Shared and specific muscle synergies in natural motor behaviors

TLDR

Choosing muscle patterns to achieve a goal is highly complex due to the high dimensionality and nonlinear dynamics linking muscle activity to movement. The study aims to determine whether the central nervous system employs a modular architecture for motor coordination by characterizing motor output across many movements. EMG from 13 hind‑limb muscles of freely moving frogs during jumping, swimming, and walking was recorded and analyzed with multidimensional factorization to extract invariant amplitude and timing relationships among muscle activations. Decomposing muscle activations into spatial and temporal synergies uncovered both shared and behavior‑specific patterns, revealing a spatial organization of muscle activity and supporting a modular motor controller.

Abstract

Selecting the appropriate muscle pattern to achieve a given goal is an extremely complex task because of the dimensionality of the search space and because of the nonlinear and dynamical nature of the transformation between muscle activity and movement. To investigate whether the central nervous system uses a modular architecture to achieve motor coordination we characterized the motor output over a large set of movements. We recorded electromyographic activity from 13 muscles of the hind limb of intact and freely moving frogs during jumping, swimming, and walking in naturalistic conditions. We used multidimensional factorization techniques to extract invariant amplitude and timing relationships among the muscle activations. A decomposition of the instantaneous muscle activations as combinations of nonnegative vectors, synchronous muscle synergies, revealed a spatial organization in the muscle patterns. A decomposition of the same activations as a combination of temporal sequences of nonnegative vectors, time-varying muscle synergies, further uncovered specific characteristics in the muscle activation waveforms. A mixture of synergies shared across behaviors and synergies for specific behaviors captured the invariances across the entire dataset. These results support the hypothesis that the motor controller has a modular organization.