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Feedforward Architectures Driven by Inhibitory Interactions

Billeh, Yazan N. and Schaub, Michael T. (2018) Feedforward Architectures Driven by Inhibitory Interactions. Journal of Computational Neuroscience, 44 (1). pp. 63-74. ISSN 0929-5313 . https://resolver.caltech.edu/CaltechAUTHORS:20171120-074949795

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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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1007/s10827-017-0669-1DOIArticle
https://link.springer.com/article/10.1007%2Fs10827-017-0669-1PublisherArticle
http://rdcu.be/y2I3PublisherFree ReadCube access
https://arxiv.org/abs/1701.04905arXivDiscussion Paper
ORCID:
AuthorORCID
Billeh, Yazan N.0000-0001-5200-4992
Schaub, Michael T.0000-0003-2426-6404
Additional Information:© 2017 Springer Science+Business Media, LLC. Received: 22 January 2017; Revised: 10 October 2017; Accepted: 17 October 2017; First Online: 14 November 2017. We are thankful for discussions with Christof Koch, Costas Anastassiou, and Mauricio Barahona and comments from Jean-Charles Delvenne and Renaud Lambiotte. YNB wishes to thank the Allen Institute founders, P. G. Allen and J. Allen, for their vision, encouragement and support. Most of this work has been performed while MTS was at the Université catholique de Louvain. MTS acknowledges support from the ARC and the Belgium network DYSCO (Dynamical Systems, Control and Optimisation) and an F.S.R. fellowship of the Université catholique de Louvain. MTS has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 702410. Compliance with Ethical Standards. The authors declare that they have no conflict of interest.
Funders:
Funding AgencyGrant Number
Australian Research CouncilUNSPECIFIED
Belgium Network DYSCOUNSPECIFIED
Université catholique de LouvainUNSPECIFIED
Marie Curie Fellowship702410
Subject Keywords:Feedforward networks; inhibitory feed back; leaky-integrate-and-fire; Information propagation; neural networks
Issue or Number:1
Record Number:CaltechAUTHORS:20171120-074949795
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20171120-074949795
Official Citation:Billeh, Y.N. & Schaub, M.T. J Comput Neurosci (2018) 44: 63. https://doi.org/10.1007/s10827-017-0669-1
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:83327
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:20 Nov 2017 18:16
Last Modified:03 Oct 2019 19:05

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