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Approximate Inference for Time-Varying Interactions and Macroscopic Dynamics of Neural Populations

The models in statistical physics such as an Ising model offer a convenient way to characterize stationary activity of neural populations. Such stationary activity of neurons may be expected for recordings from in vitro slices or anesthetized animals. However, modeling activity of cortical circuitri...

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Detalles Bibliográficos
Autores principales: Donner, Christian, Obermayer, Klaus, Shimazaki, Hideaki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5283755/
https://www.ncbi.nlm.nih.gov/pubmed/28095421
http://dx.doi.org/10.1371/journal.pcbi.1005309
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author Donner, Christian
Obermayer, Klaus
Shimazaki, Hideaki
author_facet Donner, Christian
Obermayer, Klaus
Shimazaki, Hideaki
author_sort Donner, Christian
collection PubMed
description The models in statistical physics such as an Ising model offer a convenient way to characterize stationary activity of neural populations. Such stationary activity of neurons may be expected for recordings from in vitro slices or anesthetized animals. However, modeling activity of cortical circuitries of awake animals has been more challenging because both spike-rates and interactions can change according to sensory stimulation, behavior, or an internal state of the brain. Previous approaches modeling the dynamics of neural interactions suffer from computational cost; therefore, its application was limited to only a dozen neurons. Here by introducing multiple analytic approximation methods to a state-space model of neural population activity, we make it possible to estimate dynamic pairwise interactions of up to 60 neurons. More specifically, we applied the pseudolikelihood approximation to the state-space model, and combined it with the Bethe or TAP mean-field approximation to make the sequential Bayesian estimation of the model parameters possible. The large-scale analysis allows us to investigate dynamics of macroscopic properties of neural circuitries underlying stimulus processing and behavior. We show that the model accurately estimates dynamics of network properties such as sparseness, entropy, and heat capacity by simulated data, and demonstrate utilities of these measures by analyzing activity of monkey V4 neurons as well as a simulated balanced network of spiking neurons.
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spelling pubmed-52837552017-02-17 Approximate Inference for Time-Varying Interactions and Macroscopic Dynamics of Neural Populations Donner, Christian Obermayer, Klaus Shimazaki, Hideaki PLoS Comput Biol Research Article The models in statistical physics such as an Ising model offer a convenient way to characterize stationary activity of neural populations. Such stationary activity of neurons may be expected for recordings from in vitro slices or anesthetized animals. However, modeling activity of cortical circuitries of awake animals has been more challenging because both spike-rates and interactions can change according to sensory stimulation, behavior, or an internal state of the brain. Previous approaches modeling the dynamics of neural interactions suffer from computational cost; therefore, its application was limited to only a dozen neurons. Here by introducing multiple analytic approximation methods to a state-space model of neural population activity, we make it possible to estimate dynamic pairwise interactions of up to 60 neurons. More specifically, we applied the pseudolikelihood approximation to the state-space model, and combined it with the Bethe or TAP mean-field approximation to make the sequential Bayesian estimation of the model parameters possible. The large-scale analysis allows us to investigate dynamics of macroscopic properties of neural circuitries underlying stimulus processing and behavior. We show that the model accurately estimates dynamics of network properties such as sparseness, entropy, and heat capacity by simulated data, and demonstrate utilities of these measures by analyzing activity of monkey V4 neurons as well as a simulated balanced network of spiking neurons. Public Library of Science 2017-01-17 /pmc/articles/PMC5283755/ /pubmed/28095421 http://dx.doi.org/10.1371/journal.pcbi.1005309 Text en © 2017 Donner 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 (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Donner, Christian
Obermayer, Klaus
Shimazaki, Hideaki
Approximate Inference for Time-Varying Interactions and Macroscopic Dynamics of Neural Populations
title Approximate Inference for Time-Varying Interactions and Macroscopic Dynamics of Neural Populations
title_full Approximate Inference for Time-Varying Interactions and Macroscopic Dynamics of Neural Populations
title_fullStr Approximate Inference for Time-Varying Interactions and Macroscopic Dynamics of Neural Populations
title_full_unstemmed Approximate Inference for Time-Varying Interactions and Macroscopic Dynamics of Neural Populations
title_short Approximate Inference for Time-Varying Interactions and Macroscopic Dynamics of Neural Populations
title_sort approximate inference for time-varying interactions and macroscopic dynamics of neural populations
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5283755/
https://www.ncbi.nlm.nih.gov/pubmed/28095421
http://dx.doi.org/10.1371/journal.pcbi.1005309
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