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Learning with a Network of Competing Synapses

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  • Ajaz Ahmad Bhat
  • Gaurang Mahajan
  • Anita Mehta

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.

Suggested Citation

  • Ajaz Ahmad Bhat & Gaurang Mahajan & Anita Mehta, 2011. "Learning with a Network of Competing Synapses," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-9, September.
    • Handle: RePEc:plo:pone00:0025048
    DOI: 10.1371/journal.pone.0025048
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    References listed on IDEAS

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    1. Colin Camerer, 2003. "Behavioural studies of strategic thinking," Levine's Bibliography 506439000000000490, UCLA Department of Economics.
    2. Maurice A Smith & Ali Ghazizadeh & Reza Shadmehr, 2006. "Interacting Adaptive Processes with Different Timescales Underlie Short-Term Motor Learning," PLOS Biology, Public Library of Science, vol. 4(6), pages 1-1, May.
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