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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

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

Texto completo
Autor(es):
Kinouchi, Osame [1] ; Brochini, Ludmila [2] ; Costa, Ariadne A. [3] ; Ferreira Campos, Joao Guilherme [4] ; Copelli, Mauro [4]
Número total de Autores: 5
Afiliação do(s) autor(es):
[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
Número total de Afiliações: 4
Tipo de documento: Artigo Científico
Fonte: SCIENTIFIC REPORTS; v. 9, MAR 7 2019.
Citações Web of Science: 3
Resumo

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)

Processo FAPESP: 16/00430-3 - Simulações computacionais de redes balanceadas com neurônios Integra-Dispara estocásticos
Beneficiário:Ariadne de Andrade Costa
Linha de fomento: Bolsas no Brasil - Pós-Doutorado
Processo FAPESP: 13/07699-0 - Centro de Pesquisa, Inovação e Difusão em Neuromatemática - NeuroMat
Beneficiário:Jefferson Antonio Galves
Linha de fomento: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
Processo FAPESP: 16/24676-1 - Árvores de contexto aplicadas à modelagem estatística de trens de disparo neuronais
Beneficiário:Ludmila Brochini Rodrigues
Linha de fomento: Bolsas no Brasil - Pós-Doutorado