Fractal analyses of networks of integrate-and-fire stochastic spiking neurons

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 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.Comment: 11 pages, 3 subfigures divided into 2 figure

Phase transitions and self-organized criticality in networks of stochastic spiking neurons

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Phase transitions and critical behavior are crucial issues both in theoretical and experimental neuroscience. We report analytic and computational results about phase transitions and self-organized criticality (SOC) in networks with general stochastic neurons. The stochastic neuron has a firing probability given by a smooth monotonic function Phi(V) of the membrane potential V, rather than a sharp firing threshold. We find that such networks can operate in several dynamic regimes (phases) depending on the average synaptic weight and the shape of the firing function F. In particular, we encounter both continuous and discontinuous phase transitions to absorbing states. At the continuous transition critical boundary, neuronal avalanches occur whose distributions of size and duration are given by power laws, as observed in biological neural networks. We also propose and test a new mechanism to produce SOC: the use of dynamic neuronal gains - a form of short-term plasticity probably located at the axon initial segment (AIS) - instead of depressing synapses at the dendrites (as previously studied in the literature). The new self-organization mechanism produces a slightly supercritical state, that we called SOSC, in accord to some intuitions of Alan Turing.Phase transitions and critical behavior are crucial issues both in theoretical and experimental neuroscience. We report analytic and computational results about phase transitions and self-organized criticality (SOC) in networks with general stochastic neu6FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)2013/07699-0; 2016/00430-3165828/2015-3; 310706/2015-7; 306251/2014-

Deaths by tuberculosis in a priority city for disease control in the Brazilian Northeast: sociodemographic-operational characteristics and vulnerable territories.

Introduction: Incorporating spatial approaches into epidemiological research is a challenge in public health research. The goal in this study was to analyze the spatial distribution of cases of deaths by tuberculosis in Imperatriz – MA (Brazil) and to characterize these events according to sociodemographic and operational characteristics. Methods: In this ecological study, all deaths from tuberculosis as the primary cause registered in the Mortality Information System from 2005 to 2014 were considered. The research variables were subject to descriptive analysis, point density analysis (Kernel Intensity Estimation) and area analysis. Results: Fifty cases of deaths by TB were identified, particularly the pulmonary clinical form. Male patients were predominant, with a median age of 59 years, mulatto race/color, single, who had finished secondary education. Most deaths happened at the hospital, with medical care before death and without autopsy. Most events happened at the hospital, with medical care delivery by an assistant physician and without autopsy. The point density revealed heterogeneity in the spatial distribution of the deaths, with rates of up to 2.33 deaths/km2. The area analysis by census sector presented age standardized mortality rates of 0.00 to 4.00 deaths/100,000 inhabitants-year. Conclusion: The results contributed to the knowledge on the spatial distribution of cases of deaths by Tuberculosis and their characteristics in the research scenario. The importance of space is highlighted as a methodological alternative to support the planning, monitoring and assessment of health actions, targeting interventions to the control of the disease in vulnerable territories. Keywords: Tuberculosis; Health Information Systems; Mortality; Spatial analysis

Self-organized quasi-criticality in neuronal avalanches

Experimentos têm revelado que redes de neurônios, tanto in vitro como in vivo, mantêm atividade descrita por avalanches e se organizam em um estado crítico no qual essas avalanches são distribuídas de acordo com leis de potência. Mostramos no presente trabalho que um modelo de rede de elementos excitáveis com sinapses dinâ- micas é capaz de exibir criticalidade auto-organizada para ampla região do espaço de parâmetros. Nossos resultados estão de acordo com outros estudos que indicam que a depressão sináptica de curto prazo constitui mecanismo suciente para produzir criticalidade em avalanches neuronais. No entanto, segundo diversos pesquisadores, embora o ajuste de parâmetros seja grosso para que haja criticalidade no modelo, é mais preciso dizer que o sistema não apresenta criticalidade auto-organizada genu ína, mas sim quasi-criticalidade auto-organizada, como os demais modelos não conservativos presentes na literatura.Experiments have shown that neuronal networks, both in vitro and in vivo, maintain activity described by avalanches and they are organized into a critical state in which these avalanches are distributed according to power laws. We have demonstrated that a model based on a network of excitable elements with dynamical synapses is able to exhibit self-organized criticality for a wide range of the parameter\'s space. Our results are consistent with other studies that suggest short-term synaptic depression is enough to produce criticality in neuronal avalanches. However, according to several researchers, in spite of the tuning to be gross to ensure that there is criticality in the model, it is more accurate do not say that the system presents genuine self-organized criticality, but self-organized quasi-criticality as the other non-conservative models in the literature

