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

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. arXiv:1809.01646v2 [cond-mat.dis-nn]

Using compartmental models to understand excitation-inhibition imbalance in epilepsy

“…For instance, a two-level Kuramoto framework has been used to distinguish between the brain networks constructed from the EEG of healthy controls and patients with epilepsy [18]. In another instance, a network model consisting of excitatory and inhibitory neurons has been applied to study the avalanche effect in neural networks and the factors that lead the network into an epileptic regime [19]. A simple phenomenological model based on codimension-one bifurcations has been used to classify epileptic seizures into several dynamotypes based on the signatures of critical transitions occurring at the seizure onset and offset [20].…”