Collective dynamics of a neural network of excitable and inhibitory populations: oscillations, tristability, chaos

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Abstract

The purpose of this work is to study the collective dynamics of a neural network consisting of excitatory and inhibitory populations. The method of reducing the network dynamics to new generation neural mass models is used, and a bifurcation analysis of the model is carried out. As a result the conditions and mechanisms for the emergence of various modes of network collective activity are described, including collective oscillations, multistability of various types, and chaotic collective dynamics. Conclusion. The low-dimensional reduced model is an effective tool for studying the essential patterns of collective dynamics in large-scale neural networks. At the same time, the analysis also allows us to elicit more subtle effects, such as the emergence of synchrony clusters in the network and the shifting effect for the boundaries of the existence of dynamical modes.

About the authors

Sergej Yu. Kirillov

Institute of Applied Physics of the Russian Academy of Sciences

ORCID iD: 0000-0002-7731-7423
SPIN-code: 6266-8147
ul. Ul'yanova, 46, Nizhny Novgorod , 603950, Russia

Alexander Alekseevich Zlobin

Institute of Applied Physics of the Russian Academy of Sciences

ul. Ul'yanova, 46, Nizhny Novgorod , 603950, Russia

Vladimir Viktorovich Klinshov

Lobachevsky State University of Nizhny Novgorod

ORCID iD: 0000-0003-4733-1352
Scopus Author ID: 15520684700
ResearcherId: M-6226-2014
603950 Nizhny Novgorod, Gagarin Avenue, 23

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