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dc.contributor.authorAgarwal, Rahul
dc.contributor.authorChen, Zhe
dc.contributor.authorKloosterman, Fabian
dc.contributor.authorWilson, Matthew A.
dc.contributor.authorSarma, Sridevi V.
dc.date.accessioned2016-07-18T19:20:42Z
dc.date.available2016-07-18T19:20:42Z
dc.date.issued2016-06
dc.identifier.issn0899-7667
dc.identifier.issn1530-888X
dc.identifier.urihttp://hdl.handle.net/1721.1/103679
dc.description.abstractPyramidal neurons recorded from the rat hippocampus and entorhinal cortex, such as place and grid cells, have diverse receptive fields, which are either unimodal or multimodal. Spiking activity from these cells encodes information about the spatial position of a freely foraging rat. At fine timescales, a neuron’s spike activity also depends significantly on its own spike history. However, due to limitations of current parametric modeling approaches, it remains a challenge to estimate complex, multimodal neuronal receptive fields while incorporating spike history dependence. Furthermore, efforts to decode the rat’s trajectory in one- or two-dimensional space from hippocampal ensemble spiking activity have mainly focused on spike history–independent neuronal encoding models. In this letter, we address these two important issues by extending a recently introduced nonparametric neural encoding framework that allows modeling both complex spatial receptive fields and spike history dependencies. Using this extended nonparametric approach, we develop novel algorithms for decoding a rat’s trajectory based on recordings of hippocampal place cells and entorhinal grid cells. Results show that both encoding and decoding models derived from our new method performed significantly better than state-of-the-art encoding and decoding models on 6 minutes of test data. In addition, our model’s performance remains invariant to the apparent modality of the neuron’s receptive field.en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (NSF-CRCNS award (no. 1307645))en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (NIH grant R01-MH06197)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (NIH grant TR01-GM10498)en_US
dc.description.sponsorshipUnited States. Office of Naval Research (ONR-MURI grant N00014-10-1-0936)en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (CAREER Award 1055560)en_US
dc.description.sponsorshipBurroughs Wellcome Fund (CASI Award 1007274)en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (NSF EFRI-M3C)en_US
dc.language.isoen_US
dc.publisherMIT Pressen_US
dc.relation.isversionofhttp://dx.doi.org/10.1162/NECO_a_00847en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceMIT Pressen_US
dc.titleA Novel Nonparametric Approach for Neural Encoding and Decoding Models of Multimodal Receptive Fieldsen_US
dc.typeArticleen_US
dc.identifier.citationAgarwal, Rahul, Zhe Chen, Fabian Kloosterman, Matthew A. Wilson, and Sridevi V. Sarma. “A Novel Nonparametric Approach for Neural Encoding and Decoding Models of Multimodal Receptive Fields.” Neural Computation 28, no. 7 (July 2016): 1356–1387.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciencesen_US
dc.contributor.departmentPicower Institute for Learning and Memoryen_US
dc.contributor.mitauthorAgarwal, Rahulen_US
dc.contributor.mitauthorChen, Zheen_US
dc.contributor.mitauthorKloosterman, Fabianen_US
dc.contributor.mitauthorWilson, Matthew A.en_US
dc.contributor.mitauthorSarma, Sridevi V.en_US
dc.relation.journalNeural Computationen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsAgarwal, Rahul; Chen, Zhe; Kloosterman, Fabian; Wilson, Matthew A.; Sarma, Sridevi V.en_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0001-7149-3584
mit.licensePUBLISHER_POLICYen_US
mit.metadata.statusComplete


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