Seminar given by Lena Ting, Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, and Fall 2008 Visiting Miller Professor at UC Berkeley. Seminar was given September 24, 2008 for the Redwood Center for Theoretical Neuroscience at UC Berkeley.
Abstract:
How do humans and animals move so elegantly through unpredictable and dynamic environments? And why does this question continue to pose such a challenge? We have a wealth of data on the action of neurons, muscles, and limbs during a wide variety of motor behaviors, yet these data are difficult to interpret, as there is no one-to-one correspondence between a desired movement goal, limb motions, or muscle activity. Using combined experimental and computational approaches, we are teasing apart the neural and biomechanical influences on muscle coordination of during standing balance control in cats and humans. Our work demonstrates that variability in motor patterns both within and across subjects during balance control in humans and animals can be characterized by a low-dimensional set of parameters related to abstract, task-level variables. Temporal patterns of muscle activation across the body can be characterized by a 4-parameter, delayed-feedback model on center-of-mass kinematic variables. Changes in muscle activity that occur following large-fiber sensory-loss in cats, as well as during motor adaptation in humans, appear to be constrained within the low-dimensional parameter space defined by the feedback model. Moreover, well-adapted responses to perturbations are similar to those predicted by an optimal tradeoff between mechanical stability and energetic expenditure. Spatial patterns of muscle activation can also be characterized by a small set of muscle synergies (identified using non-negative matrix factorization) that are like motor building blocks, defining characteristic patterns of activation across multiple muscles. We hypothesize that each muscle synergy performs a task-level function, thereby providing a mechanism by which task-level motor intentions are translated into detailed, low-level muscle activation patterns. We demonstrate that a small set of muscle synergies can account for trial-by-trial variability in motor patterns across a wide range of balance conditions. Further, muscle activity and forces during balance control in novel postural configurations are best predicted my minimizing the activity of a few muscle synergies rather than the activity of individual muscles. Muscle synergies may represent a sparse motor code, organizing muscles to solve an “inverse binding problem” for motor outputs. We propose that such an organization facilitates fast motor adaptation while concurrently imposing constraints on the structure and energetic efficiency of motor patterns used during motor learning.
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