Stable memory with unstable synapses

TL;DR

This paper proposes a new model of long-term memory that allows synaptic strengths to fluctuate while still maintaining stable memory representations at the network level, challenging traditional views of static connectivity.

Contribution

It introduces a theoretical framework linking eigenvalues to memory stability, highlighting the resilience of imaginary-coded memories and their neural attractor dynamics.

Findings

Imaginary-coded memories are more noise-resistant.

Memory representations act as time-varying attractors.

The model predicts measurable signatures in neural data.

Abstract

What is the physiological basis of long-term memory? The prevailing view in neuroscience attributes changes in synaptic efficacy to memory acquisition. This view implies that stable memories correspond to stable connectivity patterns. However, an increasing body of experimental evidence points to significant, activity-independent dynamics in synaptic strengths. Motivated by these observations, we explore the possibility of memory storage within a global component of network connectivity, while individual connections fluctuate. We find a simple and general principle, stemming from stability arguments, that links eigenvalues in the complex plane to memories. Specifically, imaginary-coded memories are more resilient to noise and homeostatic plasticity than their real-coded counterparts. Memory representations are stored as time-varying attractors in neural state-space and support associative retrieval of learned information. Our results suggest a link between the properties of learning rules and those of network-level memory representations, and point at measurable signatures to be sought in experimental data.