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dc.contributor.authorUjfalussy, Balázs B
dc.contributor.authorMakara, Judit K
dc.contributor.authorLengyel, Mate
dc.contributor.authorBranco, Tiago
dc.date.accessioned2018-12-13T00:32:18Z
dc.date.available2018-12-13T00:32:18Z
dc.date.issued2018-11-07
dc.identifier.issn0896-6273
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/286832
dc.description.abstractDendrites integrate inputs nonlinearly, but it is unclear how these nonlinearities contribute to the overall input-output transformation of single neurons. We developed statistically principled methods using a hierarchical cascade of linear-nonlinear subunits (hLN) to model the dynamically evolving somatic response of neurons receiving complex, in vivo-like spatiotemporal synaptic input patterns. We used the hLN to predict the somatic membrane potential of an in vivo-validated detailed biophysical model of a L2/3 pyramidal cell. Linear input integration with a single global dendritic nonlinearity achieved above 90% prediction accuracy. A novel hLN motif, input multiplexing into parallel processing channels, could improve predictions as much as conventionally used additional layers of local nonlinearities. We obtained similar results in two other cell types. This approach provides a data-driven characterization of a key component of cortical circuit computations: the input-output transformation of neurons during in vivo-like conditions.
dc.format.mediumPrint
dc.languageeng
dc.publisherElsevier BV
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectNerve Net
dc.subjectDendrites
dc.subjectAnimals
dc.subjectHumans
dc.subjectLinear Models
dc.subjectMembrane Potentials
dc.subjectModels, Neurological
dc.titleGlobal and Multiplexed Dendritic Computations under In Vivo-like Conditions.
dc.typeArticle
prism.endingPage592.e5
prism.issueIdentifier3
prism.publicationDate2018
prism.publicationNameNeuron
prism.startingPage579
prism.volume100
dc.identifier.doi10.17863/CAM.34139
dcterms.dateAccepted2018-08-21
rioxxterms.versionofrecord10.1016/j.neuron.2018.08.032
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2018-11
dc.contributor.orcidLengyel, Mate [0000-0001-7266-0049]
dc.identifier.eissn1097-4199
rioxxterms.typeJournal Article/Review
pubs.funder-project-idWellcome Trust (095621/Z/11/Z)
cam.issuedOnline2018-11-07


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International