Dynamical neural network organization of the visual pursuit system

The central nervous system is a parallel dynamical system that connects sensory input with motor output for the performance of visual tracking. Elementary control system tools are applied to extend dynamical neural-network models to the visual smooth pursuit system. Observed eye position responses to target motions and characteristics of the plant (eye muscles and orbital mechanics) place dynamical constraints on the interposed neural-network controller. In the process of constructing a model for the controller, it is shown that two previous pursuit-system models, using efference copy and feedforward compensation, are equivalent from an input-output standpoint. A controller model possessing a potentially highly parallel implementation is introduced, and an example with supporting neural firing rate data is presented. Changes in time delays or other system dynamics are expected to lead to compensatory adaptive changes in the controller. A scheme to noninvasively simulate such changes in system dynamics was developed.<>