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Generalization of generative model for neuronal ensemble inference method

Various brain functions that are necessary to maintain life activities materialize through the interaction of countless neurons. Therefore, it is important to analyze functional neuronal network. To elucidate the mechanism of brain function, many studies are being actively conducted on functional ne...

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Autores principales: Kimura, Shun, Takeda, Koujin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10298798/
https://www.ncbi.nlm.nih.gov/pubmed/37368916
http://dx.doi.org/10.1371/journal.pone.0287708
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author Kimura, Shun
Takeda, Koujin
author_facet Kimura, Shun
Takeda, Koujin
author_sort Kimura, Shun
collection PubMed
description Various brain functions that are necessary to maintain life activities materialize through the interaction of countless neurons. Therefore, it is important to analyze functional neuronal network. To elucidate the mechanism of brain function, many studies are being actively conducted on functional neuronal ensemble and hub, including all areas of neuroscience. In addition, recent study suggests that the existence of functional neuronal ensembles and hubs contributes to the efficiency of information processing. For these reasons, there is a demand for methods to infer functional neuronal ensembles from neuronal activity data, and methods based on Bayesian inference have been proposed. However, there is a problem in modeling the activity in Bayesian inference. The features of each neuron’s activity have non-stationarity depending on physiological experimental conditions. As a result, the assumption of stationarity in Bayesian inference model impedes inference, which leads to destabilization of inference results and degradation of inference accuracy. In this study, we extend the range of the variable for expressing the neuronal state, and generalize the likelihood of the model for extended variables. By comparing with the previous study, our model can express the neuronal state in larger space. This generalization without restriction of the binary input enables us to perform soft clustering and apply the method to non-stationary neuroactivity data. In addition, for the effectiveness of the method, we apply the developed method to multiple synthetic fluorescence data generated from the electrical potential data in leaky integrated-and-fire model.
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spelling pubmed-102987982023-06-28 Generalization of generative model for neuronal ensemble inference method Kimura, Shun Takeda, Koujin PLoS One Research Article Various brain functions that are necessary to maintain life activities materialize through the interaction of countless neurons. Therefore, it is important to analyze functional neuronal network. To elucidate the mechanism of brain function, many studies are being actively conducted on functional neuronal ensemble and hub, including all areas of neuroscience. In addition, recent study suggests that the existence of functional neuronal ensembles and hubs contributes to the efficiency of information processing. For these reasons, there is a demand for methods to infer functional neuronal ensembles from neuronal activity data, and methods based on Bayesian inference have been proposed. However, there is a problem in modeling the activity in Bayesian inference. The features of each neuron’s activity have non-stationarity depending on physiological experimental conditions. As a result, the assumption of stationarity in Bayesian inference model impedes inference, which leads to destabilization of inference results and degradation of inference accuracy. In this study, we extend the range of the variable for expressing the neuronal state, and generalize the likelihood of the model for extended variables. By comparing with the previous study, our model can express the neuronal state in larger space. This generalization without restriction of the binary input enables us to perform soft clustering and apply the method to non-stationary neuroactivity data. In addition, for the effectiveness of the method, we apply the developed method to multiple synthetic fluorescence data generated from the electrical potential data in leaky integrated-and-fire model. Public Library of Science 2023-06-27 /pmc/articles/PMC10298798/ /pubmed/37368916 http://dx.doi.org/10.1371/journal.pone.0287708 Text en © 2023 Kimura, Takeda https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://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
Kimura, Shun
Takeda, Koujin
Generalization of generative model for neuronal ensemble inference method
title Generalization of generative model for neuronal ensemble inference method
title_full Generalization of generative model for neuronal ensemble inference method
title_fullStr Generalization of generative model for neuronal ensemble inference method
title_full_unstemmed Generalization of generative model for neuronal ensemble inference method
title_short Generalization of generative model for neuronal ensemble inference method
title_sort generalization of generative model for neuronal ensemble inference method
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10298798/
https://www.ncbi.nlm.nih.gov/pubmed/37368916
http://dx.doi.org/10.1371/journal.pone.0287708
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