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dc.contributor.authorSanders, Honi
dc.contributor.authorWilson, Matthew A.
dc.contributor.authorGershman, Samueal J.
dc.date.accessioned2019-09-05T16:06:11Z
dc.date.available2019-09-05T16:06:11Z
dc.date.issued2019-08-22
dc.identifier.urihttps://hdl.handle.net/1721.1/122040
dc.description.abstractCells in the hippocampus tuned to spatial location (place cells) typically change their tuning when an animal changes context, a phenomenon known as remapping. A fundamental challenge to understanding remapping is the fact that what counts as a “context change” has never been precisely defined. Furthermore, different remapping phenomena have been classified on the basis of how much the tuning changes after different types and degrees of context change, but the relationship between these variables is not clear. We address these ambiguities by formalizing remapping in terms of hidden state inference. According to this view, remapping does not directly reflect objective, observable properties of the environment, but rather subjective beliefs about the hidden state of the environment. We show how the hidden state framework can resolve a number of puzzles about the nature of remapping.en_US
dc.description.sponsorshipThis work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216.en_US
dc.publisherCenter for Brains, Minds and Machines (CBMM), bioRxiven_US
dc.relation.ispartofseriesCBMM Memo;101
dc.titleHippocampal Remapping as Hidden State Inferenceen_US
dc.typeTechnical Reporten_US
dc.typeWorking Paperen_US
dc.typeOtheren_US


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