Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/26009
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dc.contributor.authorMcDonnell, Mark Den_UK
dc.contributor.authorGraham, Bruceen_UK
dc.date.accessioned2017-10-23T22:46:09Z-
dc.date.available2017-10-23T22:46:09Z-
dc.date.issued2017-09-22en_UK
dc.identifier.othere1005634en_UK
dc.identifier.urihttp://hdl.handle.net/1893/26009-
dc.description.abstractIn the brain, the postsynaptic response of a neuron to time-varying inputs is determined by the interaction of presynaptic spike times with the short-term dynamics of each synapse. For a neuron driven by stochastic synapses, synaptic depression results in a quite different postsynaptic response to a large population input depending on how correlated in time the spikes across individual synapses are. Here we show using both simulations and mathematical analysis that not only the rate but the phase of the postsynaptic response to a rhythmic population input varies as a function of synaptic dynamics and synaptic configuration. Resultant phase leads may compensate for transmission delays and be predictive of rhythmic changes. This could be particularly important for sensory processing and motor rhythm generation in the nervous system. © 2017 McDonnell, Graham.en_UK
dc.language.isoenen_UK
dc.publisherPublic Library of Scienceen_UK
dc.relationMcDonnell MD & Graham B (2017) Phase changes in neuronal postsynaptic spiking due to short term plasticity. PLoS Computational Biology, 13 (9), Art. No.: e1005634. https://doi.org/10.1371/journal.pcbi.1005634en_UK
dc.rights© 2017 McDonnell, Graham. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en_UK
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_UK
dc.titlePhase changes in neuronal postsynaptic spiking due to short term plasticityen_UK
dc.typeJournal Articleen_UK
dc.identifier.doi10.1371/journal.pcbi.1005634en_UK
dc.identifier.pmid28937977en_UK
dc.citation.jtitlePLoS Computational Biologyen_UK
dc.citation.issn1553-7358en_UK
dc.citation.issn1553-734Xen_UK
dc.citation.volume13en_UK
dc.citation.issue9en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.contributor.funderBiotechnology and Biological Sciences Research Councilen_UK
dc.citation.date22/09/2017en_UK
dc.contributor.affiliationUniversity of South Australiaen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.identifier.isiWOS:000411981000005en_UK
dc.identifier.scopusid2-s2.0-85030484964en_UK
dc.identifier.wtid516224en_UK
dc.contributor.orcid0000-0002-3243-2532en_UK
dc.date.accepted2017-06-08en_UK
dcterms.dateAccepted2017-06-08en_UK
dc.date.filedepositdate2017-10-19en_UK
dc.relation.funderprojectBalancing resource and energy usage for optimal performance in neural systemen_UK
dc.relation.funderrefBB/K01854X/1en_UK
rioxxterms.apcpaiden_UK
rioxxterms.typeJournal Article/Reviewen_UK
rioxxterms.versionVoRen_UK
local.rioxx.authorMcDonnell, Mark D|en_UK
local.rioxx.authorGraham, Bruce|0000-0002-3243-2532en_UK
local.rioxx.projectBB/K01854X/1|Biotechnology and Biological Sciences Research Council|http://dx.doi.org/10.13039/501100000268en_UK
local.rioxx.freetoreaddate2017-10-19en_UK
local.rioxx.licencehttp://creativecommons.org/licenses/by/4.0/|2017-10-19|en_UK
local.rioxx.filenamejournal.pcbi.1005634.pdfen_UK
local.rioxx.filecount1en_UK
local.rioxx.source1553-734Xen_UK
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