Chaotic hopping between attractors in neural networks

摘要

We present a neurobiologically--inspired stochastic cellular automaton whosestate jumps with time between the attractors corresponding to a series ofstored patterns. The jumping varies from regular to chaotic as the modelparameters are modified. The resulting irregular behavior, which mimics thestate of attention in which a systems shows a great adaptability to changingstimulus, is a consequence in the model of short--time presynaptic noise whichinduces synaptic depression. We discuss results from both a mean--fieldanalysis and Monte Carlo simulations.