System size resonance in an attractor neural network

TL;DR

This paper investigates how an attractor neural network's response to periodic stimuli is maximized at a specific finite system size, revealing a non-trivial system size resonance phenomenon.

Contribution

It introduces the concept of system size resonance in attractor neural networks, showing how signal amplification peaks at a particular finite size.

Findings

Signal amplification peaks at a specific finite system size

Response depends non-trivially on system size

Resonance phenomenon observed in neural network dynamics

Abstract

We study the response of an attractor neural network, in the ferromagnetic phase, to an external, time-dependent stimulus, which drives the system periodically two different attractors. We demonstrate a non-trivial dependance of the system via a system size resonance, by showing a signal amplification maximum at a certain finite size.