dc.contributor.author | Billeh, Yazan N. | |
dc.contributor.author | Schaub, Michael T | |
dc.date.accessioned | 2018-06-14T17:06:37Z | |
dc.date.available | 2018-09-02T05:00:05Z | |
dc.date.issued | 2017-11 | |
dc.identifier.issn | 0929-5313 | |
dc.identifier.issn | 1573-6873 | |
dc.identifier.uri | http://hdl.handle.net/1721.1/116316 | |
dc.description.abstract | Directed 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 networks | en_US |
dc.description.sponsorship | Université catholique de Louvain (F.S.R. Fellowship) | en_US |
dc.description.sponsorship | Horizon 2020 Framework Programme (European Commission) (Marie Sklodowska-Curie Grant Agreement 702410) | en_US |
dc.description.sponsorship | Wallonia-Brussels Federation. Actions de Recherche Concertée (ARC) | en_US |
dc.description.sponsorship | Interuniversity Attraction Poles Programme. Belgian Network DYSCO (Dynamical Systems, Control and Optimisation) | en_US |
dc.publisher | Springer US | en_US |
dc.relation.isversionof | http://dx.doi.org/10.1007/s10827-017-0669-1 | en_US |
dc.rights | Article 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.source | Springer US | en_US |
dc.title | Feedforward architectures driven by inhibitory interactions | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Billeh, 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.department | Massachusetts Institute of Technology. Institute for Data, Systems, and Society | en_US |
dc.contributor.mitauthor | Schaub, Michael T. | |
dc.relation.journal | Journal of Computational Neuroscience | en_US |
dc.eprint.version | Author's final manuscript | en_US |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
dc.date.updated | 2018-01-16T05:24:35Z | |
dc.language.rfc3066 | en | |
dc.rights.holder | Springer Science+Business Media, LLC | |
dspace.orderedauthors | Billeh, Yazan N.; Schaub, Michael T. | en_US |
dspace.embargo.terms | N | en |
mit.license | PUBLISHER_POLICY | en_US |