Learning with a network of competing synapses

TL;DR

This paper introduces a game theory-inspired model of synaptic competition with different timescales, providing insights into learning, memory, and optimal performance in neural networks.

Contribution

It presents a novel model of synaptic interactions driven by competition, linking synaptic dynamics to network-level learning and memory, supported by analytical and numerical analysis.

Findings

System behavior depends on synaptic parameters and signal strength.

Multiple timescales play a functional role in network dynamics.

Model aligns with empirical motor adaptation data.

Abstract

Competition between synapses arises in some forms of correlation-based plasticity. Here we propose a game theory-inspired model of synaptic interactions whose dynamics is driven by competition between synapses in their weak and strong states, which are characterized by different timescales. The learning of inputs and memory are meaningfully definable in an effective description of networked synaptic populations. We study, numerically and analytically, the dynamic responses of the effective system to various signal types, particularly with reference to an existing empirical motor adaptation model. The dependence of the system-level behavior on the synaptic parameters, and the signal strength, is brought out in a clear manner, thus illuminating issues such as those of optimal performance, and the functional role of multiple timescales.