Effects of refractory periods in the dynamics of a diluted neural network

FRANCISCO TAMARIT; DANIEL STARIOLO; SERGIO CANNAS; PABLO SERRA

PHYSICAL REVIEW E

AMER PHYSICAL SOC

Lugar: New York; Año: 1996 vol. 53 p. 5146 - 5152

1539-3755

We propose a stochastic dynamics for a neural network which accounts for the effects of the refractory periods (absolute and relative) in the dynamics of a single neuron. The dynamics can be solved analytically in an extremely diluted network. We found a very rich scenario that presents retrieval phases and a period doubling route to chaos in the attractors of the overlap order parameter. Our model incorporates some characteristics that make it biologically appealing, such as asymmetric synaptic efficacies, dilution of the synaptic matrix, absolute and relative refractory periods, complex retrieval dynamics, and low levels of activity in the retrieval regime.