Oscillations and low firing rates in associative memory neural networks

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Abstract
A neural network with excitatory neurons for associative storage and inhibitory neurons for control of firing rates is proposed. In distinction to attractor neural networks which are endowed with fixed-point dynamics, the basic recall mode of the network consists of a relaxation to a limit cycle originating from an inhibitory feedback loop. Nonlocal synaptic connections between excitatory neurons store all the information and yield robust associative abilities of the network. Inhibitory neutrons with short-range connections and nonlinear interaction (shunting) are introduced to stabilize low levels of neural activity. The mean firing rate per neuron ranges between 0.1 and 0.5 impulses per Monte Carlo step (MCS). The average activity of excitatory and inhibitory neurons oscillates with frequency of 0.5/MCS. The model generalizes the attractor concept for associative memory and brings logical neural networks closer to biological reality. DOI: https://doi.org/10.1103/PhysRevA.40.4145 ©1989 American Physical Society