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Exactly solvable statistical physics models for large neuronal populations

Maximum entropy methods provide a principled path connecting measurements of neural activity directly to statistical physics models, and this approach has been successful for populations of [Formula: see text] neurons. As [Formula: see text] increases in new experiments, we enter an undersampled reg...

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Autores principales: Lynn, Christopher W., Yu, Qiwei, Pang, Rich, Bialek, William, Palmer, Stephanie E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cornell University 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10614989/
https://www.ncbi.nlm.nih.gov/pubmed/37904743
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author Lynn, Christopher W.
Yu, Qiwei
Pang, Rich
Bialek, William
Palmer, Stephanie E.
author_facet Lynn, Christopher W.
Yu, Qiwei
Pang, Rich
Bialek, William
Palmer, Stephanie E.
author_sort Lynn, Christopher W.
collection PubMed
description Maximum entropy methods provide a principled path connecting measurements of neural activity directly to statistical physics models, and this approach has been successful for populations of [Formula: see text] neurons. As [Formula: see text] increases in new experiments, we enter an undersampled regime where we have to choose which observables should be constrained in the maximum entropy construction. The best choice is the one that provides the greatest reduction in entropy, defining a “minimax entropy” principle. This principle becomes tractable if we restrict attention to correlations among pairs of neurons that link together into a tree; we can find the best tree efficiently, and the underlying statistical physics models are exactly solved. We use this approach to analyze experiments on [Formula: see text] neurons in the mouse hippocampus, and show that the resulting model captures the distribution of synchronous activity in the network.
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spelling pubmed-106149892023-10-31 Exactly solvable statistical physics models for large neuronal populations Lynn, Christopher W. Yu, Qiwei Pang, Rich Bialek, William Palmer, Stephanie E. ArXiv Article Maximum entropy methods provide a principled path connecting measurements of neural activity directly to statistical physics models, and this approach has been successful for populations of [Formula: see text] neurons. As [Formula: see text] increases in new experiments, we enter an undersampled regime where we have to choose which observables should be constrained in the maximum entropy construction. The best choice is the one that provides the greatest reduction in entropy, defining a “minimax entropy” principle. This principle becomes tractable if we restrict attention to correlations among pairs of neurons that link together into a tree; we can find the best tree efficiently, and the underlying statistical physics models are exactly solved. We use this approach to analyze experiments on [Formula: see text] neurons in the mouse hippocampus, and show that the resulting model captures the distribution of synchronous activity in the network. Cornell University 2023-10-16 /pmc/articles/PMC10614989/ /pubmed/37904743 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
spellingShingle Article
Lynn, Christopher W.
Yu, Qiwei
Pang, Rich
Bialek, William
Palmer, Stephanie E.
Exactly solvable statistical physics models for large neuronal populations
title Exactly solvable statistical physics models for large neuronal populations
title_full Exactly solvable statistical physics models for large neuronal populations
title_fullStr Exactly solvable statistical physics models for large neuronal populations
title_full_unstemmed Exactly solvable statistical physics models for large neuronal populations
title_short Exactly solvable statistical physics models for large neuronal populations
title_sort exactly solvable statistical physics models for large neuronal populations
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10614989/
https://www.ncbi.nlm.nih.gov/pubmed/37904743
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