Enhancing associative memory recall in non-equilibrium materials through activity

TL;DR

This paper investigates how non-equilibrium activity, modeled as Gaussian-colored noise, enhances the memory capacity of Hopfield networks by altering the energy landscape and increasing entropy production.

Contribution

It introduces non-equilibrium activity into the Hopfield model, demonstrating increased memory capacity and altered energy landscape compared to equilibrium systems.

Findings

Non-equilibrium conditions increase storage capacity.

Entropy production modifies the energy landscape.

Memory regions become more accessible under activity.

Abstract

Associative memory, a form of content-addressable memory, facilitates information storage and retrieval in many biological and physical systems. In statistical mechanics models, associative memory at equilibrium is represented through attractor basins in the free energy landscape. Here, we use the Hopfield model, a paradigmatic model to describe associate memory, to investigate the effect of non-equilibrium activity on memory retention and recall. We introduce activity into the system as gaussian-colored noise which breaks detailed balance and forces the system out of equilibrium. We observe that, under these non-equilibrium conditions, the Hopfield network has a higher storage capacity than that allowed at equilibrium. Using analytical and numerical techniques, we show that the rate of entropy production modifies the energy landscape and helps the system to access memory regions which were previously inaccessible.