STDP allows fast rate-modulated coding with Poisson-like spike trains.

Spike timing-dependent plasticity (STDP) has been shown to enable single neurons to detect repeatedly presented spatiotemporal spike patterns. This holds even when such patterns are embedded in equally dense random spiking activity, that is, in the absence of external reference times such as a stimu...

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Autores principales:	Matthieu Gilson, Timothée Masquelier, Etienne Hugues
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/ffaa2389ab1147ca97a43d9c22c49820
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id	oai:doaj.org-article:ffaa2389ab1147ca97a43d9c22c49820
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spelling	oai:doaj.org-article:ffaa2389ab1147ca97a43d9c22c498202021-11-18T05:51:50ZSTDP allows fast rate-modulated coding with Poisson-like spike trains.1553-734X1553-735810.1371/journal.pcbi.1002231https://doaj.org/article/ffaa2389ab1147ca97a43d9c22c498202011-10-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22046113/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Spike timing-dependent plasticity (STDP) has been shown to enable single neurons to detect repeatedly presented spatiotemporal spike patterns. This holds even when such patterns are embedded in equally dense random spiking activity, that is, in the absence of external reference times such as a stimulus onset. Here we demonstrate, both analytically and numerically, that STDP can also learn repeating rate-modulated patterns, which have received more experimental evidence, for example, through post-stimulus time histograms (PSTHs). Each input spike train is generated from a rate function using a stochastic sampling mechanism, chosen to be an inhomogeneous Poisson process here. Learning is feasible provided significant covarying rate modulations occur within the typical timescale of STDP (~10-20 ms) for sufficiently many inputs (~100 among 1000 in our simulations), a condition that is met by many experimental PSTHs. Repeated pattern presentations induce spike-time correlations that are captured by STDP. Despite imprecise input spike times and even variable spike counts, a single trained neuron robustly detects the pattern just a few milliseconds after its presentation. Therefore, temporal imprecision and Poisson-like firing variability are not an obstacle to fast temporal coding. STDP provides an appealing mechanism to learn such rate patterns, which, beyond sensory processing, may also be involved in many cognitive tasks.Matthieu GilsonTimothée MasquelierEtienne HuguesPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 7, Iss 10, p e1002231 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Matthieu Gilson Timothée Masquelier Etienne Hugues STDP allows fast rate-modulated coding with Poisson-like spike trains.
description	Spike timing-dependent plasticity (STDP) has been shown to enable single neurons to detect repeatedly presented spatiotemporal spike patterns. This holds even when such patterns are embedded in equally dense random spiking activity, that is, in the absence of external reference times such as a stimulus onset. Here we demonstrate, both analytically and numerically, that STDP can also learn repeating rate-modulated patterns, which have received more experimental evidence, for example, through post-stimulus time histograms (PSTHs). Each input spike train is generated from a rate function using a stochastic sampling mechanism, chosen to be an inhomogeneous Poisson process here. Learning is feasible provided significant covarying rate modulations occur within the typical timescale of STDP (~10-20 ms) for sufficiently many inputs (~100 among 1000 in our simulations), a condition that is met by many experimental PSTHs. Repeated pattern presentations induce spike-time correlations that are captured by STDP. Despite imprecise input spike times and even variable spike counts, a single trained neuron robustly detects the pattern just a few milliseconds after its presentation. Therefore, temporal imprecision and Poisson-like firing variability are not an obstacle to fast temporal coding. STDP provides an appealing mechanism to learn such rate patterns, which, beyond sensory processing, may also be involved in many cognitive tasks.
format	article
author	Matthieu Gilson Timothée Masquelier Etienne Hugues
author_facet	Matthieu Gilson Timothée Masquelier Etienne Hugues
author_sort	Matthieu Gilson
title	STDP allows fast rate-modulated coding with Poisson-like spike trains.
title_short	STDP allows fast rate-modulated coding with Poisson-like spike trains.
title_full	STDP allows fast rate-modulated coding with Poisson-like spike trains.
title_fullStr	STDP allows fast rate-modulated coding with Poisson-like spike trains.
title_full_unstemmed	STDP allows fast rate-modulated coding with Poisson-like spike trains.
title_sort	stdp allows fast rate-modulated coding with poisson-like spike trains.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/ffaa2389ab1147ca97a43d9c22c49820
work_keys_str_mv	AT matthieugilson stdpallowsfastratemodulatedcodingwithpoissonlikespiketrains AT timotheemasquelier stdpallowsfastratemodulatedcodingwithpoissonlikespiketrains AT etiennehugues stdpallowsfastratemodulatedcodingwithpoissonlikespiketrains
_version_	1718424767751520256

STDP allows fast rate-modulated coding with Poisson-like spike trains.

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