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Modeling functional cell types in spike train data
A major goal of computational neuroscience is to build accurate models of the activity of neurons that can be used to interpret their function in circuits. Here, we explore using functional cell types to refine single-cell models by grouping them into functionally relevant classes. Formally, we defi...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10002678/ https://www.ncbi.nlm.nih.gov/pubmed/36909648 http://dx.doi.org/10.1101/2023.02.28.530327 |
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author | Zdeblick, Daniel N. Shea-Brown, Eric T. Witten, Daniela M. Buice, Michael A. |
author_facet | Zdeblick, Daniel N. Shea-Brown, Eric T. Witten, Daniela M. Buice, Michael A. |
author_sort | Zdeblick, Daniel N. |
collection | PubMed |
description | A major goal of computational neuroscience is to build accurate models of the activity of neurons that can be used to interpret their function in circuits. Here, we explore using functional cell types to refine single-cell models by grouping them into functionally relevant classes. Formally, we define a hierarchical generative model for cell types, single-cell parameters, and neural responses, and then derive an expectation-maximization algorithm with variational inference that maximizes the likelihood of the neural recordings. We apply this “simultaneous” method to estimate cell types and fit single-cell models from simulated data, and find that it accurately recovers the ground truth parameters. We then apply our approach to in vitro neural recordings from neurons in mouse primary visual cortex, and find that it yields improved prediction of single-cell activity. We demonstrate that the discovered cell-type clusters are well separated and generalizable, and thus amenable to interpretation. We then compare discovered cluster memberships with locational, morphological, and transcriptomic data. Our findings reveal the potential to improve models of neural responses by explicitly allowing for shared functional properties across neurons. |
format | Online Article Text |
id | pubmed-10002678 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-100026782023-03-11 Modeling functional cell types in spike train data Zdeblick, Daniel N. Shea-Brown, Eric T. Witten, Daniela M. Buice, Michael A. bioRxiv Article A major goal of computational neuroscience is to build accurate models of the activity of neurons that can be used to interpret their function in circuits. Here, we explore using functional cell types to refine single-cell models by grouping them into functionally relevant classes. Formally, we define a hierarchical generative model for cell types, single-cell parameters, and neural responses, and then derive an expectation-maximization algorithm with variational inference that maximizes the likelihood of the neural recordings. We apply this “simultaneous” method to estimate cell types and fit single-cell models from simulated data, and find that it accurately recovers the ground truth parameters. We then apply our approach to in vitro neural recordings from neurons in mouse primary visual cortex, and find that it yields improved prediction of single-cell activity. We demonstrate that the discovered cell-type clusters are well separated and generalizable, and thus amenable to interpretation. We then compare discovered cluster memberships with locational, morphological, and transcriptomic data. Our findings reveal the potential to improve models of neural responses by explicitly allowing for shared functional properties across neurons. Cold Spring Harbor Laboratory 2023-03-01 /pmc/articles/PMC10002678/ /pubmed/36909648 http://dx.doi.org/10.1101/2023.02.28.530327 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator. |
spellingShingle | Article Zdeblick, Daniel N. Shea-Brown, Eric T. Witten, Daniela M. Buice, Michael A. Modeling functional cell types in spike train data |
title | Modeling functional cell types in spike train data |
title_full | Modeling functional cell types in spike train data |
title_fullStr | Modeling functional cell types in spike train data |
title_full_unstemmed | Modeling functional cell types in spike train data |
title_short | Modeling functional cell types in spike train data |
title_sort | modeling functional cell types in spike train data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10002678/ https://www.ncbi.nlm.nih.gov/pubmed/36909648 http://dx.doi.org/10.1101/2023.02.28.530327 |
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