Cargando…

Stochastic Computations in Cortical Microcircuit Models

Experimental data from neuroscience suggest that a substantial amount of knowledge is stored in the brain in the form of probability distributions over network states and trajectories of network states. We provide a theoretical foundation for this hypothesis by showing that even very detailed models...

Descripción completa

Detalles Bibliográficos
Autores principales: Habenschuss, Stefan, Jonke, Zeno, Maass, Wolfgang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3828141/
https://www.ncbi.nlm.nih.gov/pubmed/24244126
http://dx.doi.org/10.1371/journal.pcbi.1003311
_version_ 1782291186227085312
author Habenschuss, Stefan
Jonke, Zeno
Maass, Wolfgang
author_facet Habenschuss, Stefan
Jonke, Zeno
Maass, Wolfgang
author_sort Habenschuss, Stefan
collection PubMed
description Experimental data from neuroscience suggest that a substantial amount of knowledge is stored in the brain in the form of probability distributions over network states and trajectories of network states. We provide a theoretical foundation for this hypothesis by showing that even very detailed models for cortical microcircuits, with data-based diverse nonlinear neurons and synapses, have a stationary distribution of network states and trajectories of network states to which they converge exponentially fast from any initial state. We demonstrate that this convergence holds in spite of the non-reversibility of the stochastic dynamics of cortical microcircuits. We further show that, in the presence of background network oscillations, separate stationary distributions emerge for different phases of the oscillation, in accordance with experimentally reported phase-specific codes. We complement these theoretical results by computer simulations that investigate resulting computation times for typical probabilistic inference tasks on these internally stored distributions, such as marginalization or marginal maximum-a-posteriori estimation. Furthermore, we show that the inherent stochastic dynamics of generic cortical microcircuits enables them to quickly generate approximate solutions to difficult constraint satisfaction problems, where stored knowledge and current inputs jointly constrain possible solutions. This provides a powerful new computing paradigm for networks of spiking neurons, that also throws new light on how networks of neurons in the brain could carry out complex computational tasks such as prediction, imagination, memory recall and problem solving.
format Online
Article
Text
id pubmed-3828141
institution National Center for Biotechnology Information
language English
publishDate 2013
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-38281412013-11-16 Stochastic Computations in Cortical Microcircuit Models Habenschuss, Stefan Jonke, Zeno Maass, Wolfgang PLoS Comput Biol Research Article Experimental data from neuroscience suggest that a substantial amount of knowledge is stored in the brain in the form of probability distributions over network states and trajectories of network states. We provide a theoretical foundation for this hypothesis by showing that even very detailed models for cortical microcircuits, with data-based diverse nonlinear neurons and synapses, have a stationary distribution of network states and trajectories of network states to which they converge exponentially fast from any initial state. We demonstrate that this convergence holds in spite of the non-reversibility of the stochastic dynamics of cortical microcircuits. We further show that, in the presence of background network oscillations, separate stationary distributions emerge for different phases of the oscillation, in accordance with experimentally reported phase-specific codes. We complement these theoretical results by computer simulations that investigate resulting computation times for typical probabilistic inference tasks on these internally stored distributions, such as marginalization or marginal maximum-a-posteriori estimation. Furthermore, we show that the inherent stochastic dynamics of generic cortical microcircuits enables them to quickly generate approximate solutions to difficult constraint satisfaction problems, where stored knowledge and current inputs jointly constrain possible solutions. This provides a powerful new computing paradigm for networks of spiking neurons, that also throws new light on how networks of neurons in the brain could carry out complex computational tasks such as prediction, imagination, memory recall and problem solving. Public Library of Science 2013-11-14 /pmc/articles/PMC3828141/ /pubmed/24244126 http://dx.doi.org/10.1371/journal.pcbi.1003311 Text en © 2013 Habenschuss et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Habenschuss, Stefan
Jonke, Zeno
Maass, Wolfgang
Stochastic Computations in Cortical Microcircuit Models
title Stochastic Computations in Cortical Microcircuit Models
title_full Stochastic Computations in Cortical Microcircuit Models
title_fullStr Stochastic Computations in Cortical Microcircuit Models
title_full_unstemmed Stochastic Computations in Cortical Microcircuit Models
title_short Stochastic Computations in Cortical Microcircuit Models
title_sort stochastic computations in cortical microcircuit models
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3828141/
https://www.ncbi.nlm.nih.gov/pubmed/24244126
http://dx.doi.org/10.1371/journal.pcbi.1003311
work_keys_str_mv AT habenschussstefan stochasticcomputationsincorticalmicrocircuitmodels
AT jonkezeno stochasticcomputationsincorticalmicrocircuitmodels
AT maasswolfgang stochasticcomputationsincorticalmicrocircuitmodels