PATTERN-RECOGNITION, CHAOS, AND MULTIPLICITY IN NEURAL NETWORKS OF EXCITABLE SYSTEMS

We study a neural network composed of excitable FitzHugh neurons that interact by diffusive type connections. Patterns of neural activity ma y be stored by a Hebbian rule. The stored patterns are recalled and gi ven by the transient activity of the neurons after the network has bee n perturbed by related patterns and relaxes back to its steady state. Periodic perturbations of the network are repeated requests for comput ations and result in simple periodic, complex periodic, and chaotic re sponses and corresponding computational performances.