Learning in asymptotically behaving neural networks

The authors present results of attempts to introduce learning for a class of neural networks that has been proven to be asymptotically stable and that can be used to model several existing structures in the central nervous system (e.g., cerebellum). Specifically, the authors discuss the structure of this class of asymptotically behaving neural networks, introduce a Hebbian learning rule that can be used to modify both the inhibitory and excitatory synapses, and use this rule to train a simple network from this class in an XOR problem. The simulator and its user interface that are under development for the study of such problems are also presented