Instabilities in attractor networks with fast synaptic fluctuations and partial updating of the neurons activity

We present and study a probabilistic neural automaton in which the fraction of simultaneously-updated neurons is a parameter, ρ∈(0,1). For small ρ, there is relaxation towards one of the attractors and a great sensibility to external stimuli and, for ρ≥ρc, itinerancy among attractors. Tuning ρ in this regime, oscillations may abruptly change from regular to chaotic and vice versa, which allows one to control the efficiency of the searching process. We argue on the similarity of the model behavior with recent observations, and on the possible role of chaos in neurobiology.