Computational model of the motor program generator for pursuit.

Abstract

A parallel processing neural network model of a motor program generator (MPG) for pursuit eye movements (PEM) was developed. The MPG model consists of two neural networks (velocity maps), which represent velocity values theta R and theta L respectively, as eccentric locations on the map with zero in the center. Neurons are arranged in a circular layer and connected only to their immediate neighbors. The potential field P of all neurons is analogous to a flat circular membrane whose center can be pushed up or down. During PEM one of the two maps, which are connected in a push-pull fashion, always features an activity peak (AP) which travels with constant velocity vT from one neuron to the next. The shape of P defines whether AP travels in a circle (theta = constant), towards the periphery (theta increase) or towards the center (theta decrease). Such a model provides a novel approach for understanding neural generators of non-periodical motor programs.