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dc.contributor.authorSteuber, Volker
dc.contributor.authorSchultheiss, N.W.
dc.contributor.authorSilver, R.A.
dc.contributor.authorDe Schutter, E.
dc.contributor.authorJaeger, D.
dc.date.accessioned2013-02-04T15:00:07Z
dc.date.available2013-02-04T15:00:07Z
dc.date.issued2011
dc.identifier.citationSteuber , V , Schultheiss , N W , Silver , R A , De Schutter , E & Jaeger , D 2011 , ' Determinants of synaptic integration and heterogeneity in rebound firing explored with data-driven models of deep cerebellar nucleus cells ' , Journal of Computational Neuroscience , vol. 30 , no. 3 , pp. 633-658 . https://doi.org/10.1007/s10827-010-0282-z
dc.identifier.issn0929-5313
dc.identifier.otherPURE: 86990
dc.identifier.otherPURE UUID: 1ae86904-411c-4de4-a8d3-e7e54d1b0dd8
dc.identifier.otherdspace: 2299/6018
dc.identifier.otherScopus: 79960094606
dc.identifier.urihttp://hdl.handle.net/2299/9831
dc.description“The original publication is available at www.springerlink.com” This is an open access article following payment of an open access publication fee.
dc.description.abstractSignificant inroads have been made to understand cerebellar cortical processing but neural coding at the output stage of the cerebellum in the deep cerebellar nuclei (DCN) remains poorly understood. The DCN are unlikely to just present a relay nucleus because Purkinje cell inhibition has to be turned into an excitatory output signal, and DCN neurons exhibit complex intrinsic properties. In particular, DCN neurons exhibit a range of rebound spiking properties following hyperpolarizing current injection, raising the question how this could contribute to signal processing in behaving animals. Computer modeling presents an ideal tool to investigate how intrinsic voltage-gated conductances in DCN neurons could generate the heterogeneous firing behavior observed, and what input conditions could result in rebound responses. To enable such an investigation we built a compartmental DCN neuron model with a full dendritic morphology and appropriate active conductances. We generated a good match of our simulations with DCN current clamp data we recorded in acute slices, including the heterogeneity in the rebound responses. We then examined how inhibitory and excitatory synaptic input interacted with these intrinsic conductances to control DCN firing. We found that the output spiking of the model reflected the ongoing balance of excitatory and inhibitory input rates and that changing the level of inhibition performed an additive operation. Rebound firing following strong Purkinje cell input bursts was also possible, but only if the chloride reversal potential was more negative than −70 mV to allow de-inactivation of rebound currents. Fast rebound bursts due to T-type calcium current and slow rebounds due to persistent sodium current could be differentially regulated by synaptic input, and the pattern of these rebounds was further influenced by HCN current. Our findings suggest that active properties of DCN neurons could play a crucial role for signal processing in the cerebellum.en
dc.language.isoeng
dc.relation.ispartofJournal of Computational Neuroscience
dc.rightsOpen
dc.subjectcerebellum
dc.subjection channel
dc.subjectexcitability
dc.subjectsimulation
dc.subjectinhibition
dc.subjectlearning
dc.titleDeterminants of synaptic integration and heterogeneity in rebound firing explored with data-driven models of deep cerebellar nucleus cellsen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
dc.description.statusPeer reviewed
dc.description.versiontypeFinal Published version
dcterms.dateAccepted2011
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.1007/s10827-010-0282-z
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue
herts.rights.accesstypeOpen


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