Shared and specific muscle synergies in natural motor behaviors

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.

Fig. 1.

Significance of synchronous synergy extraction. The fraction of the total variation of each dataset explained by the combination of five synergies (black bars) is compared with the fraction of the total variation of simulated structureless datasets explained by five synergies extracted from the simulated data with the same procedure used for the real data (gray bars, mean ± SD over 100 runs).

Fig. 2.

Behavior-independent and behavior-specific synchronous synergies. The three shared synergies are extracted from the entire dataset of muscle patterns recorded during jumping, swimming, and walking in three frogs. One synergy (jump-walk) is extracted from only jumping and walking episodes. The other behavior-specific synergies (jump, swim, and walk) are extracted from only the muscle patterns of individual behaviors. Each synergy is normalized to the maximum over all muscles.

Fig. 3.

Significance of time-varying synergy extraction. A much larger fraction of the total variation of the data is explained by the combination of five synergies (black bars) than by five synergies extracted from simulated structureless data (gray bars, mean ± SD over 100 runs).

Fig. 4.

Behavior-independent and behavior-specific time-varying synergies. Each synergy represents the activation of the 13 muscles with specific activation waveforms (20 samples for a total duration of 500 ms; amplitude is color coded) and it is normalized to the maximum over all samples of all muscles. Three shared synergies are extracted from the entire dataset, whereas the other behavior-specific synergies are extracted from only the muscle patterns from two behaviors (jump–swim and jump–walk) or a single behavior (swim and walk).