Sincronização em Redes Neurais

• Ano de defesa: 2012
• Autor(a) principal: Batista, Carlos Adalberto Schnaider
• Orientador(a): Batista, Antonio Marcos
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: UNIVERSIDADE ESTADUAL DE PONTA GROSSA
• Programa de Pós-Graduação: Programa de Pós-Graduação em Ciências
• Departamento: Fisica
• País: BR
• Palavras-chave em Português: redes; neurônios; caos
• Palavras-chave em Inglês: network; neurons; Chaos
• Área do conhecimento CNPq: CNPQ::CIENCIAS EXATAS E DA TERRA::FISICA
• Link de acesso: http://tede2.uepg.br/jspui/handle/prefix/892

We consider a network of coupled maps that displays a property of scale-free network. The connectivity of each site satisfies a power law distribution, where most sites have few connections and the minority dominating the connectivity with many connections. Experimental evidence show that a biological neural network in some areas of the brain have a similar architecture to a scale-free network. On the other hand, a consistent response of a set of neurons can be described by the phase synchronization of chaotic activity of your bursts. We use the model having a dynamic Rulkov comparable to the neural firing to investigate the synchronization of chaotic phase, in particular its dependence on the coupling properties. We conducted this study inspired by research on diseases such as tremors and Parkinson’s disease, which in dynamic terms are related to synchronized behavior. In this thesis we present two forms well-known in the literature on how to deal with the problem by applying numerical methods: applying an external signal with certain amplitude and frequency, and a feedback signal extracted from the network itself with a certain delay time in the system to suppress or control the rhythms synchronized. We find a satisfactory regions of control and suppression of unwanted neural rhythms according to the variation of system parameters. Finally, we show some results concerning the effect of synchronization according to the interaction between a group of networks varying the probability of connections between networks and within each network.