure the first one will be of interest
to you, although it is not directly related to the question of memory
in the nervous system, but rather to your comments on the loss
of plasticity and functional specialization in tissue. The data are
contained in a recent paper by Buchsbaum in the Journal oj
Experimental ^oology. He and his co-workers were trying to develop
a planarian tissue culture inethod, and succeeded in making a
pleasantly simple medium in which explants were grown. They
observed that a small explant occasionally proliferated as a sheet,
reached a certain size, folded back on itself, apparently dedifferenti-
ated, and developed into a planarian. This rather unexpected
observation suggests that, at least at this level, the loss of plasticity
with specialization is reversible.

More directly relevant to our major concern here is the recent
paper by Sporn and Dingman in the Journal of Psychiatric Research
in which 8-azaguanine was used to interfere with RNA synthesis,
and a significant decrease in the rate of maze learning was ob-
served. I would also like to mention the on-going thesis work of
Eugene Sachs, in our laboratory, which may provide additional
insight into aspects of information storage.

Some time ago, in collaboration with Wenzel and Tschirgi, we
observed that small intraventricular injections of electrolytes seri-
ously interfered with the performance of some previously estab-
lished conditioned responses. Mr. Sachs has investigated the effects
of small alterations in central potassium or calcium on learning
and performance by making intraventricular injections before
each training session. Control groups are first trained, and then
receive an appropriate number of central injections. Sachs' results
indicate that animals perform conditioned responses best under
conditions of central electrolyte concentration like those present
during training and poorly under other conditions, including
normal cerebrospinal fluid concentration. Control groups that
receive the injections after training show no evidence of accom-
modation effects. In these animals, central injection causes per-

Summary and Ge?ieral Discussion 369

formance deterioration, while in the annnals in which these changes
were present during learning, performance continues perfectly.
Certain chemical changes seem to facilitate learning, while others
slow it. These groups showed differential sensitivity to drugs many
months after training, indicating that the effects of the small elec-
trolyte shifts are long lasting. These various findings show that
very small local electrolyte shifts seem capable of affecting the
long-term storage of an experience in such a way that readout
is optimal when the electrolyte microenvironment of the readout
mechanism resembles the situation during the initial experience.

I would like to add just one thing. We have replicated the
cannibalism experiments of McConnell using a blind procedure.
It seems to me that the most striking evidence in favor of a sequence
specificity model comes from such studies.

Gerard: Let me answer your second question about the aza-
guanine findings first. That paper appeared while our own experi-
ments were in progress and were coming out the same way. As
to your rounded-up planaria, I seem to remember that Child and
Hyman got smaller segments to regenerate, but this is an unim-
portant detail. I am not quite sure what you are asking of me.
Maybe another question will help: When the cells reorganize
inside such a sheath or coating and are all mixed up, if you have
previously trained them, do they remember?

John: That is one reason why we are doing tissue culture ex-
periments. I do not know yet.

Gerard: Regarding your electrolyte shift, this strikes me as
exactly what would be expected, on the following argument.
Small shifts in the calcium-potassium ratio produce large changes
in the neuron thresholds; high potassium lowers threshold, high
calcium raises it. If you have done your conditioning under one
set of thresholds of the neuron group, tlien the engram set up
would be congruent with that distribution of neuron thresholds
in that neuron population. Having once established the pattern,
which is more difficult with calcium and easier with potassium,
you would need the same balance of thresholds that then existed
in order to re-evoke the engram in a given assembly of cells,
because various cell thresholds do not change exactly in proportion
to the ion ratio. If learnins; was under the normal ion ratio, then

370 Information Storage and Neural Control

any shift in ion balance would disturb it. It seems to me this is
exactly what one would expect.

John: That is one possible explanation. Yet experiences learned
under normal circumstances may be retrieved in situations in
which it is unreasonable to argue that the configuration of excita-
bility of neuron populations is quite as it was during the experience.
An alternative to your suggestion might be that the altered
electrolyte surround directly affects storage mechanisms on the
molecular level.

Gerard: It is a matter of how it has shifted. These are probably
very big shifts, even with small amounts of electrolytes. You
remember the work Ochs did with the Bures potassium technique.
It is a nice way of locating the engram, besides the split-brain
technique. He had rats learn a performance with one hemisphere
inhibited with high potassium chloride. He removed this and the
animals behaved perfectly well. But sometime later, when he
blocked the other hemisphere with potassium chloride, with the
first still ticking away happily, the rats had no knowledge of what
they had learned. The engram was in only the part of the brain
which was active during learning. It is this kind of an effect, I
think, that you are dealing with.

Let me discuss your last question. Maybe we should not go
into it because this whole planaria business, while fascinating, is
a bit off the line of the discussion. You may not know, though,
that your student Corning turned up with Jim McConnell and
reported the RNAse results, but could not interpret them. My inter-
pretation, and I think the one you have used, seemed reasonable.

Let me remind the group of the basic experiment. A flatworm is
trained, cut in two pieces, and the head allowed to regenerate a
tail and the tail a head. Both new worms remember, as McConnell
demonstrated. Dr. John and his group showed, further, that if each
of these two parts is regenerated in RNAse, the head worm still re-
members but the tail worm does not. One can explain this in
terms of the fact that the head worm has more organized structural
units in it to begin with and does not have to re-create many neu-
rons. Now, would something of this kind apply to your question?

John: I am sorry. I am talking about the cannibalism studies.
One group of planaria is fed shredded, trained worms. Another

Summary and General Discussion 371

group of planaria is fed shredded, naive worms. On subsequent
training, the group which ate trained worms was found to acquire
that conchtioned response significantly more rapidly than the
group which ate naive worms. This experiment was run blind
in our laboratory, and the results confirmed previous reports by
McConnell's group. One is probably justified in assuming the
absence of enzymes in the planarian gut, which would degrade
macromolecules. The reason I refer to this work is to ask what
sort of mechanism you would suggest to account for these results.

Gerard: WeU, it is even more unbelievable than the earlier
stuff, but I still tliink that what I was saying was relevant to your
question. I would have to assume that these informed molecules
are not completely degraded in being digested and absorbed, and
so supply templates on which the organized learning can be based,
just as for the tail regrowing a head with its neurons.

I wonder if we should not let some of tlie other people get in
before pushing this one point.

Morrell: I had hoped to get a specific comment on the plausi-
bility of my suggestion for chemical "protection." I wonder
whether a possible mechanism for preservation of an imposed
shift in charge distribution might be the bonding of the charged
moiety to phospholipid. Conceivably such bonding might not
only protect this given molecular rearrangement, but also fix it
to sites within the membrane where influences on synaptic trans-
mission might be expected. There is some evidence by Tobias
which indicates that axons treated with proteases continue to
conduct action potentials for many hours, while treatment with
lipase rapidly abolishes conduction. Tobias (personal communi-
cation) has now found that similar treatment with ribonuclease
also impairs the capacity of the axon to generate action potentials.
Moreover, there is some preliminary evidence from Dr. Herzenberg
(personal communication) to the effect that the DNA-RNA speci-
fication system may not only regulate protein synthesis but also
influence molecules containing phospholipid. In fact, these lipid
molecules are antigenic and thus conceivably could provide a
chemical mechanism for cell recognition.

Gerard: I think that is fine to have on the record. I had rather
not push it, although I must say that I heard recently that Tobias'

372 Information Storage and Neural Control

finding, which I have also quoted with enthusiasm, is under
question as to whether the hpase at the pH and ionic strength
used was acting on hpids or exhibiting its other, venomlike, action.
So this may not hold.

Saul Kit (Houston, Texas) : Dr. John's question is a very
complicated one. I believe I would have to discuss it with him to
understand fully all of its implications. I think we should be very
careful in extrapolating from the molecular biology level to the
neurophysiology frame of reference. I should prefer, therefore, to
let Dr. Gerard's answer stand.

Gregory Bateson (Palo Alto, California) : This is changing the
subject somewhat, but going back to what Dr. Gerard said about
evolution and the relations between Lamarckian and Darwinian
theories, there are some rather peculiar problems in the economics
of communication within the organism which indicate, at first
glance, that neither the Lamarckian nor the Darwinian system
will work. Let me put it this way. We have an organism. We
describe it at any given time or over any given finite time in terms
of all necessary variables to define all possible states — Vi, V2, . . . ,
V„ — perhaps many thousands of variables. Any one of these has
a finite set of values. If the organism exceeds any of these finite
thresholds, it dies.

Now consider a pre-girafTe which has the good fortune to get
the mutant "long neck" as an item in the genotypic corpus of
genes. That genotypic system is not going to tell the heart of the
giraffe that it now has to enlarge in order to supply the head with
blood. It is not going to deal with the new problems of the inter-
vertebral disks. It is not going to solve all sorts of other new
somatic problems which, in fact, the happy giraffe, the lucky
giraffe, is going to have to deal with at the somatic level. The
giraffe is going to have to occupy servo-circuits within its soma
to modify the size of the heart, and so on. By doing so, it has
reduced the finite set of possible states of its organism.

Later, this pre-giraffe is lucky enough to get another externally
adaptive mutation — let us say big feet, which it needs for kicking lions.
It is now again limited to a subset of its possibilities; and if it has to deal
with both mutations simultaneously, it is limited to that overlapping
subset of possibilities which is compatible with both mutations.

Summary and General Discussion 373

You see that very soon a sequence of externally adaptive muta-
tions of this sort is going to lead to a nonviable giraffe. It is using
up its somatic flexibility with every adaptive mutation that it gets.
The only way it can regain flexibility is by getting those mutations
which will enlarge its heart or do whatever is necessary to cope
with the externally adaptive changes. It has to shift some of its
acc|uired characteristics from the somatic servo-systems to the
soldered-in genotypic systems.

The system can only work if there is a comparatively large
number of mutations which will simulate a Lamarckian process,
and evidently God set it up this way to deceive the Russians.

Now, let us look at the other side of the picture. Suppose the
system were set up on Lamarckian lines. The genotype would
then have to pick up from the soma (and it is difficult enough to
imagine it picking up anything) those particular acquired charac-
teristics which are the essential ones. But the enlarged heart is
not just an enlarged heart. It is one item in a general shift in value
all around servo-circuits to enlarge that heart. All those values
at other points around those circuits are going to be picked up
in a Lamarckian system, passed on by inheritance, and soldered
into the genotype. In fact, a Lamarckian system will very rapidly
gum up the works by decreasing the somatic flexibility just as
badly as the Darwinian system.

We face, therefore, an economics of communicational pathways.
Evolution will only work if you have one system (the genotype)
relatively independent of the other (the somatic), with natural
selection playing on the whole thing. You have to have a digital
genotype, soldered in, with random changes, and you have to
have a system (the soma) of analogue operations. The soma is
being, so to speak, a trial model to test the genotype. The hen is
the egg's way of finding out if it was a good egg or not.

