A neural network computational map approach to reflexive motor control

Using the neural basis of human motor control as a guide, it was possible to develop a control strategy based on the localized structure of reflex arcs, antagonistic actuation, and the encoding of movement by neuronal populations. Starting with a dynamic joint model consisting of an agonistic-antagonist pair of actuators with musclelike properties, it is shown that transitions from one posture to another can be accomplished by adjusting the steady-state open-loop stiffness of the opposing muscle pair and modulating the reflex gains as functions of the system state to shape the transient response. A computational map neural network paradigm is used to calculate time-varying reflex gains that move the system towards the direction of minimum error. Simulation results show that desired phase-plane trajectories can be tracked fairly accurately using a reasonable number of repetitions to learn the motion