Retrieval properties of attractor neural networks incorporating biological features - A self-consistent signal-to-noise analysis

Abstract

A network of integrate-and-fire excitatory neurons is investigated using the recently proposed self-consistent signal-to-noise analysis, which provides a new method for analyzing the behaviour of networks of neurons that have asymmetric synaptic matrices. The neural dynamics is described in terms of two continuous variables, namely the firing rate and the afferent current of each (excitatory) neuron. The afferent current is described by a differential equation that includes a decay term, the weighted inputs from other excitatory neurons, and a term that models the inhibitory interneurons. The effective inhibition chosen here depends upon both the level of activity of the excitatory neurons a..