Robust short-term memory without synaptic learning

TL;DR

This paper proposes a novel neural network mechanism that enables short-term memory retention without synaptic learning, relying on metastable states in clustered networks of simple neurons, aligning with biological phenomena.

Contribution

It introduces a robust, synaptic-learning-free model for short-term memory using metastable states in clustered neural networks, applicable across various topologies and neuron models.

Findings

Network can retain information for a few seconds.

Mechanism is robust to different network topologies.

Reproduces neurobiological phenomena like synchronization and avalanche statistics.

Abstract

Short-term memory in the brain cannot in general be explained the way long-term memory can -- as a gradual modification of synaptic weights -- since it takes place too quickly. Theories based on some form of cellular bistability, however, do not seem able to account for the fact that noisy neurons can collectively store information in a robust manner. We show how a sufficiently clustered network of simple model neurons can be instantly induced into metastable states capable of retaining information for a short time (a few seconds). The mechanism is robust to different network topologies and kinds of neural model. This could constitute a viable means available to the brain for sensory and/or short-term memory with no need of synaptic learning. Relevant phenomena described by neurobiology and psychology, such as local synchronization of synaptic inputs and power-law statistics of forgetting avalanches, emerge naturally from this mechanism, and we suggest possible experiments to test its viability in more biological settings.