The whole economics of the system depends upon keeping the
soma and the genotype separate. If you are right in saying that
cultural evolution is something much more like a Lamarckian
system, I think we may look forward to considerable chaos in
the culture. The genotype is the analogue of a legislator. He can
only afford to make those changes which affirm changes that have
already occurred at the somatic or popular level. If we live in a

374 Information Storage and Neural Control

Lamarckian system in which the lower levels are maximally able
to affect the higher ones, then perhaps we are headed for chaos.

Gerard: I am sorry we are talking about the giraffe. It seems
a camel would be more appropriate. As you know, the definition
of a camel is an animal made by a committee. This seems to be
the problem you are bothered about. I also think that, in a sense,
it should be a camel, because I let his head under the tent and
you have brought the whole animal in. The issues you are raising
are really not too close to the basic one of the fixation of the experi-
ence as I was trying to discuss it. Let me simply say this in response
to these important considerations.

As you know, this difficulty — the fact that there must be multiple
changes that interact with each other — has been recognized by
evolutionary theorists for a long time. One of the earliest criticisms
of natural selection was that it could explain the survival of the
fittest but not the arrival of the fittest. I think this is partly what
you are raising. I am certainly no expert in the field of evolution,
but I have been in close touch with many of the experts in this
field over many years. They remain an absolutely solid phalanx
on selection as an adequate and satisfactory mechanism for
evolution, without bringing in Lamarckianism. Waddington and
Dobzhansky have recognized very clearly the fact that natural
selection has favored mutable genes, which is a bit in your direction.

Hyman Olken (Livermore, California): I have one question
I would like to ask Dr. Morrell. Bottley pointed out that if you
increase the frequency of light pulses toward a certain value, you
get increased response; then if you increase beyond that frequency,
the response decreases. Would that have any effect on the results
that you pointed out yesterday where you tested the memory of
certain frequencies and recovered other ones?

Morrell: Well, it would have an influence on the detectability
of any frequency in the system with which we were working. You
could see from the illustrations that frequencies beyond seven, say,
would gradually fill in the interval, and you could not possibly
count a frequency; therefore, it would be undetectable with these
methods.

Max E. Valentinuzzi (Atlanta, Georgia): I think that this
is the appropriate moment to bring up three questions which have

Summary and General Discussion 375

not been answered as yet. They are related to the amount of
information necessary to transmit or to organize one unit of
information. As you know, it is not possible to transmit information
if there is not a previous amount of information available as a
storage unit. So, the first question is: How many units of infor-
mation do we need as a minimum to store one unit of information?
The second question is: How much energy is necessary to organize
one unit of information? The third question is: How much energy
is necessary to transmit from one point to another the same unit
of information?

Warren S. McCulloch (Cambridge, Massachusetts): Do the
first two questions amount to how much information you have to
have to make another unit of information? This is one of the nasty
cjuestions that is puzzling us at the present time. There is a way
of approaching it, but no one is happy about it. You cannot say
in a simple way, "How much for unit?"; but you can ask — and
it is the famous question put by John von Neumann — "How
much of a computing machine do you have to have for that
computing machine to make more?" This is the same question;
you have the problem of the generation of a computer, and it
does not matter whether you make it formally or make it in hard-
ware. The actual problem is that of starting with no form. This
means starting from noise, and from noise it is hard to get anything,
to generate any form. The answer is that nobody knows how much
information you need.

Gerard: What about the second question on the energy for
transmitting the unit of information?

McCulloch: With regard to the last question, as small an
amount of energy as you can get in one packet can carry one bit.
The limit is strictly that of the physics.

Kit: I wonder if this question is not too general. Should we
not be thinking about the kind of information that we are storing,
transmitting, and replicating? I think estimates could be made
of the amount of energy needed to replicate a DNxA molecule.
Also, one can measure the amount of energy consumed by a
bacterial cell during the replication of the DNA of a phage. This
measured value will be greater than the amount needed for phage
DNA synthesis and presumably will be an upper limit of the

376 Informatwn Storage and Neural Control

amount of energy needed. However, I feel diat if we investigate
another information system, the amount of energy required to
make anotlier unit of information might be very different.

McCulloch: Light does not come in packets smaller than a
single photon, and from Bowman's figures one photon can excite.
That is the lowest figure that anybody has and the lowest anyone
will ever have.

Gerard: Time has gone on. It is now my privilege and pleasure,
since I am acting as moderator at the moment, to thank the
organizers of this symposium, the Houston Neurological Society
and Baylor University, the various local people who have been
kind to us, and, above all, the speakers who have given us such
interesting material. We are adjourned.

APPENDIX A

INTRODUCTION

Michael H. Arbib

LOGICAL Calculus of the Ideas Immanent in Nervous
Activity" by Warren S. McCulloch and Walter Pitts is the classic
paper on neurophysiological automata theory and still merits
reading today, almost twenty years after its publication. Section I.
which gives the neurophysiological basis for the model, is still valid
in all its essentials and remains the most readable discussion of this
basis. Section II, on the theory of nets without circles, and the dis-
cussion of Section IV are equally excellent.

However, Section III, the theory of nets with circles, was only
intended as a sketchy account. It was presented in Carnap's
notation, which was not apt for the task at hand, and is incomplete,
hard to read, and contains many errors. Hence, for this part of
the theory, we advise the reader to turn to more recent publications.
The theory of nets with circles was first fully worked out by
Kleene (1) and has since been given an elegant re-presentation
by Copi, Elgot, and Wright (2). The assertions of McCulloch and
Pitts concerning the connection between the neural nets and Turing
machines [Turing (3)] have been fully worked out by Arbib (4).

REFERENCES

\. Kleene, S. C: Representation of Events in Nerve Nets and Finite
Automata. In Automata Studies, ed. by C. E. Shannon and J. Mc-
Carthy, Princeton, Princeton University Press, 1956, p. 3.

2. Copi, I. M., Elgot, C. C, and Wright, J. B.: Reahzation of events by

logical nets. J. Assn. Computing Mchy., 5.- 181-1 96, 1958.

3. Turing, A. M.: On computable numbers, with an application to the

Entscheidungs-problem. Proc. London Math. Soc. (2) ^i.-230-265,
1936; with a correction, ibid., 43:544-546, 1947.

4. Arbib, M.: Turing machines, finite automata and neural nets. J.

Assn. Computing Mchy., 8:461-415, 1961.

377

A LOGICAL CALCULUS OF THE
IDEAS IMMANENT IN NERVOUS ACTIVITY*

Warren S. McCulloch and Walter H. Pitts

Because of the "all-or-none" character of nervous activity,
neural events and the relations among them can be treated by
means of propositional logic. It is found that the behavior of
everv net can l:)e described in these terms, with the addition of
more complicated logical means for nets containing circles; and
that for any logical expression satisfying certain conditions, one
can find a net behax'ing in the fashion it describes. It is shown
that many particular choices among possible neurophysiological
assumptions are equivalent, in the sense that for every net be-
having under one assumption, there exists another net which
behaves under the other and gives the same results, although
perhaps not in the same time. Various applications of the calculus
are discussed.

INTRODUCTION

HEORETIClAL neurophysiology rests on certain cardinal as-
sumptions. The nervous system is a net of neurons, each having a
soma and an axon. Their adjunctions, or synapses, are always be-
tween the axon of one neuron and the soma of another. At any in-
stant a neuron has some threshold, which excitation must exceed to
initiate an impulse. This, except for the fact and the time of its
occurrence, is determined by the neuron, not by the excitation.
From the point of excitation the impulse is propagated to all parts
of the neuron. The velocity along the axon varies directly with its
diameter, from less than one meter per second in thin axons,
which are usually short, to more than 150 ixieters per second in
thick axons, which are usually long. The time for axonal conduc-
tion is consequently of little iinportance in determining the tiine

*Reprinted from The Bulletin of Mathematical Biophysics, 5:115-133. 1943, with
permission of the Editor, N. Rashevsky.

379

380 Information Storage and Neural Control

of arrival of impulses at points unequally remote from the same
source. Excitation across synapses occurs predominantly from
axonal terminations to somata. It is still a moot point whether
this depends upon irreciprocity of individual synapses or merely
upon prevalent anatomical configurations. To suppose the latter
requires no hypothesis ad hoc and explains known exceptions, but
any assumption as to cause is compatible with the calculus to
come. No case is known in which excitation through a single syn-
apse has elicited a nervous impulse in any neuron, whereas any
neuron may be excited by impulses arriving at a sufficient number
of neighboring synapses within the period of latent addition, which
lasts less than one quarter of a millisecond. Observed temporal
summation of impulses at greater intervals is impossible for single
neurons and empirically depends upon structural properties of the
net. Between the arrival of impulses upon a neuron and its own
propagated impulse there is a synaptic delay of more than half
a millisecond. During the first part of the nervous impulse the
neuron is absolutely refractory to any stimulation. Thereafter its
excitability returns rapidly, in some cases reaching a value above
normal from which it sinks again to a subnormal value, whence
it returns slowly to normal. Frequent activity augments this sub-
normality. Such specificity as is possessed by nervous impulses
depends solely upon their time and place and not on any other
specificity of nervous energies. Of late only inhibition has been
seriously adduced to contravene this thesis. Inhibition is the ter-
mination or prevention of the activity of one group of neurons by
concurrent or antecedent activity of a second group. Until recently
this could be explained on the supposition that previous activity
of neurons of the second group might so raise the thresholds of
internuncial neurons that they could no longer be excited by
neurons of the first group, whereas the impulses of the first group
must sum with the impulses of these internuncials to excite the
now inhibited neurons. Today, some inhibitions have been shown
to consume less than one millisecond. This excludes internuncials
and requires synapses through which impulses inhibit that neuron
which is being stimulated by impulses through other synapses.
As yet experiment has not shown whether the refractoriness is
relative or absolute. We will assume the latter and demonstrate

