Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons.

The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there ex...

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Autores principales:	Lars Buesing, Johannes Bill, Bernhard Nessler, Wolfgang Maass
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/a94c33180bfb45c2b702bdc4888cd9e9
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spelling	oai:doaj.org-article:a94c33180bfb45c2b702bdc4888cd9e92021-11-18T05:51:48ZNeural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons.1553-734X1553-735810.1371/journal.pcbi.1002211https://doaj.org/article/a94c33180bfb45c2b702bdc4888cd9e92011-11-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22096452/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there exists a powerful computational framework for stochastic computations, probabilistic inference by sampling, which can explain a large number of macroscopic experimental data in neuroscience and cognitive science. But it has turned out to be surprisingly difficult to create a link between these abstract models for stochastic computations and more detailed models of the dynamics of networks of spiking neurons. Here we create such a link and show that under some conditions the stochastic firing activity of networks of spiking neurons can be interpreted as probabilistic inference via Markov chain Monte Carlo (MCMC) sampling. Since common methods for MCMC sampling in distributed systems, such as Gibbs sampling, are inconsistent with the dynamics of spiking neurons, we introduce a different approach based on non-reversible Markov chains that is able to reflect inherent temporal processes of spiking neuronal activity through a suitable choice of random variables. We propose a neural network model and show by a rigorous theoretical analysis that its neural activity implements MCMC sampling of a given distribution, both for the case of discrete and continuous time. This provides a step towards closing the gap between abstract functional models of cortical computation and more detailed models of networks of spiking neurons.Lars BuesingJohannes BillBernhard NesslerWolfgang MaassPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 7, Iss 11, p e1002211 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Lars Buesing Johannes Bill Bernhard Nessler Wolfgang Maass Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons.
description	The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there exists a powerful computational framework for stochastic computations, probabilistic inference by sampling, which can explain a large number of macroscopic experimental data in neuroscience and cognitive science. But it has turned out to be surprisingly difficult to create a link between these abstract models for stochastic computations and more detailed models of the dynamics of networks of spiking neurons. Here we create such a link and show that under some conditions the stochastic firing activity of networks of spiking neurons can be interpreted as probabilistic inference via Markov chain Monte Carlo (MCMC) sampling. Since common methods for MCMC sampling in distributed systems, such as Gibbs sampling, are inconsistent with the dynamics of spiking neurons, we introduce a different approach based on non-reversible Markov chains that is able to reflect inherent temporal processes of spiking neuronal activity through a suitable choice of random variables. We propose a neural network model and show by a rigorous theoretical analysis that its neural activity implements MCMC sampling of a given distribution, both for the case of discrete and continuous time. This provides a step towards closing the gap between abstract functional models of cortical computation and more detailed models of networks of spiking neurons.
format	article
author	Lars Buesing Johannes Bill Bernhard Nessler Wolfgang Maass
author_facet	Lars Buesing Johannes Bill Bernhard Nessler Wolfgang Maass
author_sort	Lars Buesing
title	Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons.
title_short	Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons.
title_full	Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons.
title_fullStr	Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons.
title_full_unstemmed	Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons.
title_sort	neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/a94c33180bfb45c2b702bdc4888cd9e9
work_keys_str_mv	AT larsbuesing neuraldynamicsassamplingamodelforstochasticcomputationinrecurrentnetworksofspikingneurons AT johannesbill neuraldynamicsassamplingamodelforstochasticcomputationinrecurrentnetworksofspikingneurons AT bernhardnessler neuraldynamicsassamplingamodelforstochasticcomputationinrecurrentnetworksofspikingneurons AT wolfgangmaass neuraldynamicsassamplingamodelforstochasticcomputationinrecurrentnetworksofspikingneurons
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Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons.

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