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We present and study a probabilistic neural automaton in which the fractionof simultaneously-updated neurons is a parameter, rho (0, 1) . For small rho,there is relaxation towards one of the attractors and a great sensibility toexternal stimuli and, for rho >= rho_c, itinerancy among attractors. Tuning rhoin this regime, oscillations may abruptly change from regular to chaotic andvice versa, which allows one to control the efficiency of the searchingprocess. We argue on the similarity of the model behavior with recentobservations and on the possible role of chaos in neurobiology.

Instabilities in Attractor Networks with Fast Synaptic Fluctuations and Partial Updating of the Neurons Activity