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Sci Rep. 2019 Mar 07;9(1):3874. doi: 10.1038/s41598-019-40473-1.

Stochastic oscillations and dragon king avalanches in self-organized quasi-critical systems.

Scientific reports

Osame Kinouchi, Ludmila Brochini, Ariadne A Costa, João Guilherme Ferreira Campos, Mauro Copelli

Affiliations

  1. Universidade de São Paulo, Departamento de Física-FFCLRP, Ribeirão Preto, SP, Brazil. [email protected].
  2. Universidade de São Paulo, Instituto de Matemática e Estatística, São Paulo, SP, Brazil.
  3. Universidade Federal de Goiás, Unidade Acadêmica Especial de Ciências Exatas, Jataí, GO, Brazil.
  4. Universidade Federal de Pernambuco, Departamento de Física, Recife, PE, Brazil.
  5. Universidade de São Paulo, Departamento de Física-FFCLRP, Ribeirão Preto, SP, Brazil.

PMID: 30846773 PMCID: PMC6405991 DOI: 10.1038/s41598-019-40473-1

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. Here we make a linear stability analysis of the mean field fixed points of two self-organized quasi-critical 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.

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