Investigation of rat\'s behavioral models by genetic algorithms

O labirinto em cruz elevado é um dos aparatos experimentais mais utilizados em avaliações neurobiológicas de ansiedade e defesa de ratos e camundongos. Estudamos aqui o uso de redes neurais artificiais otimizadas por algoritmos genéticos para investigar o comportamento de ratos nesse labirinto. Ao contrário dos demais modelos já propostos, a construção da trajetória do agente virtual independe de dados experimentais conhecidos a priori. Mostramos que, ao utilizar um agente desenvolvido a partir da otimização de uma função de avaliação inspirada no conflito de medo e ansiedade, o modelo pode simular inclusive o efeito causado pela introdução de drogas ansiolíticas e ansiogênicas em ratos (clordiazepóxido 5 mg/kg e semicarbazida 20, 40 e 80 mg/kg). Os resultados das simulações do agente virtual estão de acordo com dados experimentais, revelando que a exploração de braços abertos é reduzida em relação a dos braços fechados, especialmente sob inserção de drogas ansiogênicas, que intensificam o medo do animal. Drogas ansiolíticas, ao contrário, estimulam a exploração. Para finalizar, foi realizada uma investigação aprofundada das trajetórias e redes neurais artificiais dos melhores ratos controle virtuais (que simulam ratos sem efeito de drogas). Conforme sugerem os resultados, a função de avaliação proposta pode conter as características mais relevantes envolvidas no comportamento do rato no labirinto em cruz elevado.The elevated plus-maze is one of the most used experimental apparatus for neurobiological evaluations of anxiety and defense of rats and mice. We investigate here the use of artificial neural networks otimized by genetic algorithms to nvestigate the behavior of rats in this maze. Unlike other proposed models, the development of the virtual agent\'s trajectory is independent of prior known experimental data. We show that, when using a agent developed from the optimization of a function inspired by the anxiety and fear conflict, the model can even simulate the effect caused by the introduction of anxiolytic and axiogenic drugs in rats (chlordiazepoxide 5 mg/kg and semicarbazide 20, 40 and 80 mg/kg). The results of simulations of the virtual agent agree with experimental data, in which the exploration of open arms is reduced compared to the exploration of enclosed arms, especially under effects of anxiogenic drugs, which enhance the animal fear. Anxiolytic drugs, on the other hand, stimulate exploration. Finally, a detailed investigation of trajectories and artificial neural networks of the best virtual control rats (that simulate rats without drugs) was performed. As the results suggest, the proposed fitness function may contain the most relevant features involved in the behavior of rats in the elevated plus-maze

Correlations induced by depressing synapses in critically self-organized networks with quenched dynamics

In a recent work, mean-field analysis and computer simulations were employed to analyze critical selforganization in networks of excitable cellular automata where randomly chosen synapses in the network were depressed after each spike (the so-called annealed dynamics). Calculations agree with simulations of the annealed version, showing that the nominal branching ratio sigma converges to unity in the thermodynamic limit, as expected of a self-organized critical system. However, the question remains whether the same results apply to the biological case where only the synapses of firing neurons are depressed (the so-called quenched dynamics). We show that simulations of the quenched model yield significant deviations from sigma = 1 due to spatial correlations. However, the model is shown to be critical, as the largest eigenvalue of the synaptic matrix approaches unity in the thermodynamic limit, that is, lambda(c) = 1. We also study the finite size effects near the critical state as a function of the parameters of the synaptic dynamics954CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À CIÊNCIA E TECNOLOGIA DO ESTADO DE PERNAMBUCO - FACEPEFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPsem informaçãosem informaçãosem informação2016/00430-3; 2016/20945-8; 2013/07699-