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

Entropy

Ludmila Brochini¹  Osame Kinouchi²  Ariadne A. Costa³ 

[1] Departamento de Estatística, Instituto de Matemática e Estatística (IME), Universidade de São Paulo, São Paulo-SP 05508-090, Brazil;Departamento de Física, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP), Universidade de São Paulo, Ribeirão Preto-SP 14040-901, Brazil;Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA;

关键词: self-organized criticality;    neuronal avalanche;    stochastic neuron;    spiking neuron;    neuron models;    neuronal networks;    power law;    supercriticality;   

DOI  :  10.3390/e19080399

来源: DOAJ

【 摘 要 】

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 (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 coexist with neuronal avalanches.

【 授权许可】

Unknown