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dc.contributor.authorPhilips, Ryan T.
dc.contributor.authorSur, Mriganka
dc.contributor.authorChakravarthy, V. Srinivasa
dc.date.accessioned2018-01-23T16:27:48Z
dc.date.available2018-01-23T16:27:48Z
dc.date.issued2017-10
dc.date.submitted2017-03
dc.identifier.issn1553-7358
dc.identifier.issn1553-734X
dc.identifier.urihttp://hdl.handle.net/1721.1/113276
dc.description.abstractOrientation preference maps (OPMs) are present in carnivores (such as cats and ferrets) and primates but are absent in rodents. In this study we investigate the possible link between astrocyte arbors and presence of OPMs. We simulate the development of orientation maps with varying hypercolumn widths using a variant of the Laterally Interconnected Synergetically Self-Organizing Map (LISSOM) model, the Gain Control Adaptive Laterally connected (GCAL) model, with an additional layer simulating astrocytic activation. The synaptic activity of V1 neurons is given as input to the astrocyte layer. The activity of this astrocyte layer is now used to modulate bidirectional plasticity of lateral excitatory connections in the V1 layer. By simply varying the radius of the astrocytes, the extent of lateral excitatory neuronal connections can be manipulated. An increase in the radius of lateral excitatory connections subsequently increases the size of a single hypercolumn in the OPM. When these lateral excitatory connections become small enough the OPM disappears and a salt-and-pepper organization emerges.en_US
dc.publisherPublic Library of Scienceen_US
dc.relation.isversionofhttp://dx.doi.org/10.1371/journal.pcbi.1005785en_US
dc.rightsCreative Commons Attribution 4.0 International Licenseen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0en_US
dc.sourcePLoSen_US
dc.titleThe influence of astrocytes on the width of orientation hypercolumns in visual cortex: A computational perspectiveen_US
dc.typeArticleen_US
dc.identifier.citationPhilips, Ryan T. et al. “The Influence of Astrocytes on the Width of Orientation Hypercolumns in Visual Cortex: A Computational Perspective.” Edited by Bard Ermentrout. PLOS Computational Biology 13, 10 (October 2017): e1005785 © 2017 Philips et alen_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.mitauthorSur, Mriganka
dc.relation.journalPLOS Computational Biologyen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2018-01-19T15:21:01Z
dspace.orderedauthorsPhilips, Ryan T.; Sur, Mriganka; Chakravarthy, V. Srinivasaen_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0003-2442-5671
mit.licensePUBLISHER_CCen_US


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