Storage capacity of attractor neural networks with depressing synapses

Abstract

We compute the capacity of a binary neural network with dynamic depressing synapses to store and retrieve an infinite number of patterns. We use a biologically motivated model of synaptic depression and a standard mean-field approach. We find that at $T=0\mathrm{}$ the critical storage capacity decreases with the degree of the depression. We confirm the validity of our main mean-field results with numerical simulations.