On theory of motor synergies

Abstract

Recently Latash, Scholz, and Schöner (2007) proposed a new view of motor synergies which stresses the idea that the nervous system does not seek a unique solution to eliminate redundant degrees of freedom but rather uses redundant sets of elemental variables that each correct for errors in the other to achieve a performance goal. This is an attractive concept because the resulting flexibility in the synergy also provides for performance stability. But although Latash et al. construe this concept as the consequence of a "neural organization" they do not say what that may be, nor how it comes about. Adaptive model theory (AMT) is a computational theory developed in our laboratory to account for observed sensory-motor behavior. It gives a detailed account, in terms of biologically feasible neural adaptive filters, of the formation of motor synergies and control of synergistic movements. This account is amplified here to show specifically how the processes within the AMT computational framework lead directly to the flexibility/stability ratios of Latash et al. (2007). Accordingly, we show that quantitative analyses of experimental data, based on the uncontrolled manifold method, do not and indeed cannot refute the possibility that the nervous system tries to find a unique (optimal) solution to eliminate redundant degrees of freedom. We show that the desirable interplay between flexibility and stability demonstrated by uncontrolled manifold analysis can be equally well achieved by a system that forms and deploys optimized motor synergies, as in AMT.