Recognition ability in a biologically motivated neural network

Abstract

In this work we study the storage capacity of an extreme and asymmetrically diluted version of the Hopfield model for associative memory when the effect of the refractory periods of the neurons is included through a time dependent threshold. We develop a new dynamical approach that allows us to take into account, in an approximative way, the dependence of the system on its whole history. We also compare our analytical results with those obtained through a very extensive numerical simulation.