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dc.contributor.authorBilleh, Yazan N.
dc.contributor.authorSchaub, Michael T
dc.date.accessioned2018-06-14T17:06:37Z
dc.date.available2018-09-02T05:00:05Z
dc.date.issued2017-11
dc.identifier.issn0929-5313
dc.identifier.issn1573-6873
dc.identifier.urihttp://hdl.handle.net/1721.1/116316
dc.description.abstractDirected information transmission is paramount for many social, physical, and biological systems. For neural systems, scientists have studied this problem under the paradigm of feedforward networks for decades. In most models of feedforward networks, activity is exclusively driven by excitatory neurons and the wiring patterns between them, while inhibitory neurons play only a stabilizing role for the network dynamics. Motivated by recent experimental discoveries of hippocampal circuitry, cortical circuitry, and the diversity of inhibitory neurons throughout the brain, here we illustrate that one can construct such networks even if the connectivity between the excitatory units in the system remains random. This is achieved by endowing inhibitory nodes with a more active role in the network. Our findings demonstrate that apparent feedforward activity can be caused by a much broader network-architectural basis than often assumed. Keywords: Feedforward networks, Inhibitory feedback, Leaky-integrate-and-fire Information propagation, Neural networksen_US
dc.description.sponsorshipUniversité catholique de Louvain (F.S.R. Fellowship)en_US
dc.description.sponsorshipHorizon 2020 Framework Programme (European Commission) (Marie Sklodowska-Curie Grant Agreement 702410)en_US
dc.description.sponsorshipWallonia-Brussels Federation. Actions de Recherche Concertée (ARC)en_US
dc.description.sponsorshipInteruniversity Attraction Poles Programme. Belgian Network DYSCO (Dynamical Systems, Control and Optimisation)en_US
dc.publisherSpringer USen_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/s10827-017-0669-1en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceSpringer USen_US
dc.titleFeedforward architectures driven by inhibitory interactionsen_US
dc.typeArticleen_US
dc.identifier.citationBilleh, Yazan N., and Michael T. Schaub. “Feedforward Architectures Driven by Inhibitory Interactions.” Journal of Computational Neuroscience, vol. 44, no. 1, Feb. 2018, pp. 63–74.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Data, Systems, and Societyen_US
dc.contributor.mitauthorSchaub, Michael T.
dc.relation.journalJournal of Computational Neuroscienceen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2018-01-16T05:24:35Z
dc.language.rfc3066en
dc.rights.holderSpringer Science+Business Media, LLC
dspace.orderedauthorsBilleh, Yazan N.; Schaub, Michael T.en_US
dspace.embargo.termsNen
mit.licensePUBLISHER_POLICYen_US


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