Cerebellar control of endpoint position-a simulation model

The ability of a neural network model of the cerebellum to control a nonlinear dynamical model of the neuromuscular system is explored. The cerebellum is represented by adjustable pattern generator (APG) modules capable of commanding movements from arbitrary starting positions to specific endpoints. The network is trained to match endpoints to visual targets using a biologically motivated learning rule. Neural signals recorded from a monkey subject helped to guide realistic simulations. The simulation results illustrate how commanded velocity automatically increases when the initial position of the limb is farther from the target position. The mechanism of this `feedforward´ compensation can be traced to a smaller value of limb position input during the preselection period. The decreased excitation serves to increase the number of Purkinje cells that get switched to an off state before the movement begins, and the resultant decrease in loop inhibition leads to a larger commanded velocity. The simulations also demonstrate how limited feedback through the cerebellum can be used, without the threat of instability, to regulate the achievement of a targeted endpoint