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 may be stored by a Hebbian rule. The stored patterns are recalled and given by the transient activity of the neurons after the network has been perturbed by related patterns and relaxes back to its steady state. Periodic perturbations of the network are repeated requests for computations and result in simple periodic, complex periodic, and chaotic responses and corresponding computational performances.