.1 Logical Calculus of the Ideas Immanent in Jservous Activity 381

that tlie difference is immaterial to our argument. Either variety
of refractoriness can be accounted for in eitlier of two ways. The
"inhibitory synapse" may be of such a kind as to produce a sub-
stance whicii raises the tlireshold of the neuron, or it may be so
placed that the local chsturbance prockiced by its excitation
opposes the alteration induced by tlie otlierwise excitatory syn-
apses. Inasmuch as position is already known to have such effects
in the case of electrical stimulation, the first hypothesis is to be
excluded unless and until it be substantiated, for the second
involves no new hypothesis. We have, then, two explanations of
inhibition based on the same general premises, differing only in
the assumed nervous nets and, consecjuently, in the time required
for inhibition. Hereafter we shall refer to such nerv'ous nets as
equivalent in the extended sense. Since we are concerned with properties
of nets which are invariant under equivalence, we may make the
physical assumptions which are most convenient for the calculus.
Many years ago one of us, by considerations impertinent to
this argument, was led to conceive of the I'esponse of any neuron
as factually equivalent to a proposition which proposed its ade-
quate stimulus. He therefore attempted to record the behavior of
complicated nets in the notation of the symbolic logic of proposi-
tions. The "all-or-none" law of nervous activity is sufficient to
insure that the activity of any neuron may be represented as a
proposition. Physiological relations existing among nervous activ-
ities correspond, of course, to relations among the propositions;
and the utility of the representation depends upon the identity
of these relations with those of the logic of propositions. To each
reaction of any neuron there is a corresponding assertion of a
simple proposition. This, in turn, implies either some other simple
proposition or the disjunction or the conjunction, with or without
negation, of similar propositions, according to the configuration
of the synapses upon and the threshold of the neuron in question.
Two difficulties appeared. The first concerns facilitation and ex-
tinction, in which antecedent activity temporarily alters responsive-
ness to subsequent stimulation of one and the same part of the
net. The second concerns learning, in which activities concurrent
at some previous time have altered the net permanently, so that
a stimulus which would previously have been inadequate is now

382 Information Storage and Neural Control

adequate. But for nets undergoing both alterations, we can sub-
stitute equivalent fictitious nets composed of neurons whose con-
nections and thresholds are unaltered. But one point must be
made clear: neither of us conceives the formal equivalence to be
a factual explanation. Per contra! — we regard facilitation and
extinction as dependent upon continuous changes in threshold
related to electrical and chemical variables, such as after-potentials
and ionic concentrations; and learning as an enduring change
which can survive sleep, anaesthesia, convulsions and coma. The
importance of the formal equivalence lies in this: that the altera-
tions actually underlying facilitation, extinction and learning in
no way affect the conclusions which follow from the formal treat-
ment of the activity of nervous nets, and the relations of the
corresponding propositions remain those of the logic of propositions.
The nervous system contains many circular paths, whose ac-
tivity so regenerates the excitation of any participant neuron that
reference to time past becomes indefinite, although it still implies
that afferent activity has realized one of a certain class of con-
figurations over time. Precise specification of these implications
by means of recursive functions, and determination of those that
can be embodied in the activity of nervous nets, completes the
theory.

THE THEORY: NETS WITHOUT CIRCLES

We shall make the following physical assumptions for our cal-
culus.

1. The activity of the neuron is an ^'all-or-none" process.

2. A certain fixed number of synapses must be excited within
the period of latent addition in order to excite a neuron at any
time, and this number is independent of previous activity and
position on the neuron.

3. The only significant delay within the nervous system is syn-
aptic delay.

4. The activity of any inhibitory synapse absolutely prevents
excitation of the neuron at that time.

5. The structure of the net does not change with time.

A Logical Calculus of the Ideas Immanent in jYervous Activity 383

To present the theory, the most appropriate symbolism is that
of Language II of R. Carnap (1938), augmented with various
notations drawn from B. Russell and A. N. Whitehead (1927)
including the Pnncipia conventions for dots. Typographical neces-
sity, however, will compel us to use the upright 'E' for the existen-
tial operator instead of the inverted, and an arrow ('-^') for
implication instead of the horseshoe. We shall also use the Carnap
syntactical notations, but print them in boldface rather than
German type; and we shall introduce a functor S, whose value
for a property P is the property which holds of a number when P
holds of its predecessor; it is defined by 'S{P) (/) . = . P(A'.v) . / = .v')';
the brackets around its argument will often be omitted, in which
case this is understood to be the nearest predicate-expression [Pr]
on the right. Moreover, we shall write S-Pr for S{S{Pr)), etc.

The neurons of a given net '^ may be assigned designations
'^I'j '^2', . . . , 'c„'. This done, we shall denote the property of a
number, that a neuron c, fires at a time which is that number of
synaptic delays from the origin of time, by ^A^' with the numeral
i as subscript, so that N ,{t) asserts that c, fires at the time t. N, is
called the action of c,. We shall sometimes regard the subscripted
numeral of ' N' as if it belonged to the object-language, and were
in a place for a functoral argument, so that it might be replaced
by a number-variable [z] and quantified; this enables us to abbre-
viate long but finite disjunctions and conjunctions by the use of
an operator. We shall employ this locution quite generally for
sequences of Pr\ it may be secured formally by an obvious dis-
junctive definition. The predicates '.Vi', '.V^', . . . , comprise the
syntactical class ' N\

Let us define the peripheral afferents of V)! as the neurons of ^^I
with no axons synapsing upon them. Let N,, . . . ^ N^ denote the
actions of such neurons and A^,,+i, N,^,, . . . , N„ those of the rest.
Then a solution of VX will be a class of sentences of the form S-
A^p+i (21) . ^ . Pr, {N,, N,, ... , N„ 2i), where Pr, contains no
free variable save Zi and no descriptive symbols save the A'' in the
argument [Arg], and possibly some constant sentences [sa]; and
such that each S, is true of VX. Conversely, given a Pvi {^i, ^2
^p\, Zi, s), containing no free variable save those in its Arg, we
shall say that it is realizable in the narrow sense if there exists a net 9l

384 Information Storage and Neural Control

and a series of A^, in it such that M (zi) . = . Pr^ (M, A^o, •• • ,
Zi, sai) is true of it, where sax has the form A^(0). We shall call it
realizable in the extended sense, or simply realizable, if for some n S"{Pri)
ipi, • ... pp. Zu s) is realizable in the above sense. Cp, is here the
realizing neuron. We shall say of two laws of nervous excitation
which are such that every S which is realizable in either sense
upon one supposition is also realizable, perhaps by a different
net, upon the other, that they aie equivalent assumptions, in

that sense.

The following theorems about realizability all refer to the ex-
tended sense. In some cases, sharper theorems about narrow
realizability can be obtained; but in addition to greater com-
plication in statement this were of little practical value, since our
present neurophysiological knowledge determines the law of ex-
citation only to extended equivalence, and the more precise
theorems differ according to which possible assumption we make.
Our less precise theorems, however, are invariant under equiva-
lence, and are still sufficient for all purposes in which the exact
time for impulses to pass through the whole net is not crucial.
Our central problems may now be stated exactly: first, to find
an effective method of obtaining a set of computable S constituting
a solution of any given net; and second, to characterize the class
of realizable S in an effective fashion. Materially stated, the
problems are to calculate the behavior of any net, and to find a
net which will behave in a specified way, when such a net exists.
A net will be called cyrlic if it contains a circle: i.e., if there
exists a chain c„ C/+i , ... of neurons on it, each member of the
chain synapsing upon the next, with the same beginning and end.
If a set of its neurons Ci , c-i , . . . , Cp is such that its removal from
VX leaves it without circles, and no smaller class of neurons has this
property, the set is called a cj>clic set, and its cardinality is the
order o/vX. In an important sense, as we shall see, the order of a
net is an index of the complexity of its behavior. In particular,
nets of zero order have especially simple properties; we shall
discuss them first.

Let us define a temporal propositional expression (a TPE), desig-
nating a temporal propositional function {TPF), by the following
recursion:

A^,

A Logical Calculus of the Ideas Immanent in Nervous Activity 385

1. A^p^ [zi] is a TPE, where Pi is a predicate-variable.

2. If Si and So are TPE containing" the same free individual
variable, so are SS\, SivSo, Si.S-> and S,. ^-^ S2.

3. Nothing else is a TPE.

Theorem I

Every net of order 0 can be solved in terms of temporal propositional
expressions.

Let Ci be any neuron of V^l with a threshold 6, > 0, and let Cn,
Ci2, . •• , (',p have respectively //,i, '?,2, •• • , n,p excitatory synapses
upon it. Let Cj], r,2, • • • , ^'jy have inhibitory synapses upon it.
Let Ki be the set of the subclasses of \n,i, n,2, •• • , fi,p\ such that
the sum of their members exceeds 6,. We shall then be able to
write, in accordance with the assumptions mentioned above,

where the 'E' ^'^^ 'H' are syntactical symbols for disjunctions
and conjunctions which are finite in each case. Since an expression
of this form can be written for each C; which is not a peripheral
afferent, we can, by substituting the corresponding expression in
(1) for each A''^,,, or A'',,- whose neuron is not a peripheral afferent,
and repeating the process on the result, ultimately come to an
expression for A^, in terms solely of peripherally afferent A^, since
^^l is without circles. Moreover, this expression will be a TPE,
since obviously (1) is; and it follows immediately from the definition
that the result of substituting" a TPE for a constituent p{z) in a
TPE is also one.

Theorem II

Every TPE is realizable by a net of order zero.

The functor .9 obviously coi"nmutes with disjunction, conjunction,
and negation. It is obvious that the result of substituting any S,,
realizable in the narrow sense (i.n.s.), for the p{z) in a realizable
expression Si is itself realizable i.n.s.; one constructs the realizing
net by replacing the peripheral afferents in the net for Si by the
realizing" neurons in the nets for the Si. The one neuron net

386 Information Storage and Neural Control

realizes p\{z\) i.n.s., and Figure 1-a sliows a net tliat realizes
Spi{zi) and hence SS-i, i.n.s., if So can be realized i.n.s. Now if
So and S3 are realizable then S"'S-2. and S"Sz are realizable i.n.s.,
for suitable m and n. Hence so are S^'^'^So and ^''""'""Sa. Now the
nets of Figures lb, c and d respectively realize S{pi{zi)\ p2{z\)),
S{pi{zi) . p2{zx)), and S\pi{z,) . ~ poiz,)) i.n.s. Hence S'-+"+' (SiV
S2), ^"'+"+1 (Si . So), and ^''«+"+i (Si . ~ So) are realizable i.n.s.
Therefore Si v SoSi . SoSi . ~ So are realizable if Si and So are.
By complete induction, all TPE are realizable. In this way all
nets may be regarded as built out of the fundamental elements
of Figures la, b, c, d, precisely as the temporal propositional ex-
pressions are generated out of the operations of precession, dis-
junction, conjunction, and conjoined negation. In particular,
corresponding" to any description of state, or distribution of the
values true and false for the actions of all the neurons of a net save
that which makes them all false, a single neuron is constructible
whose firing is a necessary and sufficient condition for the validity
of that description. Moreover, there is always an indefinite number
of topologically different nets realizing any TPE.

