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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Kinouchi, Osame [1] ; Brochini, Ludmila [2] ; Costa, Ariadne A. [3] ; Ferreira Campos, Joao Guilherme [4] ; Copelli, Mauro [4]
Total Authors: 5
[1] Univ Sao Paulo, Dept Fis FFCLRP, Ribeirao Preto, SP - Brazil
[2] Univ Sao Paulo, Inst Matemat & Estat, Sao Paulo, SP - Brazil
[3] Univ Fed Goias, Unidade Acad Especial Ciencias Exatas, Jatai, Go - Brazil
[4] Univ Fed Pernambuco, Dept Fis, Recife, PE - Brazil
Total Affiliations: 4
Document type: Journal article
Source: SCIENTIFIC REPORTS; v. 9, MAR 7 2019.
Web of Science Citations: 3

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. (AU)

FAPESP's process: 16/00430-3 - Computational simulations of stochastic integrate-and-fire neurons balanced networks
Grantee:Ariadne de Andrade Costa
Support type: Scholarships in Brazil - Post-Doctorate
FAPESP's process: 13/07699-0 - Research, Innovation and Dissemination Center for Neuromathematics - NeuroMat
Grantee:Jefferson Antonio Galves
Support type: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 16/24676-1 - Context trees applied to the statistical modeling of neural spike trains
Grantee:Ludmila Brochini Rodrigues
Support type: Scholarships in Brazil - Post-Doctorate