Minimal cross-trial generalization in learning the representation of an odor-guided choice task.
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| Title: | Minimal cross-trial generalization in learning the representation of an odor-guided choice task. |
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| Authors: | Song M; Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America., Takahashi YK; National Institute on Drug Abuse Intramural Research Program, NIH, Baltimore, Maryland, United States of America., Burton AC; Department of Psychology, University of Maryland, College Park, Maryland, United States of America.; Program in Neuroscience and Cognitive Science, University of Maryland, College Park, Maryland, United States of America., Roesch MR; Department of Psychology, University of Maryland, College Park, Maryland, United States of America.; Program in Neuroscience and Cognitive Science, University of Maryland, College Park, Maryland, United States of America., Schoenbaum G; National Institute on Drug Abuse Intramural Research Program, NIH, Baltimore, Maryland, United States of America., Niv Y; Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America.; Department of Psychology, Princeton University, Princeton, New Jersey, United States of America., Langdon AJ; Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America.; Department of Psychology, Princeton University, Princeton, New Jersey, United States of America. |
| Source: | PLoS computational biology [PLoS Comput Biol] 2022 Mar 25; Vol. 18 (3), pp. e1009897. Date of Electronic Publication: 2022 Mar 25 (Print Publication: 2022). |
| Publication Type: | Journal Article; Research Support, N.I.H., Extramural |
| 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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| ISSN: | 1553-7358 |
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| DOI: | 10.1371/journal.pcbi.1009897 |
