# Stochastic oscillations produce dragon king avalanches in self-organized quasi-critical systems

## TL;DR

This paper analyzes how stochastic oscillations in self-organized quasi-critical systems can produce power-law avalanches and rare dragon king events, explaining neuronal activity patterns that differ from standard self-organized criticality models.

## Contribution

It provides a linear stability analysis of SOqC systems, revealing how demographic noise near a stable focus leads to diverse neuronal avalanches and dragon king events.

## Key findings

- Stability analysis shows fixed points are stable foci losing stability at criticality.
- Demographic noise near the focus induces stochastic oscillations that can produce avalanches.
- Power law avalanches and dragon king events coexist due to noise-induced oscillations.

## Abstract

In the last decade, several models with network adaptive mechanisms (link deletion-creation, dynamic synapses, dynamic gains) have been proposed as examples of self-organized criticality (SOC) to explain neuronal avalanches. However, all these systems present stochastic oscillations hovering around the critical region that are incompatible with standard SOC. This phenomenology has been called self-organized quasi-criticality (SOqC). Here we make a linear stability analysis of the mean field fixed points of two SOqC systems: a fully connected network of discrete time stochastic spiking neurons with firing rate adaptation produced by dynamic neuronal gains and an excitable cellular automata with depressing synapses. We find that the fixed point corresponds to a stable focus that loses stability at criticality. We argue that when this focus is close to become indifferent, demographic noise can elicit stochastic oscillations that frequently fall into the absorbing state. This mechanism interrupts the oscillations, producing both power law avalanches and dragon king events, which appear as bands of synchronized firings in raster plots. Our approach differs from standard SOC models in that it predicts the coexistence of these different types of neuronal activity.