Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity.

The principles by which networks of neurons compute, and how spike-timing dependent plasticity (STDP) of synaptic weights generates and maintains their computational function, are unknown. Preceding work has shown that soft winner-take-all (WTA) circuits, where pyramidal neurons inhibit each other v...

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Autores principales:	Bernhard Nessler, Michael Pfeiffer, Lars Buesing, Wolfgang Maass
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/79431ec7b4384b8db8731574949f8579
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spelling	oai:doaj.org-article:79431ec7b4384b8db8731574949f85792021-11-18T05:52:12ZBayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity.1553-734X1553-735810.1371/journal.pcbi.1003037https://doaj.org/article/79431ec7b4384b8db8731574949f85792013-04-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23633941/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358The principles by which networks of neurons compute, and how spike-timing dependent plasticity (STDP) of synaptic weights generates and maintains their computational function, are unknown. Preceding work has shown that soft winner-take-all (WTA) circuits, where pyramidal neurons inhibit each other via interneurons, are a common motif of cortical microcircuits. We show through theoretical analysis and computer simulations that Bayesian computation is induced in these network motifs through STDP in combination with activity-dependent changes in the excitability of neurons. The fundamental components of this emergent Bayesian computation are priors that result from adaptation of neuronal excitability and implicit generative models for hidden causes that are created in the synaptic weights through STDP. In fact, a surprising result is that STDP is able to approximate a powerful principle for fitting such implicit generative models to high-dimensional spike inputs: Expectation Maximization. Our results suggest that the experimentally observed spontaneous activity and trial-to-trial variability of cortical neurons are essential features of their information processing capability, since their functional role is to represent probability distributions rather than static neural codes. Furthermore it suggests networks of Bayesian computation modules as a new model for distributed information processing in the cortex.Bernhard NesslerMichael PfeifferLars BuesingWolfgang MaassPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 9, Iss 4, p e1003037 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Bernhard Nessler Michael Pfeiffer Lars Buesing Wolfgang Maass Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity.
description	The principles by which networks of neurons compute, and how spike-timing dependent plasticity (STDP) of synaptic weights generates and maintains their computational function, are unknown. Preceding work has shown that soft winner-take-all (WTA) circuits, where pyramidal neurons inhibit each other via interneurons, are a common motif of cortical microcircuits. We show through theoretical analysis and computer simulations that Bayesian computation is induced in these network motifs through STDP in combination with activity-dependent changes in the excitability of neurons. The fundamental components of this emergent Bayesian computation are priors that result from adaptation of neuronal excitability and implicit generative models for hidden causes that are created in the synaptic weights through STDP. In fact, a surprising result is that STDP is able to approximate a powerful principle for fitting such implicit generative models to high-dimensional spike inputs: Expectation Maximization. Our results suggest that the experimentally observed spontaneous activity and trial-to-trial variability of cortical neurons are essential features of their information processing capability, since their functional role is to represent probability distributions rather than static neural codes. Furthermore it suggests networks of Bayesian computation modules as a new model for distributed information processing in the cortex.
format	article
author	Bernhard Nessler Michael Pfeiffer Lars Buesing Wolfgang Maass
author_facet	Bernhard Nessler Michael Pfeiffer Lars Buesing Wolfgang Maass
author_sort	Bernhard Nessler
title	Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity.
title_short	Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity.
title_full	Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity.
title_fullStr	Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity.
title_full_unstemmed	Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity.
title_sort	bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/79431ec7b4384b8db8731574949f8579
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Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity.

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