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Journal of Computational Neuroscience

Erschienen in:

14.11.2017

Feedforward architectures driven by inhibitory interactions

verfasst von: Yazan N. Billeh, Michael T. Schaub

Erschienen in: Journal of Computational Neuroscience | Ausgabe 1/2018

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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.

Vorheriger Artikel Modeling mesoscopic cortical dynamics using a mean-field model of conductance-based networks of adaptive exponential integrate-and-fire neurons

Nächster Artikel Variable synaptic strengths controls the firing rate distribution in feedforward neural networks

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Titel: Feedforward architectures driven by inhibitory interactions
verfasst von: Yazan N. Billeh
Michael T. Schaub
Publikationsdatum: 14.11.2017
Verlag: Springer US
Erschienen in: Journal of Computational Neuroscience / Ausgabe 1/2018
Print ISSN: 0929-5313
Elektronische ISSN: 1573-6873
DOI: https://doi.org/10.1007/s10827-017-0669-1

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