Learning and stabilization of winner-take-all dynamics through interacting excitatory and inhibitory plasticity
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Open access
Date
2014-07Type
- Journal Article
ETH Bibliography
yes
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Abstract
Winner-Take-All (WTA) networks are recurrently connected populations of excitatory and inhibitory neurons that represent promising candidate microcircuits for implementing cortical computation. WTAs can perform powerful computations, ranging from signal-restoration to state-dependent processing. However, such networks require fine-tuned connectivity parameters to keep the network dynamics within stable operating regimes. In this article, we show how such stability can emerge autonomously through an interaction of biologically plausible plasticity mechanisms that operate simultaneously on all excitatory and inhibitory synapses of the network. A weight-dependent plasticity rule is derived from the triplet spike-timing dependent plasticity model, and its stabilization properties in the mean-field case are analyzed using contraction theory. Our main result provides simple constraints on the plasticity rule parameters, rather than on the weights themselves, which guarantee stable WTA behavior. The plastic network we present is able to adapt to changing input conditions, and to dynamically adjust its gain, therefore exhibiting self-stabilization mechanisms that are crucial for maintaining stable operation in large networks of interconnected subunits. We show how distributed neural assemblies can adjust their parameters for stable WTA function autonomously while respecting anatomical constraints on neural wiring. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000087080Publication status
publishedExternal links
Journal / series
Frontiers in Computational NeuroscienceVolume
Pages / Article No.
Publisher
Frontiers MediaSubject
winner-take-all; competition; plasticity; self-organization; contraction theory; canonical microcircuits; inhibitory plasticityOrganisational unit
03453 - Douglas, Rodney J.
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ETH Bibliography
yes
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