# Self-Organized Supercriticality and Oscillations in Networks of Stochastic Spiking Neurons

## TL;DR

This paper investigates networks of stochastic spiking neurons, demonstrating self-organized supercriticality with power-law avalanches and oscillations, relevant to understanding brain rhythms and phenomena like epilepsy.

## Contribution

It introduces the concept of self-organized supercriticality in neuronal networks with dynamic gains, showing robustness and coexistence of avalanches and oscillations.

## Key findings

- Networks reach a supercritical state with power-law avalanches.
- Neuronal gains induce collective oscillations matching brain rhythms.
- Self-organized supercriticality is robust to parameter changes.

## Abstract

Networks of stochastic spiking neurons are interesting models in the area of Theoretical Neuroscience, presenting both continuous and discontinuous phase transitions. Here we study fully connected networks analytically, numerically and by computational simulations. The neurons have dynamic gains that enable the network to converge to a stationary slightly supercritical state (self-organized supercriticality or SOSC) in the presence of the continuous transition. We show that SOSC, which presents power laws for neuronal avalanches plus some large events, is robust as a function of the main parameter of the neuronal gain dynamics. We discuss the possible applications of the idea of SOSC to biological phenomena like epilepsy and dragon king avalanches. We also find that neuronal gains can produce collective oscillations that coexists with neuronal avalanches, with frequencies compatible with characteristic brain rhythms.