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Modeling conditional distributions of neural and behavioral data with masked variational autoencoders

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dc.contributor.authorSchulz, Auguste
dc.contributor.authorVetter, Julius
dc.contributor.authorGao, Richard
dc.contributor.authorMorales, Daniel
dc.contributor.authorLobato-Rios, Victor
dc.contributor.authorRamdya, Pavan
dc.contributor.authorGoncalves, Pedro J.
dc.contributor.authorMacke, Jakob H.
dc.contributor.imecauthorGoncalves, Pedro J.
dc.date.accessioned2025-03-07T21:02:43Z
dc.date.available2025-03-07T21:02:43Z
dc.date.issued2025-MAR 25
dc.description.wosFundingTextWe thank Artur Speiser and Paul Fischer for data management and technical support, Lisa Haxel and Michael Deistler for feedback on the manuscript, and all Mackelab members for discussions. This work was supported by the German Research Foundation (DFG) through Germany's Excellence Strategy (EXC-Number 2064/1, PN 390727645) and SFB1233 (PN 276693517) , SFB 1089 (PN 227953431) , the German Federal Ministry of Education and Research (Tubingen AI Center, FKZ: 01IS18039) , and the Human Frontier Science Program (HFSP) , and the European Union (ERC, DeepCoMechTome, 101089288) . A.S. and J.V. are members of the International Max Planck Research School for Intelligent Systems (IMPRS-IS) . D.M. acknowledges a Marie Curie EuroTech postdoctoral fellowship, a Swiss Government Excellence Postdoctoral Scholarship (2018.0483) , and funding from the European Union's Horizon 2020 research and innovation program under the Marie Sk 1 o- dowska-Curie grant agreement no. 754462. V.L.-R. acknowledges support from the Mexican National Council for Science and Technology, CONACYT, under the grant number 709993. P.R. acknowledges support from an SNSF Project grant (no. 175667) and an SNSF Eccellenza grant (no. 181239) .
dc.identifier.doi10.1016/j.celrep.2025.115338
dc.identifier.issn2211-1247
dc.identifier.pmidMEDLINE:39985768
dc.identifier.urihttps://imec-publications.be/handle/20.500.12860/45356
dc.publisherCELL PRESS
dc.source.issue3
dc.source.journalCELL REPORTS
dc.source.numberofpages21
dc.source.volume44
dc.subject.keywordsMOVEMENT
dc.title

Modeling conditional distributions of neural and behavioral data with masked variational autoencoders

dc.typeJournal article
dspace.entity.typePublication
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