Theorem III

Let there be given a complex sentence Si built up in any manner out
of elementary sentences of the form p(zi — zz) where zz is any numeral,
by ary of the propositional connections: negation, disfunction, conjunction,
implication, and equivalence. Then Si is a TPE and only if it is false
when its constituent p(zi — zz) are all assumed false — i.e., replaced
by false sentences — or that the last line in its truth-table contains an
'F', — or there is no term in its Hilbert disjunctive normal form com-
posed exclusively of negated terms.

These latter three conditions are of course equivalent (Hilbert
and Ackermann, 1938). We see by induction that the first of them
is necessary, since p{zi — zz) becomes false when it is replaced
by a false sentence, and Si v So, Si . S2 and Si . ~ S2 are all
false if both their constituents are. We see that the last condition
is sufficient by remarking that a disjunction is a TPE when its
constituents are, and that any term

Si . So . . . . Sm . -^ S,„+i . '^ . . . . -^ s„
can be written as

A Logical Calculus of the Ideas Immanent in Nervous Activity 387

(Si . So ... . S„0 . ~ {Sm+xV S,n + lV . . . .V S„),

which is clearly a TPE.

The method of the last theorems does in fact provide a very
convenient and workable procedure for constructing nervous nets
to order, for those cases where there is no reference to events
indefinitely far in the past in the specification of the conditions.
By way of example, we may consider the case of heat produced
by a transient cooling.

If a cold object is held to the skin for a moment and removed,
a sensation of heat will be felt; if it is applied for a longer time, the
sensation will be only of cold, with no preliminary warmth, how-
ever transient. It is known that one cutaneous receptor is affected
by heat, and another by cold. If we let Ni and A^2 be the actions
of the respective receptors and N?. and A^4 of neurons whose
activity implies a sensation of heat and cold, our requirements
may be written as

N^{t) : = : A'i(/-1) . v . N^.{t-^) . ^N~,{t-2)

Ndt) . = .No(t-2) .No(t-l)

where we suppose for simplicity that the required persistence in
the sensation of cold is, say, two synaptic delays, compared with
one for that of heat. These conditions clearly fall under Theorem
III. A net may consequently be constructed to realize them, by
the method of Theorem II. We begin by writing them in a fashion
which exhibits them as built out of their constituents by the
operations realized in Figures la, b, c, d: i.e., in the form

N^(t) . ^ . S{A\it) V S[{SN,{t)) >'^N,(t)]}
N,(t) . ^ . S{[SN,{t)] .N,{t)].

First we construct a net for the function enclosed in the greatest
number of brackets and proceed outward; in this case we run a
net of the form shown in Figure la from Co to some neuron r„, say,
so that

Nait) . = . SN,(t).
Next introduce two nets of the forms Ic and Id, both running
from Ca and c^, and ending respectively at Ci and say Cb. Then

A^4(0 . = . S[NAt) . N,it)] . ^ . S[(SN2(t)) . N,(t)].

388 Information Storage and Neural Control

Finally, run a net of the form lb from C\ and Cb to fs, and derive

.¥3(0 . ^ . .S[.Vi(Ov.V,(0]

. ^ . .StVi(0 v.S'[GSWo(0) . ~A'2(0]1.

These expressions for N z{t) and iV4(/) are the ones desired; and
the realizing net in toto is shown in Figure le.

This illusion makes very clear the dependence of the correspond-
ence between perception and the "external world'' upon the
specific structural properties of the intervening nervous net. The
same illusion, of course, could also have been produced under
various other assumptions about the behavior of the cutaneous
receptors, with correspondingly different nets.

We shall now consider some theorems of equivalence: i.e.,
theorems which demonstrate the essential identity, save for time,
of various alternative laws of nervous excitation. Let us first dis-
cuss the case of relative inhibition. By this we mean the supposition
that the firing of an inhibitory synapse does not absolutely prevent
the firing of the neuron, but merely raises its threshold, so that
a greater number of excitatory synapses must fire concurrently
to fire it than would otherwise be needed. We may suppose, losing
no generality, that the increase in threshold is unity for the firing
of each such synapse; we then have the theorem:

Theorem IV

Relative and absolute inhibition are equivalent in the extended sense.

We may write out a law of nervous excitation after the fashion
of (1), but employing the assumption of relative inliibition instead;
inspection then shows that this expression is a TPE. An example
of the replacement of relative inhibition by absolute is given by
Figure If. The reverse replacement is even easier; we give the
inhibitory axons afferent to c, any sufficiently large number of
inhibitory synapses apiece.

Second, we consider the case of extinction. We may write this
in the forni of a variation in the threshold 6, after the neuron Ct
has fired; to the nearest integer — and only to this approximation
is the variation in threshold significant in natural forms of excita-
tion— this may be written as a sequence di + bj for j synaptic

A Logical Calculus of the Ideas Immanent in Nervous Activity 389

delays after firing, where bj = 0 for / large enough, say 7 = M or
greater. We may then state

Theorem V

Extinction is equivalent to absolute inhibition.

For, assuming relative inhibition to hold for the moment, we
need merely run M circuits U\, U'2, . . . 'hi containing respectively
1, 2, ... , A/ neurons, such that the firing of each link in any is
sufficient to fire the next, from the neuron c, back to it, where
the end of the circuit Wj has just b,- inhibitory synapses upon c,.
It is evident that this will produce the desired results. The reverse
substitution may be accomplished by the diagram of Figure Ig.
From the transitivity of replacement, we infer the theorem. To
this group of theorems also belongs the well-known

Theorem VI

Facilitation and temporal summation may be replaced by spatial sum-
mation.

This is obvious: one need merely introduce a suitable secjuence
of delaying chains, of increasing numbers of synapses, between the
exciting cell and the neuron whereon temporal summation is
desired to hold. The assumption of spatial summation will then
give the required results. See e.g. Figure Ih. This procedure had
application in showing that the observed temporal summation in
gross nets does not imply such a mechanism in the interaction of
individual neurons.

The phenomena of learning, which arc of a character persisting
over most physiological changes in nervous activity, seem to re-
quire the possibility of permanent alterations in the structure of
nets. The simplest such alteration is the formation of new synapses
or equivalent local depressions of threshold. We suppose that some
axonal terminations cannot at first excite the succeeding neuron;
but if at any time the neuron fires, and the axonal terminations
are simultaneously excited, they become synapses of the ordinary
kind, henceforth capable of exciting the neuron. The loss of an
inhibitory synapse gives an entirely equivalent result. We shall
then have

390 Information Storage and Neural Control

Theorem VII

Alterable synapses can be replaced by circles.

This is accomplished by the method of Figure li. It is also to
be remarked tliat a neuron which becomes and remains spon-
taneously active can likewise be replaced by a circle, which is set
into activity by a peripheral afferent when the activity commences,
and inhibited by one when it ceases.

THE THEORY: NETS WITH CIRCLES

The treatment of nets which do not satisfy our previous assump-
tion of freedom from circles is very much more difficult than that
case. This is largely a consequence of the possibility that activity
may be set up in a circuit and continue reverberating around it
foi an indefinite period of time, so that the realizable Pr may
involve reference to past events of an indefinite degree of remote-
ness. Consider such a net VX, say of order /;, and let Ci, c-2, . . . , r^ be
a cyclic set of neurons of ^^l. It is first of all clear from the definition
that every N^ of ^^\ can be expressed as a TPE, of M, A^2, . . . , Np
and the absolute afferents; the solution of v)l involves then only
the determination of expressions for the cyclic set. This clone, we
shall derive a set of expressions [A\:

Ndz,) . ^ . Pr.[S"" M(zi), *S"''-^ N,(z,), ... , S"''^ N,{z,)], (2)

where Pr , also involves peripheral afferents. Now if n is the least
common multiple of the n,„ we shall, by substituting their equiva-
lents according to (2) in (3) for the A^„ and repeating this
process often enough on the result, obtain S of the form

N,{z,) . ^ . Pr,[S"N,{zr), S"N,(zi), ... , S" Np(z,)]. (3)

These expressions may be written in the Hilbert disjunctive nor-
mal form as

N,{z,) . = . E S„ n '?" A^. (zi ) n ~ *^" ^i(2i), for suitable ^ (-i)

where S„ is a TPE of the absolute afferents of V^I. There exist
some 2" different sentences formed out of the pN, by conjoining
to the conjunction of some set of them the conjunction of the

A Logical Calculus of the Ideas Immanent in Nervous Activity 391

negations of the rest. Denumerating these by A'i(zi), ^'2(21), . ..,
X-2p{zi), we may, by use of the expressions (4), arrive at an equi-
pollent set of equations of the form

X,{z,) . ^ .ZPruiz,) . S^Xjiz,). (5)

Now we import the subscripted numerals i,j into the object-
language: i.e., define Pri and Pr2 such that Pri(zzi,Zi) . = . X,{zi)
and Prj(zzi,zz2,Zi) . = . Pr,j{zi) are provable whenever zzi and
ZZ2 denote i and / respectively.
Then we may rewrite (5) as

(zi)zzp : Pri(zi, Z3)
. = . {EZ'{)zZp . Pr-iizi. Zo, z-i - zzn) . Priizo, Zz - zZn) (6)

where zz^ denotes n and zZp denotes 2'\ By repeated substitution
we arrive at an expression

(zi)zZp : Pri(zi, zz„zzo) . = . {Ez-z)zZp {Ezz)zZp . . . {Ez„)zZp

Pr-zizi, z-2, ZZn {zzo — 1)) . Pr-i{z2,Zz,zZn {zz2 - 1)) (7)

Pr2(z„_i,z„,0) . Pri(Zn,0), for any numeral ZZ2 which denotes s.

This is easily shown by induction to be equipollent to

{zi)zzp : . Pri{Zi,zZnZZ2) : = : (Ef) (Z2) zzo — l/(zoZZ„)

^ ZZp . fiZZnZZ2) = Zi . Pr2{f(,ZZn (Z2 + 1)), (8)

f(zz,a-z)) . PrAf {0),0)
and since this is the case for all ZZ2, it is also true that

(Z4) {z,)zzp : Pn{z,,z,) . = . (Ef) (Z2) (Z4 - 1) ./(Z2)

^ ZZp . /(Z4) = Zi/(Z4) - zi . Pro[/(z2 + 1),/(Z2), Z2] . (9)

Pri[/(res (Z4. zZn)), res (Z4, zz,,)],

where zz„ denotes n, res {r,s) is the residue of /• mod s and zZp
denotes 2''. This may be written in a less exact way as

N^t) . ^ . (Ecf>) ix)t - 1 . <^(.r) ^ 2' . 0(0 = i .

P[0(.f+ l),0(.r) ..V,(o^ (0)],

where a and t are also assumed divisible by n, and Pr2 denotes P.
From the preceding remarks we shall have

392 Information Storage and Neural Control

Theorem VIII

The expression (9) for neurons of the cyclic set oj a net S'X together
with certain TPE expressing the actions oJ other neurons in terms oj
them, constitute a solution of V)I.

