The effects of inhibitory neuron fraction on the dynamics of an avalanching neural network

The statistical analysis of the collective neural activity known as avalanches provides insight into the proper behavior of brains across many species. In this talk we present 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 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 regime of low inhibitory fraction to protect itself from the dominating avalanches present in these extended distributions.