The effects of inhibitory and excitatory neurons on the dynamics and control of avalanching neural networks

TL;DR

This paper investigates how the balance of inhibitory and excitatory neurons influences neural avalanche dynamics, showing that adjusting this balance can prevent epileptic-like activity and proposing control strategies to manage avalanche distributions.

Contribution

It introduces a neural network model demonstrating how inhibitory neuron fraction affects avalanche behavior and proposes targeted neuron disabling as a control method.

Findings

Increasing inhibitory neurons reduces avalanche size and duration.

Targeted disabling of excitatory neurons effectively curtails avalanche activity.

The model suggests brain operates away from epileptic regimes by maintaining low inhibitory fractions.

Abstract

The statistical analysis of the collective neural activity known as avalanches provides insight into the proper behavior of brains across many species. We consider a neural network model based on the work of Lombardi, Herrmann, De Arcangelis et al. that captures the relevant dynamics of neural avalanches, and we show how tuning the fraction of inhibitory neurons in this model alters the connectivity of the network over time, removes exponential cut-offs present in the distributions of avalanche strength and duration, and transitions the power spectral density of the network into an `epileptic' regime. We propose that the brain operates away from this power law regime of low inhibitory fraction to protect itself from the dominating avalanches present in these extended distributions. We present control strategies that curtail these power law distributions through either random or, more effectively, targeted disabling of excitatory neurons.