title: Chaotic hopping between attractors in neural networks.
abstract: 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 system 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.
authors.affiliation: Institute Carlos I for Theoretical and Computational Physics, and Departamento de Electromagnetismo y Física de la Materia, University of Granada, E-18071-Granada, Spain.
authors.forename: Joaquín
authors.initials: J
authors.lastname: Marro
authors.forename: Joaquín J
authors.initials: JJ
authors.lastname: Torres
authors.forename: Jesús M
authors.initials: JM
authors.lastname: Cortés
mesh_terms: D000818:Animals; D001921:Brain / Q000502:physiology*; D008959:Models, Neurological; D009010:Monte Carlo Method; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D013569:Synapses / Q000502:physiology*; D009435:Synaptic Transmission / Q000502:physiology; D013997:Time Factors