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. |
|---|---|
| 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 |
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