Chaotic hopping between attractors in neural networks

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We present a neurobiologically--inspired stochastic cellular automaton whose state jumps with time between the attractors corresponding to a series of stored patterns. The jumping varies from regular to chaotic as the model parameters are modified. The resulting irregular behavior, which mimics the state of attention in which a systems shows a great adaptability to changing stimulus, is a consequence in the model of short--time presynaptic noise which induces synaptic depression. We discuss results from both a mean--field analysis and Monte Carlo simulations.
12 pages, 5 figures

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Neurons and Cognition

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http://salesiana.dossiersoluciones.com/handle/123456789/111876

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/q-bio/0604020
 http://arxiv.org/abs/q-bio/0604020

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arXiv