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Computational Motor Control: Redundancy and Invariance

¹Institut National de la Santé et de la Recherche Médicale (INSERM) U742, Action Neuroimagerie Modelisation, Université Pierre et Marie Curie, Paris; ²INSERM U534, Space and Action; and ³INSERM U371, Brain and Vision Research, Bron, France

Submitted 16 March 2006; accepted in final form 24 September 2006

The nervous system controls the behavior of complex kinematically redundant biomechanical systems. How it computes appropriate commands to generate movements is unknown. Here we propose a model based on the assumption that the nervous system: 1) processes static (e.g., gravitational) and dynamic (e.g., inertial) forces separately; 2) calculates appropriate dynamic controls to master the dynamic forces and progress toward the goal according to principles of optimal feedback control; 3) uses the size of the dynamic commands (effort) as an optimality criterion; and 4) can specify movement duration from a given level of effort. The model was used to control kinematic chains with 2, 4, and 7 degrees of freedom [planar shoulder/elbow, three-dimensional (3D) shoulder/elbow, 3D shoulder/elbow/wrist] actuated by pairs of antagonist muscles. The muscles were modeled as second-order nonlinear filters and received the dynamics commands as inputs. Simulations showed that the model can quantitatively reproduce characteristic features of pointing and grasping movements in 3D space, i.e., trajectory, velocity profile, and final posture. Furthermore, it accounted for amplitude/duration scaling and kinematic invariance for distance and load. These results suggest that motor control could be explained in terms of a limited set of computational principles.

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