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A non-Markovian random walk underlies a stochastic model of spike-timing-dependent plasticity

A non-Markovian random walk underlies a stochastic model of spike-timing-dependent plasticity
A non-Markovian random walk underlies a stochastic model of spike-timing-dependent plasticity
A stochastic model of spike-timing-dependent plasticity (STDP) proposes that spike timing influences the probability but not the amplitude of synaptic strength change at single synapses. The classic, biphasic STDP profile emerges as a spatial average over many synapses presented with a single spike pair or as a temporal average over a single synapse presented with many spike pairs. We have previously shown that the model accounts for a variety of experimental data, including spike triplet results, and has a number of desirable theoretical properties, including being entirely self-stabilizing in all regions of parameter space. Our earlier analyses of the model have employed cumbersome spike-to-spike averaging arguments to derive results. Here, we show that the model can be reformulated as a non-Markovian random walk in synaptic strength, the step sizes being fixed as postulated. This change of perspective greatly simplifies earlier calculations by integrating out the proposed switch mechanism by which changes in strength are driven and instead concentrating on the changes in strength themselves. Moreover, this change of viewpoint is generative, facilitating further calculations that would be intractable, if not impossible, with earlier approaches. We prepare the machinery here for these later calculations but also briefly indicate how this machinery may be used by considering two particular applications.
1180-1230
Elliott, Terry
b4262f0d-c295-4ea4-b5d8-3931470952f9
Elliott, Terry
b4262f0d-c295-4ea4-b5d8-3931470952f9

Elliott, Terry (2010) A non-Markovian random walk underlies a stochastic model of spike-timing-dependent plasticity. Neural Computation, 22 (5), 1180-1230. (doi:10.1162/neco.2009.06-09-1038). (PMID:20028229)

Record type: Article

Abstract

A stochastic model of spike-timing-dependent plasticity (STDP) proposes that spike timing influences the probability but not the amplitude of synaptic strength change at single synapses. The classic, biphasic STDP profile emerges as a spatial average over many synapses presented with a single spike pair or as a temporal average over a single synapse presented with many spike pairs. We have previously shown that the model accounts for a variety of experimental data, including spike triplet results, and has a number of desirable theoretical properties, including being entirely self-stabilizing in all regions of parameter space. Our earlier analyses of the model have employed cumbersome spike-to-spike averaging arguments to derive results. Here, we show that the model can be reformulated as a non-Markovian random walk in synaptic strength, the step sizes being fixed as postulated. This change of perspective greatly simplifies earlier calculations by integrating out the proposed switch mechanism by which changes in strength are driven and instead concentrating on the changes in strength themselves. Moreover, this change of viewpoint is generative, facilitating further calculations that would be intractable, if not impossible, with earlier approaches. We prepare the machinery here for these later calculations but also briefly indicate how this machinery may be used by considering two particular applications.

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e-pub ahead of print date: 7 April 2010
Published date: May 2010
Organisations: Web & Internet Science

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Local EPrints ID: 270877
URI: http://eprints.soton.ac.uk/id/eprint/270877
PURE UUID: ed5958e5-7b0d-445a-9539-a1e57048fd51

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Date deposited: 21 Apr 2010 09:39
Last modified: 16 Jul 2019 22:20

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Author: Terry Elliott

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