The statistical analysis of the collective neural activity known as avalanches provides insight into the proper behavior of brains across many species. In this paper 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  .
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 .
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alters the connectivity of the network over time,
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. We present control strategies that curtail these power law distributions through either random or, more effectively, targeted disabling of excitatory neurons