Consider now the question of the reahzabihty of a set of S,. A
first necessary condition, demonstrable by an easy induction, is that

(z.2)zi . pAz-2) ^ p,{z,) .^.Si^ sMj (10)

should be true, with similar statements for the other free p in Si'.
i.e., no nervous net can take account of future peripheral afferents.
Any S, satisfying this requirement can be replaced by an equi-
pollent S of the form

{Ef) (z,)zy {z,)zz,r.hPr,„,

:f{Zr,Z,,Zs = 1 . ^ ./>.3(Z2) (11)

where zZp denotes p, by defining

Pr„,i = /[(zi) {Z2)zi{zs)zzp : . f(zu z-i, Zs) = 0 . v . /(zi, Zo, Zs)
= 1 :/(zi, Zo, Z3) = 1 . = . /),3(z,) : -^ : S,].

Consider now these series of classes a,, for which

N ,{V) : = : {E<\>) {.v)t(^m)q : 4>ecxi :ISf„,{x) . = . <i>{t, x, m) = 1.

[/ = ry + !,••• ,M] (12)

holds for some net. These will be called prehensible classes. Let us
define the Boolean ring generated by a class of classes k as the
aggregate of the classes which can be formed from members of k
by repeated application of the logical operations; i.e., we put

-^ aeX : a, ^eX . — > . — a, a . (3, aW jSeX].

We shall also define

^(k) . = . (R(k) - t'p' - "'V',

f-i\e(K) =p X[(a, /3) : atK -^ ae\ . ^ . — a, a . (3, aV (3, S "aeX

and

G{'\>,t) = i[{m) . cf>{t -\- l,t, m) = '!^(m)].

A Logical Calculus of the Ideas Immanent in Nervous Activity 393

The class !-iv,,(/c) is formed from k in analogy with H\(>'), but by
repeated apphcation not only of the logical operations but also
of that which replaces a class of properties P e a by S{P) e S ^^ a.
We shall then have the

Lemma

Priipu Pi. • . . , p.,. Zi) is a TPE if and only if

(Zl) (pu ... , pra) {Ep„, + i) : />,„+! e ir^^eilpl, p2, • • • , P,n] )

A„+i(zi) = PuiPuPi, ... ,A»,Zl) (13)

is true; and it is a TPE not involving \S" if and only if this holds
when '<-R,.' is replaced by 'f-R', and we then obtain

Theorem IX

A series of classes ai, a-^, ... a, is a series of f)rehensible classes if and
only if

(Em) (En) (p)n(i) ('V) : . i.r)ni';^ix) = Ov •b{x = 1 :^ : (E^)
{Ey)m . 'M^) = 0 . fSeiillyiiEi) . y = a,)) . v . {x)m .
^(.r) = 0 . l3efk[yaE,) . y = «,)] : (0 (0) : ^ea. . (14)

'i4>, nt + p) . ^ . (Ef) . fef3 . {w)m{.v.)t - 1 .

(t){n{t + 1) + p, nx + p, iv) = f(nt + p, nx + p, iv).

The proof here follows directly from the lemma. The condition
is necessary, since every net for which an expression of the form
(4) can be written obviously verifies it, the t];'s being the charac-
teristic functions of the S„ and the (3 for each -^ being the class
whose designation has the form JJ ^r, J J PTj, where Pr,, denotes

I'-i J-4i,-,

a,, for all k. Conversely, we may write an expression of the form
(4) for a net VX fulfilling prehensible classes satisfying (14) by putting
for the Pra Pr denoting the ']j's and a Pr, written in the analogue
for classes of the disjunctive normal form, and denoting the a
corresponding to that '4^, conjoined to it. Since every S of the form
(4) is clearly realizable, we have the theorem.

It is of some interest to consider the extent to which we can
by knowledge of the present determine the whole past of various
special nets: i.e., when we may construct a net the firing of the
cyclic set of whose neurons requires the peripheral afferents to

394 Information Storage and Neural Control

have had a set of past values specified by given functions 0,. In
this case the classes a, of the last theorem reduced to unit classes;
and the condition may be transformed into

{Em, n) {v)n{i, <];) {Ej) : . {x)m : '\>{x) = 0 . w , '^(x) = I :
^i€j(({;, nt + p) : -^ : (:w)m(x)t — 1 . (f)i{n{t + 1)
+ p, nx -\- 'p,w) = 4>j{nt + p, nx + p, w) : .
(:u, v) (w)m . 4>iix>'{u + 1) -\- p, nn + p, w)
= (t)i{n(v + 1) -]r p,nv -{- p,w).

On account of limitations of space, we have presented the above
argument very sketchily; we propose to expand it and certain of
its implications in a further publication.

The condition of the last theorem is fairly simple in principle,
though not in detail; its application to practical cases would,
however, require the exploration of some 2-" classes of functions,
namely the members of fjv(|ai, ••• , «..j). Since each of these is
a possible ^ of Theorem IX, this result cannot be sharpened. But
we may obtain a sufficient condition for the realizability of an S
which is very easily applicable and probably covers most practical
purposes. This is given by

Theorem X

Let us define a set of X" of S by the following recursion:

1. Any TPE and any TPE whose arguments have been re-
placed by members of K belong to K;

2. If Pri{zi) is a member of K, then (22)21 • Pri{zo), (£22)2:1 .
Pviiz-i), and C,„rXzi) • * belong to it, where C,„„ denotes the property
of being congruent to m modulo n, m < n.

3. The set K has no further members.
Then every member of K is realizable.

For, if Pr\{zi) is realizable, nervous nets for which

A^,(2i) . = . Pry{z,) . SN,{zi)
iV,(zi) . ^ . Pn(z,)vSN,fzr)

are the expressions of equation (4), realize (22)21 • Priiz-z) and

A Logical Calculus of the Ideas Immanent in Nervous Activity 395

{E Zi)Zi . Priiz^j respectively; and a simple circuit, a, C2, ... , c,.,
of ?2 links, each sufficient to excite the next, gives an expression

A^„(zi) . = .M(0) . C.„

for the last form. By induction we derive the theorem.

One more thing is to be remarked in conclusion. It is easily
shown: first, that every net, if furnished with a tape, scanners
connected to afferents, and suitable efferents to perform the
necessary inotor-operations, can compute only such numbers as
can a Turing machine; second, that each of the latter numbers
can be computed by such a net; and that nets with circles can be
computed by such a net; and that nets with circles can compute,
without scanners and a tape, some of the numbers the machine
can, but no otliers, and not all of them. This is of interest as
affording a psychological justification of the Turing definition of
computability and its equivalents, Clhurch's X — definability and
Kleene's primitive recursiveness: If any number can be computed
by an organism, it is computable by these definitions, and con-
versely.

CONSEQUENCES

Causality, which requires description of states and a law of
necessary connection relating them, has appeared in several forms
in several sciences, but never, except in statistics, has it been as
irreciprocal as in this theory. Specification for any one time of
afferent stimulation and of the activity of all constituent neurons,
each an "all-or-none'' affair, determines the state. Specification
of the nervous net provides the law of necessary connection whereby
one can compute from the description of any state that of the
succeeding state, but the inclusion of disjunctive relations prevents
complete determination of the one before. Moreover, the regen-
erative activity of constituent circles renders reference indefinite
as to time past. Thus our knowledge of the world, including
ourselves, is incomplete as to space and indefinite as to time.
This ignorance, implicit in all our brains, is the counterpart of
the abstraction which renders our knowledge useful. The role of
brains in determining the epistemic relations of our theories to our

396

Information Storage and Neural Control

-<1 ^

<■
<

^f-

FlOURE 1

A Logical Calculus of the Ideas Immanent in Nervous Activity 397

observations and of these to the facts is all too clear, for it is ap-
parent that every idea and every sensation is realized by activity
within that net, and by no such activity are the actual afferents
fully determined.

There is no theory we may hold and no observation we can make
that will retain so much as its old defective reference to the facts
if the net be altered. Tinnitus, paraesthesias, hallucinations, de-
lusions, confusions and disorientations intervene. Thus empiry
confirms that if our nets are undefined, our facts are undefined,
and to the "real'' we can attribute not so much as one quality
or "form." With determination of the net, the unknowable object
of knowledge, the "thing in itself,'' ceases to be unknowable.

To psychology, however defined, specification of the net would
contribute all that could be achieved in that field — even if the
analysis were pushed to ultimate psychic units or "psychons," for
a psychon can be no less than the activity of a single neuron.
Since that activity is inherently propositional. all psychic events
have an intentional, or "semiotic," character. The "all-or-none"
law of these activities, and the conformity of their relations to
those of the logic of propositions, insure that the relations of

-^ EXPRESSION FOR THE FIGURES

In the figure the neuron cv is always marked with the numeral i upon the
body of the cell, and the corresponding action is denoted by W with i as sub-
script, as in the text.

Figure la N-i(t) . = . A^i(/ - 1)

Figure lb A^3(0 • = • A^i^' - 1) V N2(t - 1)

Figure Ic A^3(0 . = . A^i(/ - D • NM - 1)

Figure Id Nsit) . = . N,(t - 1) . ^ N-2(t - 1)

Figure le .V,(0 : = : .V,(/ - 1) . V . ,V2(/ - 3) . - A^,(/ - 2)

N,(t) . = . N-At - 2) . N-zit - 1)

Figure If N^{t) : = : ~ A^i(; - 1) . N ■i{t - \)vN^{t - 1) . V . yVi(t - 1) •

N-At - 1) . iVsO - 1)
is!, it) : = : - 7Vi(< - 2) . N At - 2) v N -M - 2) . v . NAt - 2) .

NAt - 2) . ^At - 2)
Figure Ig A^3(0 . = . NAt - 2) . ~ iV,(< - 3)
Figure Ih iVsCO . = . A^,(/ - 1) . NAt - 2)
Figure li .V3(0 : = : Ar,(/ - 1) . V . ,V,(^ - 1) . {Ex)t - 1 . A^,(.v) . N Ax)

398 Information Storage and Neural Control

psychons are those of the two-valued logic of propositions. Thus
in psychology, introspective, behavioristic or physiological, the
fundamental relations are those of two-valued logic.

