Long-term memory stabilized by noise-induced rehearsal

TL;DR

This paper demonstrates that unstructured neural noise, through spike timing dependent plasticity, can stabilize long-term memories in cortical networks by reinforcing synaptic patterns without explicit rehearsal.

Contribution

It introduces a model showing how noise-induced rehearsals combined with specific STDP rules can stabilize memories in unstable synapses, explaining irregular cortical activity.

Findings

Noise carries memory imprints via temporal correlations

STDP with noise can reinforce all stored patterns

Neural noise helps maintain stable synaptic connectivity

Abstract

Cortical networks can maintain memories for decades despite the short lifetime of synaptic strength. Can a neural network store long-lasting memories in unstable synapses? Here, we study the effects of random noise on the stability of memory stored in synapses of an attractor neural network. The model includes ongoing spike timing dependent plasticity (STDP). We show that certain classes of STDP rules can lead to the stabilization of memory patterns stored in the network. The stabilization results from rehearsals induced by noise. We show that unstructured neural noise, after passing through the recurrent network weights, carries the imprint of all memory patterns in temporal correlations. Under certain conditions, STDP combined with these correlations, can lead to reinforcement of all existing patterns, even those that are never explicitly visited. Thus, unstructured neural noise can stabilize the existing structure of synaptic connectivity. Our findings may provide the functional reason for highly irregular spiking displayed by cortical neurons and provide justification for models of system memory consolidation. Therefore, we propose that irregular neural activity is the feature that helps cortical networks maintain stable connections.