Learning with a Network of Competing Synapses

Overview

Journal PLoS One

Specialties General Medicine
Science

Date 2011 Oct 8

PMID 21980377

Citations 2

Authors

Ajaz Ahmad Bhat

Gaurang Mahajan

Anita Mehta

Affiliations

Soon will be listed here.

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.

Citing Articles

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