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dc.contributor.authorAmemori, Ken-ichi
dc.contributor.authorGibb, Leif G.
dc.contributor.authorGraybiel, Ann M.
dc.date.accessioned2011-10-24T20:33:45Z
dc.date.available2011-10-24T20:33:45Z
dc.date.issued2011-05
dc.date.submitted2010-12
dc.identifier.issn1662-5161
dc.identifier.urihttp://hdl.handle.net/1721.1/66562
dc.description.abstractWe propose here that the modular organization of the striatum reflects a context-sensitive modular learning architecture in which clustered striosome–matrisome domains participate in modular reinforcement learning (RL). Based on anatomical and physiological evidence, it has been suggested that the modular organization of the striatum could represent a learning architecture. There is not, however, a coherent view of how such a learning architecture could relate to the organization of striatal outputs into the direct and indirect pathways of the basal ganglia, nor a clear formulation of how such a modular architecture relates to the RL functions attributed to the striatum. Here, we hypothesize that striosome–matrisome modules not only learn to bias behavior toward specific actions, as in standard RL, but also learn to assess their own relevance to the environmental context and modulate their own learning and activity on this basis. We further hypothesize that the contextual relevance or “responsibility” of modules is determined by errors in predictions of environmental features and that such responsibility is assigned by striosomes and conveyed to matrisomes via local circuit interneurons. To examine these hypotheses and to identify the general requirements for realizing this architecture in the nervous system, we developed a simple modular RL model. We then constructed a network model of basal ganglia circuitry that includes these modules and the direct and indirect pathways. Based on simple assumptions, this model suggests that while the direct pathway may promote actions based on striatal action values, the indirect pathway may act as a gating network that facilitates or suppresses behavioral modules on the basis of striatal responsibility signals. Our modeling functionally unites the modular compartmental organization of the striatum with the direct–indirect pathway divisions of the basal ganglia, a step that we suggest will have important clinical implications.en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (NIH R01 MH060379)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (NIH R01 NS025529)en_US
dc.description.sponsorshipUnited States. Office of Naval Research (grant ONR N00014-07-1-0903)en_US
dc.description.sponsorshipEuropean Union (Grant 201716)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (NIH Grant 5 R01 MH079076)en_US
dc.language.isoen_US
dc.publisherFrontiers Media S.A.en_US
dc.relation.isversionofhttp://dx.doi.org/10.3389/fnhum.2011.00047en_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.sourceFrontiersen_US
dc.titleShifting responsibly: the importance of striatal modularity to reinforcement learning in uncertain environmentsen_US
dc.typeArticleen_US
dc.identifier.citationAmemori, Ken-ichi, Leif G. Gibb, and Ann M. Graybiel. “Shifting Responsibly: The Importance of Striatal Modularity to Reinforcement Learning in Uncertain Environments.” Frontiers in Human Neuroscience 5, Article 47 (2011).en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciencesen_US
dc.contributor.departmentMcGovern Institute for Brain Research at MITen_US
dc.contributor.approverGraybiel, Ann M.
dc.contributor.mitauthorGraybiel, Ann M.
dc.contributor.mitauthorAmemori, Ken-ichi
dc.contributor.mitauthorGibb, Leif G.
dc.relation.journalFrontiers in Human Neuroscienceen_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.orderedauthorsAmemori, Ken-ichi; Gibb, Leif G.; Graybiel, Ann M.en
dc.identifier.orcidhttps://orcid.org/0000-0002-4326-7720
mit.licensePUBLISHER_POLICYen_US
mit.metadata.statusComplete


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