Stochastic perturbationmethods for spike-timing-dependent plasticity

Todd K. Leen, Robert Friel

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Online machine learning rules and many biological spike-timingdependent plasticity (STDP) learning rules generate jump process Markovchains for the synaptic weights. We give aperturbation expansion for the dynamics that, unlike the usual approximation by a Fokker-Planck equation (FPE), iswell justified. Our approach extends the related system size expansion by giving an expansion for the probability density as well as its moments. We apply the approach to two observed STDP learning rules and show that in regimes where the FPE breaks down, the new perturbation expansion agrees well with Monte Carlo simulations. The methods are also applicable to the dynamics of stochastic neural activity. Like previous ensemble analyses of STDP, we focus on equilibrium solutions, although the methods can in principle be applied to transients as well.

Original languageEnglish (US)
Pages (from-to)1109-1146
Number of pages38
JournalNeural Computation
Volume24
Issue number5
DOIs
StatePublished - 2012

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Learning
Weights and Measures
Plasticity
Machine Learning
Equations
Max Planck
Ensemble
Jump
Monte Carlo Simulation
Approximation

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Arts and Humanities (miscellaneous)

Cite this

Stochastic perturbationmethods for spike-timing-dependent plasticity. / Leen, Todd K.; Friel, Robert.

In: Neural Computation, Vol. 24, No. 5, 2012, p. 1109-1146.

Research output: Contribution to journalArticle

Leen, Todd K. ; Friel, Robert. / Stochastic perturbationmethods for spike-timing-dependent plasticity. In: Neural Computation. 2012 ; Vol. 24, No. 5. pp. 1109-1146.
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