Hence arise constructional solutions of holistic problems involving
the differentiated continuum of sense awareness and the norma-
tive, perfective and resolvent properties of perception and execu-
tion. From the irreciprocity of causality it follows that even if the
net be known, though we may predict future from present activities,
we can deduce neither afferent from central, nor central from
efferent, nor past from present activities — conclusions which are
reinforced by the contradictory testimony of eye-witnesses, by the
difficulty of diagnosing differentially the organically diseased, the
hysteric and the malingerer, and by comparing one's own mem-
ories or recollections with his contemporaneous records. Moreover,
systems which so respond to the difference between afferents to
a regenerative net and certain activity within that net, as to
reduce the difference, exhibit purposive behavior; and organisms
are known to possess many such systems, subserving homeostasis,
appetition and attention. Thus both the formal and the final
aspects of that activity which we are wont to call mental are
rigorously deducible from present neurophysiology. The psychi-
atrist may take comfort from the obvious conclusion concerning
causality — that, for prognosis, history is never necessary. He can
take little from the equally valid conclusion that his observables
are explicable only in terms of nervous activities which, until
recently, have been beyond his ken. The crux of this ignorance
is that inference from any sample of overt behavior to nervous
nets is not unique, whereas, of imaginable nets, only one in fact
exists, and may, at any moment, exhibit some unpredictable
activity. Certainly for the psychiatrist it is more to the point that
in such systems "Mind" no longer "goes more ghostly than a
ghost." Instead, diseased mentality can be understood without loss
of scope or rigor, in the scientific terms of neurophysiology. For
neurology, the theory sharpens the distinction between nets neces-
sary or merely sufficient for given activities, and so clarifies the
relations of disturbed structure to disturbed function. In its own
domain the difference between equivalent nets and nets equivalent
in the narrow sense indicates the appropriate use and importance

A Logical Calculus of the Ideas Immanent in Nervous Activity 399

of temporal studies of nervous activity: and to mathematical bio-
physics the theory contributes a tool for rigorous symbolic treat-
ment of known nets and an easy method of constructing hypo-
thetical nets of required properties.

REFERENCES

1. Carnap, R.: The Logical Sjntax of Language. New York, Harcourt.

Brace and Company, 1938.

2. Hilbert, D., und Ackermann, W.: Grundiige der Theoretischen Logik.

Berlin, J. Springer, 1927.

3. Whitehead, A. N., and Russell, B.: Principia Mathematica. Cambridge,

Cambridge University Press, 1925.

NAME INDEX

Abbott. W., 19, 170, 171, 353

Abraham, S., 348

Abt,J. P., 226

Ackerman, W., 133, 138

Ackermann, \V., 386, 399

Adey, VV. R., 240

Aldrich, A., 295

AUee, W. C, 170

Alper, T., 116

Apgar, J., 116

Aposhian, H. V., 135

Arbib, M. H., 295, 377

Arduini, A., 226

Arnon, D. I., 148, 170

Ashby, W. R.. 142. 169

Astrachan, L., 1 10, 111 , 1 12, 1 19, 124,

135
Attneave, F., 173, 184

Bach, L. M. N., 242

Bachtold,J. G., 136

Barlow, H. B.. 327

Barlow, J., 277

Barnett, L., 115

Basilic, C, 71, 113, 119

Bates, J. A. V., 241

Bateson, G.. 25, 173, 184, 185. 186, 242,

296, 330, 354, 355, 372
Baumol, ^V. J.. 171
Bavelas, A.. 173, 181, 182
Beadle, G. W.. 59
Beavers, W. R., 186
Beckwith, W.. 276
Beers, R. F.,Jr., 229
Bell, D. A., 16
Bellman, R., 167, 171
Belozersky, A. N., 87, 106, 114
Benzer, S., 71
Berg, P., 71. 98, 115
Bergold, G. H., 114
Bernard, C., 233

Bidwell, R. G. S., 170

Birdsall, T. G.. 306, 327

Bishop, G. H., 226

Blackman, R. B., 348

Block, L. N., in

Blum, M., 285, 289, 292, 293, 295

Blustein, H.. 21, 22, 184

Boltzman, L., 5, 144

Boyer. G. S.. 137

Branson, H. R., 141, 169

Brattgard, S.. 226

Brazier, M. A. B.. 19, 226, 230, 24L

242, 277. 355. 360
Brenner, S.. 71, 114, 115, 135
Brillouin. L.. 141. 142, 147, 169
Britten, R.J., 135
Broadbent, D. E., 307, 308, 327
Brown, R., 310, 327
Bubel, H. C.. 136
Buchsbaum, R., 368
Burch. N. R., 24, 329, 348, 349, 355
Burma, D. P., 114
Burns, B. D., 226
Burton, K., 101, 115
Bush, R. R., 49, 56

Caceros, C. A., 348

Carnap, R., 377, 383, 399

Chamberlin, M., 98, 115

Chargaff, E., 115

Cheng, P. v., 115

Cherry, C, 17

Childers, H. E., 329, 348

Chow, K. L., 197, 198, 200, 225, 226

Cohen, G. N., 138

Cohen, S. S., 115, 125. 134. 135

Copi, I. M.. 377

Cordes, S., 115

Corley, K., 269

Corning, VV. C, 276, 370

Courtois. G., 216, 228

401

402

Information Storage and Neural Control

Cowan, J., 294, 295
Craston, D. F., 170
Crawford, E. M., 115
Crawford, L. V., 115
Crick, F. H. C, 60, 71, 74, 77,
82, 83, 115, 119

), 81,

Daesch, G. E., 137

Darnell, J. E., Jr., 74, 122, 123, 136,

138, 139
Davenport, W. F., 241
Davern, C. I., 98, 115
Davies, D. R., 71
Davison, P. F., 115
De LaHaba, G. L., 138
Dean, W., 229
Deininger, R, L., 309, 327
Deutsch, J. A., 226
Dewson, J., 197, 198, 200, 226
Dickerson, R. E., 71
Dingman, W., 276, 368
Dixon, M. K., 137
Dobzhansky, T., 374
Doty, P., 87, 100, 115, 118
Driesch, H. A. E., 358
Dubbs, D. R., 117
Duda, W. L., 55
Dulbecco, R., 129, 136, 137
Duncan, C. P., 189, 196, 226
Dunlop, C. W., 240
Dunn, D. B., 115

Echols, H., 59, 71, 72, 73, 74, 75, 121,

353
Edstrom, J., 115
Edwards, R. J., 348
Eiduson, S., 276
Elgot, C. C, 377
Ellen, P., 276
Emerson, A. E., 170
Epstein, H. T., 115
Essman, W. B., 226
Estes, W. K., 49, 56

Feinstein, A., 17
Fields, W. S., 353
Finamore, F. J., 116
Finch, J. T., 136
Fitts, P. M., 309, 327
Flaks,J. G., 135
Fogh, J., 133, 138
Freeman, G., 137
Freese, E., 83, 116
Freifelder, D., 115
Fresco, J. R., 118
Frey, B. A., 136
Frisch-Niggemeyer, W., 116
Furth,J. J., Ill, 116

Gaarder, T., 170

Gabor, D., 17, 294

Gafford, L. G., 118

Garen, A., 71, 136

Gebhardt, L. P., 136

Geiduschek, E. P., 116

Geller, E., 276

Gerard, R. W., 26, 189, 196, 226, 227,
228, 305, 327, 353, 361, 367, 369,
370, 371, 372, 374, 375, 376

Gibbs, W., 144

Gilbert, W., 116, 135

Gillbricht, M., 170

Gillies, N. E., 116

Ginsberg, H. S., 137

Glickman, S. E., 276

Goldman, M., Ill, 116

Goldman, S., 17, 348

Goldring, S., 227

Goldstein, M. H., 241

Gorman, A. L. F., 243

Gran, H. H., 170

Gray, E. G., 285

Green, M., 137

Gregory, R. T., 27

Grey Walter, VV., 241

Gros, F., 112, 116, 135, 136

Grunberg-Manago, M., 67, 71

Gumnit, R. J., 227

Fatt, I., 289
Faulkner, P., 115

Haibt, L. H., 55

Hall, B. D., Ill, 112, 116, 118, 119,
124, 135

Name Index

403

Hall, V. E., 361

Halstead, VV. C, 211, 227

Hamberger, C. A., 227

Hammer, G., 227

Hart, R. G., 71

Hartley, J. W., 138

Hartley, R. V. L., 6

Hartline, H. K., 360

Hayashi, M., 112, 116

Hebb, D. O., 37, 54, 55, 190, 227

Hede, R., 115

Helinski, R., 71

Henderson, K., 134

Hendrix, C. E., 240

Herriot, R., 121

Hershey, A. 13., 134

Hiatt, H., 116, 135

Hilbert, D., 386, 399

Hoagland, M. B., 116

Holland, J. H., 55

Holland, J. J., 136

Holley, R. W., 103, 116

Hollingworth, B. R., 135

Hooper, L., 138

Home, R. W., 132, 136

Horowitz, N. H., 59, 70

Horvath, W.J., 314, 328

Howes, D. VV., 137

Human, M. L., 134

Hurwitz, J., Ill, 116

Hutchinson, G. E., 169

Hyden, H., 211, 226, 227, 276

Hyman, L. H., 369

lizuka, R., 211, 227
Ingram, V. M., 71
Isaacs, A., 138
Ivers, R. R., 18
Iwamura, T., 116

Jacob, F., 69, 71, 114, 135, 136

Jarvik, M. E., 226

Jasper, H. H., 228, 277

Jaynes, E. T., 144, 170

John, E. R.,20, 120,211, 227,243,247,
249, 250, 260, 261, 262, 263, 267, 276,
278, 282, 355, 364, 366, 369, 370, 372

Joklik, W. K., 136
Jones, O. W., 71
Josse, J., 135

Kaiser, A. D., 136

Katz,J. J., 211, 227, 289

Kellaway, P., 23, 24

Kcndrew, J. C., 71

Khinchin, A. I., 17

Killam, K. F., 245, 246, 247, 248, 249,

250, 259, 260, 261, 262, 263, 276
Kiinura, K., 119
Kirby, K. S., 117
Kit, S., 76, 117, 120, 122, 139, 353,362,

372, 375
Kleene, S. C., 377, 395
Kleinschmidt, W. J., 117
Klug, A., 136
Kok, I. P., 117
Kornberg, A., 125, 135
Kornberg, S. R., 135
Korolkova, T. A., 276
Kozloff, L. M., 134
Kraft, M. S., 228
Kreps, E., 211, 228, 276
Krey,J., 170
Kristiansen, K., 216, 228
Kroger, H., 114
Kurland, C. G., 116, 135
Kurtz, H., 138

Lawley, P. D., 83, 117
Lciman, A. L., 243, 267, 276
Lengyel, P., 71, 113, 117, 119
Lenneberg, E., 310, 327
Leuchtenbergcr, C., 137
Levine, M., 136
Levinthal, C., 70, 115
Levintow, L., 136, 138
Levy, H. B., 138
Liberson, W. T., 264, 268, 276
Libet, B., 227
Lichtenstein, J., 135
Lindegren, C. C., 118
Lindegren, G., 118
Lindeman, R. L., 169
Lindsay, R. K., 34, 353

