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

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.

Keywords

• neural networks
• collective dynamics
• mean-field theory
• neural mass models