Learning with a network of competing synapses

Details Learning with a network of competing synapses Learning with a network of competing synapses

2011-08-24

arxiv.org/abs/1108.4796v2

PLoS ONE 6(9) (2011): e25048

Physics

Condensed Matter

Disordered Systems and Neural Networks

16 pages, 9 figures; published in PLoS ONE

Scientific paper

10.1371/journal.pone.0025048

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.

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