404

Information Storage and Neural Control

Linschitz, H., 141, 169

Lipmann, F., 1 38

Littlefield, J. \V., 120

Livanov, M. N.. 245, 246, 264, 268,

276, 277
Lockart, R. Z., 136
Loeb, T., 117

Lorente de No, R., 332, 348
Loucks, R. B., 272, 277
Luria, S. E., 134, 136
Lute, M., 134
Lwoff, A., 137
Lwoff, M., 137

MacArthur, R., 143, 169

MacFadyen, A., 169

Magasanik, B., 117

Majkowski, J.. 252, 253, 277

Makhinko, V. I.. 117

Maling, B., 71

Mandelbrot, B., 43, 44, 45, 46, 56

Marcus, P. I., 137

Margalef, D. R., 169

Marmur, J., 87, 115, 118

Martin, E. M., 115

Martin, R. G., 71

Matthaei, J. H., 64, 71, 112, 117, 134,

139
Mayor, H.D., 17, 18,74, 121, 122, 171,

172
McConnell, J., 369, 370, 371
McConnell, W., 170
McCulloch, W. S., 36, 38, 55, 283, 296,

297, 298, 306, 375, 376, 377, 379
McGlothlen, M., 72, 121
McLaren, L. C, 136
McQuillen, K., 135
Meier, R. L., 324, 325, 328
Merrill, S. H., 116

Meselson, M.,71,96, 114, 117, 118, 135
Miller, G. A., 52, 53, 56, 355, 357
Miller, J. G., 301
Minagawa, T. , 117
Minckler, S., 118
Minsky, M., 292
Monod, J., 69, 71
Moore, H. F., 118
Morganstern, O., 170

Morrell, F., 73, 189, 228, 244, 245, 246,
248, 277, 278, 282, 355, 364, 365, 366,
367, 371, 374

Moses, L., 225

Mostellar, F., 49, 56

Mountcastle, V. B., 237, 241

Mowbray, G. H., 328

Muller,J.. 231, 233, 234

Munier, R., 138

Myers, J., 116

Nagington, J., 132, 136

Naitoh, P., 225

Nakamoto, T., 1 11, 116, 120

Nathan, P. W., 224, 229

Nathans, D., 138

Neff, W. D., 268, 277

Newton, A., 137

Nieder, P. C., 277

Nirenberg, M. W., 64, 67. 71, 74, 75.

112, 117, 134, 139
Nomura, M., 110, 111, 118
Nyquist, H., 6

Obrist, VV. D., 228

Ochoa, S., 67,71, 74, 75, 112, 113, 114,

117, 119
Odum, H. T., 170
Oesterreich, R. E., 277
Ogur, M., 118
OXeary, J. L., 226, 227
Olken, H., 374
Onesto, N., 295
Opalskii, A. F., 117
Osawa, S., 118

Park, O., 170

Park, T., 170

Patrick, B. S., 25

Patten, B. C., 140, 169, 171, 172, 356

Pautlcr, E., 297

PhiUips, D. C.,71

Pigon, A., 227

Pitts, W. H., 36, 55, 284, 377, 379

Piatt, J. R., 305, 327

Polyakov, K. L., 245, 277

Name Index

405

Prange, E., 292
Pribram, K. H., 228, 229
Puck, T. T., 137

Quastler, H., 308, 309, 327

Rabinovvitch, E. I., 170
Rabson, A., 138
Randall, C. G., 118
Ransmeier, R. E., 228
Rapoport, A., 46. 56, 314, 328
Rashevsky, N., 167, 171, 379
Reymond, D. B., 231
Rhoades, M. V., 328
Rich, A., 110, 118
Rikli, A. E., 348
Ris, H., 71

Risebrough, R. VV., 116, 135
Roberts, L., 228
Roberts, R. B., 135
Rochester, N.. 37, 38, 55
Rogers, S., 138
Roitbak, A. E, 197, 229
Roizman, B., 138
Rolfe, R., 96, 118
Root, W. L., 241
Rose, VV. R., 138
Rosenblatt, P., 54, 56
Ross, G., 228
Ross, R. W., 137
Rothman, F., 71
Rothschild, 367
Rothstcin, J., 170
Rowland, V., 192, 229
Rubin, H., 129, 137, 138
Rusinov, V. S.. 192. 229
Russell, B., 383, 399
Russell, W. R.. 224, 229
Ryther.J. H., 170

Sachs, E., 267, 276, 368

Sachs, L., 137

Saltzbcrg, B., 5, 17, 18, 19, 20, 21, 22,

23, 24, 25, 26, 296, 330, 348
Salzman, N. P., 118, 128, 136, 137, 138
Sandler. B., 228

Schafer, \V., 1 18, 131, 138

Schaffer, F. E., 118, 136

Schildkraut, C. E., 87, 96, 101, 102,

110, 118
Schlessinger, D., 135
Schmidt, K. P., 170
Schrodinger, E.. 140, 141, 142, 147, 169
Schuster, H., 74, 118
Schwartz, M., 17
Schwerdt, C. E., 118, 136
Sebring, E. D., 136
Sclfridge, O., 292
Sevring, E. D., 138
Shannon, C. E., 5, 13, 14, 15, 17, 52,

142, 144, 168, 169, 230, 240, 241, 318
Shapiro, A., 18, 19, 23, 73
Sherrington, C. S.. 51, 231, 241, 360
Shipton, H. W., 21. 241. 349
Shore, V. C., 71
Sibatani, A.. 119
Siebert, W. M., 241
Siminovitch, L.. 135, 136
Simon, E. H., 119
Simon, H. A., 41. 42, 46, 56
Sines, J. O., 277
Sinsheimer, R. E.. 119, 135
Smith, J. D., 115
Smith, K. M., 137
Snedecor, G. \V., 170
Sokolov, E. N., 233, 241
Spahr, P. F., 115, 135
Speyer,J. F., 71. 113, 117, 119
Spiegelman, S., 1 1 1. 1 12, 1 16, 1 18, 1 19,

124, 135
Spirin, A. S., 87, 106, 114, 119
Spoor, VV. A., 348
Sporn, M. B., 276, 368
Stahl, F. VV., 117
Stamm, J. S., 229
Steinberg, C. A., 346. 348
Steiner, R. F., 229
Stephenson, M. E., 103, 119
Stern, J. A., 248, 277
Stevens, A., 119
Stevens, S. S., 306, 307, 327
Stoker, M. G. P., 137
Storck, R., 112, 119
Strandberg, B. E., 71
Strauss, B., 117

406

Information Storage and Neural Control

Streisinger, G., 135
Stuart, D. C.,Jr., 133, 138
Stumpers, F. L. H. M,, 17
Sueoka, N., 99, 115, 119
Suwa, N., 227
Sved, S., 115
Sverdrup, H. U., 171
Swets,J. A., 306, 327
Szent-Gyorgi, A., 147, 148, 170
Szilard, L., 5

Takahashi, T., 119
Takahata, N., 227
Takeda, T., 227
Taketomo, Y., 185
Tamm, I., 137
Tanabe, M., 227
Tanner, W. P., Jr., 306, 327
Tatum, E. L., 59
Temin, H. M., 129, 137, 138
Tessman, I., 97, 119
Thomas, R. S., 119
Thompson, R., 229
Thoren, M., 138
Thrall, R. M., 169
Tissieres, A., 135
Tobias, J. M., 120, 229, 371
Travers, P. L., 180, 184
Tribus, M., 144, 170
Tschirgi, R. D., 211, 368
Tukey.J. W., 348
Turing, A. M., 36, 55, 377

Volkin, E., 110, 111, 112. 116, 119, 124,

135
von Foerster, H., 27"^,
von Neumann, J., 5, 36, 55, 170, 284,

285, 375

Wagner, B., 117

Warner, R. C, 114

Watson, J. D., 60, 71, 77, 80, 81, 116,

119, 120, 135, 139
Watts-Tobin, R. J., 115
Weaver, W., 17
Weill, J. D., 114
Weiner, M. F., 19, 186
Weir, H. F., 297, 298
Weiss, M., 243, 252, 254, 255, 256, 277
Weiss, S. B., Ill, 116, 120
Welch, A. J., 348
Wenzel, B. M., 211, 368
Wheelock, E. F., 137
Whitehead, A. N., 360, 383, 399
Whitfield, I. C., 234, 241
Wiener, N., 6, 169, 171, 230, 241
Winocour, E., 137
Winograd, S., 294, 295
Wittman, H. G., 71
Woese, C. R., 120
Woodward, P. M., 9, 17
Work, T. S., 115
Wright, J. B., 377
Wyatt, G. R., 135

Ulett, G. A., 277

Valentine, R. C., 115
Valentinuzzi, M. E., 24, 374
Vallentyne, J. R., 170
van Leeuwuen, W. S., 349
Vendrely, R., 119
Verbeek, L., 294, 295
Verveen, B., 289, 295
Vinograd, J., 117
Vladimirov, G. E., 229
Vogt, M., 129, 137

Yamana, K., 119
Yanofsky, C., 71
Yarmolinsky, M. B., 138
Yngve, v., 46, 47, 48, 49, 52, 56

Zamecnik, P. C., 103, 119
Zimmerman, J. B., 135
Zimmerman, T., 138
Zinder, N. D., 117, 136
Zipf, G. K., 39, 40, 43, 44, 56
Zubkoff, P. L., 116
Zuckermann, E., 266, 277

SUBJECT INDEX

Afferents

interaction of, 285-289, 296
peripheral, 383, 385, 390-393

After discharge, 199, 218, 382

All-or-none law, 381, 397

Amino acids, 62-67, 72, 103

Amnesia, retrograde, 189, 224, 366

Assimilated rhythms, 247, 248

Attention, 48, 233, 361

Auditory mechanisms, 266, 285, 307

Automata theory, 284, 377

Axons, 190, 284, 289, 293-347, 371, 379,

Bacteria, 59, 102, 106, 139

DNA from, 102

metabolism of, 97

mutant strains, 63
Bacteriophages, 94, 110, 123, 124, 127

T-even, 79, 85

T-4 mutants, 82

nitrous acid induced mutants, 97
Base pairing, 62, 65

specific, 79
Behavior

differential conditioned, 274

disturbed, 179

ordered, 306, 355

purposive, 398
Binary representation in computers, 27-

30
Binary units, 6, 9, 10
Biomass, 144, 149, 151, 155, 164
Brain

information storage in, 56

information transfer ir, 240

number of neurons in, 361

Calculation

error-free, 292, 293
Calculus, logical, 55, 284, 377
Cannibalism experiments, 369
Channel

