Tricritical behavior in a neural model with excitatory and inhibitory units

TL;DR

This paper investigates how adding inhibitory neurons to a neural network model induces a tricritical point, enhancing understanding of brain dynamics and criticality.

Contribution

It introduces a model showing the emergence of a tricritical point due to inhibitory neurons in highly connected networks.

Findings

Inhibitory neurons induce a tricritical point in the network dynamics.

The model aligns with experimental evidence on brain criticality.

Highly connected networks exhibit distinct phase transition behaviors.

Abstract

While the support for the relevance of critical dynamics to brain function is increasing, there is much less agreement on the exact nature of the advocated critical point. Thus, a considerable number of theoretical efforts are currently concentrated on which mechanisms and what type/s of transition can be exhibited by neuronal networks models. In that direction, the present work describes the effect of incorporating a fraction of inhibitory neurons on the collective dynamics. As we show, this results in the appearence of a tricritical point for highly connected networks and non-zero fraction of inhibitory neurons. We discuss the relation of the present results with relevant experimental evidence.