{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T13:08:37Z","timestamp":1740143317510,"version":"3.37.3"},"reference-count":27,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2023,10,12]],"date-time":"2023-10-12T00:00:00Z","timestamp":1697068800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01DA047869"],"award-info":[{"award-number":["R01DA047869"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01EB026908"],"award-info":[{"award-number":["R01EB026908"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000893","name":"Simons Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000893","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>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<jats:italic>functional cell types<\/jats:italic>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 \u201csimultaneous\u201d 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<jats:italic>in vitro<\/jats:italic>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.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1011509","type":"journal-article","created":{"date-parts":[[2023,10,12]],"date-time":"2023-10-12T17:22:21Z","timestamp":1697131341000},"page":"e1011509","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Modeling functional cell types in spike train data"],"prefix":"10.1371","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-6869-796X","authenticated-orcid":true,"given":"Daniel N.","family":"Zdeblick","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9012-1396","authenticated-orcid":true,"given":"Eric T.","family":"Shea-Brown","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1764-1184","authenticated-orcid":true,"given":"Daniela M.","family":"Witten","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2196-1498","authenticated-orcid":true,"given":"Michael A.","family":"Buice","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2023,10,12]]},"reference":[{"issue":"7","key":"pcbi.1011509.ref001","doi-asserted-by":"crossref","first-page":"1182","DOI":"10.1038\/s41593-019-0417-0","article-title":"Classification of electrophysiological and morphological neuron types in the mouse visual cortex","volume":"22","author":"NW Gouwens","year":"2019","journal-title":"Nature Neuroscience"},{"issue":"2","key":"pcbi.1011509.ref002","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1038\/nn.4216","article-title":"Adult mouse cortical cell taxonomy revealed by single cell transcriptomics","volume":"19","author":"B Tasic","year":"2016","journal-title":"Nature Neuroscience"},{"issue":"10","key":"pcbi.1011509.ref003","doi-asserted-by":"crossref","first-page":"e1005814","DOI":"10.1371\/journal.pcbi.1005814","article-title":"Transcriptomic correlates of neuron electrophysiological diversity","volume":"13","author":"SJ Tripathy","year":"2017","journal-title":"PLOS Computational Biology"},{"issue":"7729","key":"pcbi.1011509.ref004","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1038\/s41586-018-0654-5","article-title":"Shared and distinct transcriptomic cell types across neocortical areas","volume":"563","author":"B Tasic","year":"2018","journal-title":"Nature"},{"issue":"5","key":"pcbi.1011509.ref005","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1016\/j.neunet.2004.04.003","article-title":"Electrophysiological classes of neocortical neurons","volume":"17","author":"D Contreras","year":"2004","journal-title":"Neural Networks"},{"issue":"1","key":"pcbi.1011509.ref006","doi-asserted-by":"crossref","DOI":"10.1038\/s41467-017-02717-4","article-title":"Generalized leaky integrate-and-fire models classify multiple neuron types","volume":"9","author":"C Teeter","year":"2018","journal-title":"Nature Communications"},{"key":"pcbi.1011509.ref007","doi-asserted-by":"crossref","DOI":"10.7554\/eLife.04250","article-title":"Automatic discovery of cell types and microcircuitry from neural connectomics","volume":"4","author":"E Jonas","year":"2015","journal-title":"eLife"},{"issue":"1","key":"pcbi.1011509.ref008","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum Likelihood from Incomplete Data Via the EM Algorithm","volume":"39","author":"AP Dempster","year":"1977","journal-title":"Journal of the Royal Statistical Society: Series B (Methodological)"},{"unstructured":"Overview :: Allen Brain Atlas: Cell Types;. 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