On the interplay of noise and redundancy in the motor system

Emanuel Todorov
University of California at San Diego

The noise inherent in the CNS and the sensorimotor periphery imposes limits on behavioral performance - limits that can only be reached by a controller optimized for the task at hand. I will show that optimal feedback controllers for redundant biomechanical plants exhibit low-rank structure, corresponding to a set of sensorimotor synergies that monitor and control a few task-relevant features of the state. This "minimal intervention" strategy suppresses noise selectively, and predicts larger movement variability in redundant dimensions - in agreement with numerous psychophysical observations. I also will describe a recurrent neural network model of motor cortex, trained to perform sensorimotor transformations given noisy inputs. The network learns to suppress noise selectively - along the task-relevant dimensions of the neural population code. This is reminiscent of a minimal intervention strategy, but uses internal neural feedback rather than sensory feedback.

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