Spectral analysis of input spike trains by spike-timing-dependent plasticity.

Spike-timing-dependent plasticity (STDP) has been observed in many brain areas such as sensory cortices, where it is hypothesized to structure synaptic connections between neurons. Previous studies have demonstrated how STDP can capture spiking information at short timescales using specific input co...

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Autores principales:	Matthieu Gilson, Tomoki Fukai, Anthony N Burkitt
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/6d6e9933721e4a40a5f894e4257079a9
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spelling	oai:doaj.org-article:6d6e9933721e4a40a5f894e4257079a92021-11-18T05:51:11ZSpectral analysis of input spike trains by spike-timing-dependent plasticity.1553-734X1553-735810.1371/journal.pcbi.1002584https://doaj.org/article/6d6e9933721e4a40a5f894e4257079a92012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22792056/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Spike-timing-dependent plasticity (STDP) has been observed in many brain areas such as sensory cortices, where it is hypothesized to structure synaptic connections between neurons. Previous studies have demonstrated how STDP can capture spiking information at short timescales using specific input configurations, such as coincident spiking, spike patterns and oscillatory spike trains. However, the corresponding computation in the case of arbitrary input signals is still unclear. This paper provides an overarching picture of the algorithm inherent to STDP, tying together many previous results for commonly used models of pairwise STDP. For a single neuron with plastic excitatory synapses, we show how STDP performs a spectral analysis on the temporal cross-correlograms between its afferent spike trains. The postsynaptic responses and STDP learning window determine kernel functions that specify how the neuron "sees" the input correlations. We thus denote this unsupervised learning scheme as 'kernel spectral component analysis' (kSCA). In particular, the whole input correlation structure must be considered since all plastic synapses compete with each other. We find that kSCA is enhanced when weight-dependent STDP induces gradual synaptic competition. For a spiking neuron with a "linear" response and pairwise STDP alone, we find that kSCA resembles principal component analysis (PCA). However, plain STDP does not isolate correlation sources in general, e.g., when they are mixed among the input spike trains. In other words, it does not perform independent component analysis (ICA). Tuning the neuron to a single correlation source can be achieved when STDP is paired with a homeostatic mechanism that reinforces the competition between synaptic inputs. Our results suggest that neuronal networks equipped with STDP can process signals encoded in the transient spiking activity at the timescales of tens of milliseconds for usual STDP.Matthieu GilsonTomoki FukaiAnthony N BurkittPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 8, Iss 7, p e1002584 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Matthieu Gilson Tomoki Fukai Anthony N Burkitt Spectral analysis of input spike trains by spike-timing-dependent plasticity.
description	Spike-timing-dependent plasticity (STDP) has been observed in many brain areas such as sensory cortices, where it is hypothesized to structure synaptic connections between neurons. Previous studies have demonstrated how STDP can capture spiking information at short timescales using specific input configurations, such as coincident spiking, spike patterns and oscillatory spike trains. However, the corresponding computation in the case of arbitrary input signals is still unclear. This paper provides an overarching picture of the algorithm inherent to STDP, tying together many previous results for commonly used models of pairwise STDP. For a single neuron with plastic excitatory synapses, we show how STDP performs a spectral analysis on the temporal cross-correlograms between its afferent spike trains. The postsynaptic responses and STDP learning window determine kernel functions that specify how the neuron "sees" the input correlations. We thus denote this unsupervised learning scheme as 'kernel spectral component analysis' (kSCA). In particular, the whole input correlation structure must be considered since all plastic synapses compete with each other. We find that kSCA is enhanced when weight-dependent STDP induces gradual synaptic competition. For a spiking neuron with a "linear" response and pairwise STDP alone, we find that kSCA resembles principal component analysis (PCA). However, plain STDP does not isolate correlation sources in general, e.g., when they are mixed among the input spike trains. In other words, it does not perform independent component analysis (ICA). Tuning the neuron to a single correlation source can be achieved when STDP is paired with a homeostatic mechanism that reinforces the competition between synaptic inputs. Our results suggest that neuronal networks equipped with STDP can process signals encoded in the transient spiking activity at the timescales of tens of milliseconds for usual STDP.
format	article
author	Matthieu Gilson Tomoki Fukai Anthony N Burkitt
author_facet	Matthieu Gilson Tomoki Fukai Anthony N Burkitt
author_sort	Matthieu Gilson
title	Spectral analysis of input spike trains by spike-timing-dependent plasticity.
title_short	Spectral analysis of input spike trains by spike-timing-dependent plasticity.
title_full	Spectral analysis of input spike trains by spike-timing-dependent plasticity.
title_fullStr	Spectral analysis of input spike trains by spike-timing-dependent plasticity.
title_full_unstemmed	Spectral analysis of input spike trains by spike-timing-dependent plasticity.
title_sort	spectral analysis of input spike trains by spike-timing-dependent plasticity.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/6d6e9933721e4a40a5f894e4257079a9
work_keys_str_mv	AT matthieugilson spectralanalysisofinputspiketrainsbyspiketimingdependentplasticity AT tomokifukai spectralanalysisofinputspiketrainsbyspiketimingdependentplasticity AT anthonynburkitt spectralanalysisofinputspiketrainsbyspiketimingdependentplasticity
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Spectral analysis of input spike trains by spike-timing-dependent plasticity.

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