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dc.contributor.authorMcNamee, Daniel
dc.contributor.authorWolpert, Daniel
dc.date.accessioned2018-10-03T04:45:33Z
dc.date.available2018-10-03T04:45:33Z
dc.date.issued2019-05-01
dc.identifier.issn2573-5144
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/283097
dc.description.abstractRationality principles such as optimal feedback control and Bayesian inference underpin a probabilistic framework that has accounted for a range of empirical phenomena in biological sensorimotor control. To facilitate the optimization of flexible and robust behaviors consistent with these theories, the ability to construct internal models of the motor system and environmental dynamics can be crucial. In the context of this theoretic formalism, we review the computational roles played by such internal models and the neural and behavioral evidence for their implementation in the brain.
dc.publisherAnnual Reviews
dc.titleInternal Models in Biological Control
dc.typeArticle
prism.endingPage364
prism.publicationNameAnnual Review of Control, Robotics, and Autonomous Systems
prism.startingPage339
prism.volume2
dc.identifier.doi10.17863/CAM.30459
dcterms.dateAccepted2018-07-03
rioxxterms.versionofrecord10.1146/annurev-control-060117-105206
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2018-07-03
dc.contributor.orcidMcNamee, Daniel [0000-0001-9928-4960]
dc.contributor.orcidWolpert, Daniel [0000-0003-2011-2790]
dc.identifier.eissn2573-5144
rioxxterms.typeJournal Article/Review
pubs.funder-project-idWellcome Trust (097803/Z/11/Z)
pubs.funder-project-idRoyal Society (RP120142)
pubs.funder-project-idWellcome Trust (110257/Z/15/Z)
cam.issuedOnline2018-07-20
cam.orpheus.success2020-09-30 Green allowed version: no_version_allowed
cam.orpheus.counter29
rioxxterms.freetoread.startdate2100-01-01


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