Learning and Evolution of Control Systems

The oculomotor control system, like many other systems that are required to respond appropriately under varying conditions to a range of different cues, would be rather difficult to program by hand. A natural solution to modelling such systems, and formulating artificial control systems more generally, is to allow them to learn for themselves how they can perform most effectively. We present results from an extensive series of explicit simulations of neural network models of the development of human oculomotor control, and conclude that lifetime learning alone is not enough. Control systems that learn also benefit from constraints due to evolutionary type factors.