Fractal Analyses of Networks of Integrate-and-Fire Stochastic Spiking Neurons

Autor(es)

Costa, Ariadne A. ; Amon, Mary Jean ; Sporns, Olaf ; Favela, Luis H. ; Cornelius, S ; Coronges, K ; Goncalves, B ; Sinatra, R ; Vespignani, A

• Tipo de documento: Artigo Científico
• Fonte: COMPLEX NETWORKS IX; v. N/A, p. 11-pg., 2018-01-01.

Resumo

Although there is increasing evidence of criticality in the brain, the processes that guide neuronal networks to reach or maintain criticality remain unclear. The present research examines the role of neuronal gain plasticity in time-series of simulated neuronal networks composed of integrate-and-fire stochastic spiking neurons and the utility of fractal methods in assessing network criticality. Simulated time-series were derived from a network model of fully connected discrete-time stochastic excitable neurons. Monofractal and multifractal analyses were applied to neuronal gain time-series. Fractal scaling was greatest in networks with a mid-range of neuronal plasticity, versus extremely high or low levels of plasticity. Peak fractal scaling corresponded closely to additional indices of criticality, including average branching ratio. Networks exhibited multifractal structure, or multiple scaling relationships. Multifractal spectra around peak criticality exhibited elongated right tails, suggesting that the fractal structure is relatively insensitive to high-amplitude local fluctuations. Networks near critical states exhibited mid-range multifractal spectra width and tail length, which is consistent with the literature suggesting that networks poised at quasi-critical states must be stable enough to maintain organization but unstable enough to be adaptable. Lastly, fractal analyses may offer additional information about critical state dynamics of networks by indicating scales of influence as networks approach critical states. (AU)

• Processo FAPESP: 13/07699-0 - Centro de Pesquisa, Inovação e Difusão em Neuromatemática - NeuroMat
• Beneficiário: Oswaldo Baffa Filho
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 16/00430-3 - Simulações computacionais de redes balanceadas com neurônios Integra-Dispara estocásticos
• Beneficiário: Ariadne de Andrade Costa
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 16/20945-8 - Impacto da Topologia de Conexões na Dinâmica de Redes de Neurônios Integra-Dispara Estocásticos
• Beneficiário: Ariadne de Andrade Costa
• Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Pós-Doutorado