Disentangling signal and noise in neural responses through generative modeling.

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Bibliographic Details
Title: Disentangling signal and noise in neural responses through generative modeling.
Authors: Kay K; Center for Magnetic Resonance Research (CMRR), Department of Radiology, University of Minnesota., Prince JS; Department of Psychology, Harvard University., Gebhart T; Department of Computer Science, University of Minnesota., Tuckute G; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology.; McGovern Institute for Brain Research, Massachusetts Institute of Technology., Zhou J; Center for Computational Neuroscience (CCN), Flatiron Institute., Naselaris T; Center for Magnetic Resonance Research (CMRR), Department of Radiology, University of Minnesota.; Department of Neuroscience, University of Minnesota., Schutt H; Department of Behavioural and Cognitive Sciences, Université du Luxembourg.
Source: BioRxiv : the preprint server for biology [bioRxiv] 2024 Aug 22. Date of Electronic Publication: 2024 Aug 22.
Publication Type: Journal Article; Preprint
Journal Info: Country of Publication: United States NLM ID: 101680187 Publication Model: Electronic Cited Medium: Internet ISSN: 2692-8205 (Electronic) Linking ISSN: 26928205 NLM ISO Abbreviation: bioRxiv Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2692-8205
DOI:10.1101/2024.04.22.590510