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

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