Disentangling signal and noise in neural responses through generative modeling.
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| Title: | Disentangling signal and noise in neural responses through generative modeling. |
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| Authors: | Kay K; Center for Magnetic Resonance Research (CMRR), Department of Radiology, University of Minnesota, Minneapolis, Minnesota, United States of America., Prince JS; Department of Psychology, Harvard University, Cambridge, Massachusetts, United States of America., Gebhart T; Department of Computer Science, University of Minnesota, Minneapolis, Minnesota, United States of America., Tuckute G; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America., Zhou J; Center for Computational Neuroscience (CCN), Flatiron Institute, New York, New York, United States of America., Naselaris T; Center for Magnetic Resonance Research (CMRR), Department of Radiology, University of Minnesota, Minneapolis, Minnesota, United States of America.; Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota, United States of America., Schütt HH; Department of Behavioural and Cognitive Sciences, Université du Luxembourg, Esch-Belval Esch-sur-Alzette, Luxembourg. |
| Source: | PLoS computational biology [PLoS Comput Biol] 2025 Jul 21; Vol. 21 (7), pp. e1012092. Date of Electronic Publication: 2025 Jul 21 (Print Publication: 2025). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet ISSN: 1553-7358 (Electronic) Linking ISSN: 1553734X NLM ISO Abbreviation: PLoS Comput Biol Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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