Bayesian Integration of Information in Hippocampal Place Cells
Identifieur interne : 001413 ( Main/Merge ); précédent : 001412; suivant : 001414Bayesian Integration of Information in Hippocampal Place Cells
Auteurs : Tamas Madl [Royaume-Uni, Autriche] ; Stan Franklin [États-Unis] ; Ke Chen [Royaume-Uni] ; Daniela Montaldi [Royaume-Uni] ; Robert Trappl [Autriche]Source :
- PLoS ONE [ 1932-6203 ] ; 2014.
Abstract
Accurate spatial localization requires a mechanism that corrects for errors, which might arise from inaccurate sensory information or neuronal noise. In this paper, we propose that Hippocampal place cells might implement such an error correction mechanism by integrating different sources of information in an approximately Bayes-optimal fashion. We compare the predictions of our model with physiological data from rats. Our results suggest that useful predictions regarding the firing fields of place cells can be made based on a single underlying principle, Bayesian cue integration, and that such predictions are possible using a remarkably small number of model parameters.
Url:
DOI: 10.1371/journal.pone.0089762
PubMed: 24603429
PubMed Central: 3945610
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id="aff2"><addr-line>Institute for Intelligent Systems, University of Memphis, Memphis, Tennessee, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Institute for Intelligent Systems, University of Memphis, Memphis, Tennessee</wicri:regionArea><placeName><region type="state">Tennessee</region></placeName></affiliation></author><author><name sortKey="Chen, Ke" sort="Chen, Ke" uniqKey="Chen K" first="Ke" last="Chen">Ke Chen</name><affiliation wicri:level="4"><nlm:aff id="aff1"><addr-line>School of Computer Science, University of Manchester, Manchester, United Kingdom</addr-line></nlm:aff><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>School of Computer Science, University of Manchester, Manchester</wicri:regionArea><placeName><settlement type="city">Manchester</settlement><region type="country">Angleterre</region><region type="région" nuts="1">Grand Manchester</region></placeName><orgName type="university">Université de Manchester</orgName></affiliation></author><author><name sortKey="Montaldi, Daniela" sort="Montaldi, Daniela" uniqKey="Montaldi D" first="Daniela" last="Montaldi">Daniela Montaldi</name><affiliation wicri:level="4"><nlm:aff id="aff3"><addr-line> School of Psychological Sciences, University of Manchester, Manchester, United Kingdom</addr-line></nlm:aff><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea> School of Psychological Sciences, University of Manchester, Manchester</wicri:regionArea><placeName><settlement type="city">Manchester</settlement><region type="country">Angleterre</region><region type="région" nuts="1">Grand Manchester</region></placeName><orgName type="university">Université de Manchester</orgName></affiliation></author><author><name sortKey="Trappl, Robert" sort="Trappl, Robert" uniqKey="Trappl R" first="Robert" last="Trappl">Robert Trappl</name><affiliation wicri:level="3"><nlm:aff id="aff4"><addr-line>Austrian Research Institute for Artificial Intelligence, Vienna, Austria</addr-line></nlm:aff><country xml:lang="fr">Autriche</country><wicri:regionArea>Austrian Research Institute for Artificial Intelligence, Vienna</wicri:regionArea><placeName><settlement type="city">Vienne (Autriche)</settlement><region nuts="2" type="province">Vienne (Autriche)</region></placeName></affiliation></author></analytic><series><title level="j">PLoS ONE</title><idno type="eISSN">1932-6203</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Accurate spatial localization requires a mechanism that corrects for errors, which might arise from inaccurate sensory information or neuronal noise. In this paper, we propose that Hippocampal place cells might implement such an error correction mechanism by integrating different sources of information in an approximately Bayes-optimal fashion. We compare the predictions of our model with physiological data from rats. Our results suggest that useful predictions regarding the firing fields of place cells can be made based on a single underlying principle, Bayesian cue integration, and that such predictions are possible using a remarkably small number of model parameters.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="O Keefe, J" uniqKey="O Keefe J">J O'Keefe</name></author><author><name sortKey="Dostrovsky, J" uniqKey="Dostrovsky J">J Dostrovsky</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Burgess, N" uniqKey="Burgess N">N Burgess</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Moser, Ei" uniqKey="Moser E">EI Moser</name></author><author><name sortKey="Kropff, E" uniqKey="Kropff E">E Kropff</name></author><author><name sortKey="Moser, Mb" uniqKey="Moser M">MB Moser</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Solstad, T" 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Yang</name></author><author><name sortKey="Shadlen, Mn" uniqKey="Shadlen M">MN Shadlen</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Fiser, J" uniqKey="Fiser J">J Fiser</name></author><author><name sortKey="Berkes, P" uniqKey="Berkes P">P Berkes</name></author><author><name sortKey="Orbn, G" uniqKey="Orbn G">G Orbn</name></author><author><name sortKey="Lengyel, M" uniqKey="Lengyel M">M Lengyel</name></author></analytic></biblStruct><biblStruct></biblStruct></listBibl></div1></back></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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