Adaptively timed conditioned responses and the cerebellum: a neural network approach

Conditioned responses often reflect knowledge about the timing of a US. This knowledge is manifested in the dependence of response topography on the CS-US interval employed in training. A neural network model and set of learning rules capable of simulating temporally adaptive features of conditioned responses is reviewed, and simulations are presented. In addition, we present a neural network implementation of the model which is designed to reconcile empirical studies of long-term synaptic depression in the cerebellum with neurobiological evidence from studies of the classically conditioned nictitating membrane response of the rabbit.