Capacity, 12, 15, 142, 240, 295, 308,
311, 325

communication, 7, 52

correction, 15, 142

length of, 308

noise, 240

overloaded, 31 1
Chlorophyll, 150, 155, 157
Coding

genetic, 61, 76, 82, 112, 353

in nerve cells, 246

in nervous system, 233

in time domain, 330

of language, 43-48

spatio-temporal patterns of, 279-282
Coincidence analysis, 243, 278, 340
Communication

accuracy in, 26

channels, 12, 54, 142, 240

economics of, 25, 372

pathological alteration, 180

systems, 12, 15, 25, 242, 373

theory, 12, 17, 142, 230, 308
Communities

adaptability of, 166

bioenergetics of, 140, 147

complexity of, 1 43

diversity of, 162

energy balance in, 163

stability of, 143

trophodynamics, 140, 147
Computation, error-free, 290
Computers

averaging by, 233

coding in, 31, 36, 37

407

408

Information Storage and Neural Control

general purpose, 345

generation of, 375

simulation of brain, 38, 51
Conditioning

avoidance response, 249

differential to central stimulation, 268
Correction of errors, 14, 15

Decision making, 34, 49, 167, 243, 304,

310
Decoding, 10, 295, 303, 309
Deoxyribonucleic acids (DNA)

amount per cell, 84, 85

as genetic material, 60

average composition, 85-93

base composition, 93

base sequence of, 62, 66, 70, 72, 80, 88

distribution of, 98

equilibrium sedimentation of, 95

formation of hybrid molecules, 101

genetic information in, 60, 66, 222

heterogeneity of composition, 96, 99,
112^

molecular size of, 85, 96, 97, 101, 122

non-overlapping bands, 97

phage 0X1, 74, 98, 111

primer, 65, 109, 111

replication, 80, 363

structure, 60, 77, 80, 81, 101, 109,
121

synthesis, 76, 80, 109, 121, 125, 375
Dependency, 177
Deterministic models, 230, 236
Discrimination, 178, 243, 259, 264
Dominance, 177

Error

correction of, 7, 13

frequency of, 14

in performance, 259-264

of commission, 261

of omission, 260

probability of, 290. 293, 295
Exchange, interpersonal, 175
Expectation, 37, 185, 354
Experience, fixation of, 354, 359, 363,

374
Extinction, 153, 381, 388

Filter, 22, 278, 297, 340, 346, 348
Filtering, 311, 314, 319, 324
Fixation, 189, 355, 365-367
Frequency analysis, 23, 26, 329, 340

Galvanic skin response (GSR), 338, 348
Generalization, 248, 258, 272, 278
Genes, 59-63, 69, 72, 121, 126,289,353,
359, 374
as determinants of protein struc-
ture, 61
mutation of, 72, 126
suppressor, 72
Genetic coding, 61, 64, 70, 114

Habituation, 192, 233, 264
Homeostasis, 142, 232, 398

E
Electrocardiogram (EKG), 330, 343-

348
Electroencephalogram (EEG), 21, 23,

190, 207, 252, 278, 330-340, 348
Energy balance, 163, 165
Energy gains and losses, 140, 149, 155,

166, 171, 356
Entropy, 5, 15, 25, 141, 147, 149, 240
Environmental influences, 355, 356
Equivocation, 13, 14, 15, 19, 336

Information
capacity, 9
content, 9

coding, in brain, 268
flow, route of, 305
genetic, 59, 70, 76, 103, 108, 122, 123.

189
input overload, 311
measure, 5, 6, 7, 240
overload, 311, 314, 325
overload testing, 316
transmission of, 25, 124, 304, 310, 362

Subject Index

409

Information processing

essential subsystems, 302

in computers, 31, 32, 33

in human brain, 240, 301

in time domain, 329-348

models of, 35, 38, 41 , 54, 230, 236, 239

subsystems research, 306
Information storage, 8-10

fixation of experience, 363, 366

in nerve cells, 189

long-term, 244, 369

mechanisins, 192

short-term, 195-197, 210, 211
Information theory, 5, 240, 295

in ecology, 140-149

in neurophysiology, 230-239
Inhibition, 235, 268, 284, 341, 360, 381,

388
Inputs, 53, 235, 287, 293, 302-308, 311,

314, 325, 365
Interaction

group, 341, 343

of afferents, 285, 296

virus and cell, 129
Interference, 18, 22, 244

Language, 38, 303, 305, 362, 383

coding, 43-48

information in, 20

redundancy of, 26

statistical properties of, 42
Learning, 173, 181, 283, 303, 310, 357,
366, 381, 389

levels of, 174, 177, 183, 185, 190, 355,
359

process of, 177, 181 , 232, 354, 359, 371

theory of, 56, 173, 301, 310
Logic

of propositions, 379, 381, 397

probabilistic, 55, 284, 293

Machine, computing, see Computers
Machine, Turing, 36, 284, 377, 395
Malleability, of processing system, 355,
360, 362

Memory, 37, 52, 225, 243, 255, 280,
304, 310, 359, 363, 374

cellular, 201, 212

enduring, 189, 365

functional, 201

recent, 189

retention of, 189
Message, 12, 15, 19, 25, 301-305

{see also Binary units)
Messenger, see RNA
Metacommunications, 186, 354
Metalanguage, 186

Models, information processing, see In-
formation processing
Mutations, 61, 74, 80

chemically induced, 81

externally adaptive, 372

genetic, 63, 363

suppressor, 72

Natural selection, 167, 373
Nets, see Neuron nets
Neuron nets

anastomotic, 283-295

logically stable, 287

with circles, 390

without circles, 382
Neurons

input. 290, 294

internuncial, 380

logical functions of, 286

output, 284, 292, 297

spontaneously active, 236, 390

storage, 235

threshold of, 289, 314, 381
Noise, 13, 18, 21, 22, 171, 283, 289, 296,
305, 330, 375

errors induced by, 26, 142

fluctuation in, 309

high frequency, 340

random, 12, 18, 22
Nucleotides

composition, 94, 95

sequence, 83, 101

triplets, 82, 97, 114
Numbers, binary, 27, 29, 30

410

Information Storage and Neural Control

Omission, 260, 311, 317, 319, 324, 325
Order

functional, 355

structural, 355
Outputs, 38, 142, 233, 288, 292-297,

302-308, 311, 318, 326, 356
Overload, see Information overload

Perception, 283, 284, 304
Performance

erroneous, 269, 275

principle factors limiting, 309

under overload conditions, 320, 323
Period analysis, 189, 329, 340-348, 359
Photosynthesis, 154-156, 164, 165
Planaria, 368

cannabalism studies, 370
■ Poliovirus

biosynthesis of, 132

multiplication of, 133

properties of, 127
Prediction, 174, 181
ProbabiHstic models, 230, 233
Probability, 10, 11, 19, 20, 25, 41-43,

143, 145, 326
Problem solving, 35
Protein

specificity, 59, 68

structure, 62, 70, 72

synthesis, 59, 64-68, 70, 108, 124, 371

viral, 129-132
Purines, 60, 61, 76, 79
Pyrimidine, 60, 61, 76, 79, 103

Queuing, 311, 317, 321, 324

Random processes, 232

Receiver, 6, 25, 303

Redundancy, 7, 14, 25, 26, 84, 122, 173,

181, 283
Reinforcement, 175-177, 259, 269,272
Replication

phage, 130

virus, 123, 132

Response, 175-177, 197

behaviorally appropriate, 257
conditioned, 190, 211, 245, 248, 253,

263, 317, 371
differentiated, 271
generalization of, 248, 258
graded, 235
labeled potentials, 245

Reverberation, 37, 280, 282

Ribonucleic acid (RNA)

and information storage, 120
and memory, 244, 280
base sequence, 66, 73, 222
cellular concentration after stimula-
tion, 211, 221
general characteristics, 103
informational, see messenger
messenger, 64-67, 74, 76, 80, 98, 108-

114, 124, 363
ribosomal, 64, 105, 124, 363
synthesis. 111, 121, 125, 368
total cellular, 105
transfer, 64-66, 72, 103, 112, 131
virus, 74, 103, 107, 114, 128, 132, 139

Ribosomes, 64, 139

Shannon's Theorem 10, 142, 144, 148
Signals, 11, 13, 23, 233, 283, 289, 290,
305, 310

electroencephalographic, 330

electrocardiographic, 343, 347

meaningful, 237

random, 18

reconstituted, 335

synchronous, 284
Signals-in-noise, 230, 232
Specificity

genetic, 62

protein, 59, 62, 68
Stimuli, 176, 238

concurrent peripheral and central,
264

conditioned, 211, 248, 252, 258, 264-
266, 271, 275

flicker, 264

peripheral, 252, 266, 269

photic, 265, 269, 271, 275

tracer, 245, 271

Subject Index

411

Storage {see also Information storage)

capacity, 8-11, 375

mechanisms, 210, 370

long-term, 244, 308, 369

short-term, 195, 197
Symbols, 14, 52, 53, 362
Synapses, 37, 234, 313, 365, 379, 382,
388, 390

alterable, 390

delay across, 380, 382, 387

excitatory, 381, 385, 388

inhibitory, 382, 385, 388

irreciprocal, 360

reciprocal, 360
System

auditory, 192, 306

biological, 17, 18, 57, 305

communication, 8, 184

Darwinian, 372, 373

dissolution of, 325

equivocation of, 16

genotypic, 372, 373

homogeneous, 357

Lamarckian, 372, 374

non-linear, 234

protein synthesizing, 65

output of, 38

permanently altered, 357

random, 11, 25

receiving, 20

static, 8

visual, 306

Theorems I to X

McCulloch and Pitts, 382-394
Theory

behavior, 38, 53

mentalistic, 39

stochastic, 41, 43
Threshold [|

changes in, 382 "8

differential, 265

neuron, 272, 289, 369

occlusion, 265, 270, 273

variation in, 388
Time domain, 329-348
Trophodynamics, 140, 141
Turing machine, see Machine, Turing

Uncertainty, 5, 17, 143, 145, 314

Virus action

formation of precursor molecules, 1 30

fowl plaque virus, 131

on cell synthesis, 126

poliovirus biosynthesis, 132
Virus replication, 123, 133

Watson-Crick model for DNA, 60, 77

Internet Archive Audio

Featured

Top

Images

Featured

Top

Software

Featured

Top

Books

Featured

Top

Video

Featured

Top

Mobile Apps

Browser Extensions

Archive-It Subscription

Save Page Now

Full text of "Information storage and neural control; tenth annual scientific meeting of the Houston Neurological Society jointly sponsored by the Dept. of Neurology, Baylor University College of Medicine, Texas Medical Center, Houston, Texas"

See other formats