Instabilities in associative memory model with synaptic depression and switching phenomena among attractors

TL;DR

This paper explores how synaptic depression impacts the stability of states in an associative memory model, revealing unstable regions and switching phenomena among attractors through a dynamical mean-field approach.

Contribution

It introduces a sublattice method and derives macroscopic equations to analyze stability and switching in the model with synaptic depression.

Findings

Identification of unstable regions where memory and mixed states are unstable

Discovery of switching phenomena among attractors in the unstable region

Phase diagram illustrating effects of depression strength and pattern correlation

Abstract

We investigated how the stability of macroscopic states in the associative memory model is affected by synaptic depression. To this model, we applied the dynamical mean-field theory, which has recently been developed in stochastic neural network models with synaptic depression. By introducing a sublattice method, we derived macroscopic equations for firing state variables and depression variables. By using the macroscopic equations, we obtained the phase diagram when the strength of synaptic depression and the correlation level among stored patterns were changed. We found that there is an unstable region in which both the memory state and mixed state cannot be stable and that various switching phenomena can occur in this region.