Por volta de 1764 aparece um novo ramo da ciência - A teoria de controle - quando James Watt consertou uma máquina Newcomen e percebeu que essa era ineficiente, e criou um sistema de controle de velocidades. A evolução destes sistemas controladores pode ser observada no controle utilizando a equação de Riccati dependente de estados (SDRE). Apesar de ser uma técnica muito avançada em relação à capacidade de realizar o controle, alguns problemas precisam ser encarados quanto à sua utilização, como a necessidade de se ter recursos computacionais de alto nível e custo. Essas questões podem impedir o uso da técnica SDRE em alguns sistemas. Uma solução para este problema é apontada através do uso de uma rede neural (RNA) chamada de Rede Neural com Estados de Eco (ESN). As RNAs possuem arquiteturas baseadas em redes neurais biológicas para que tenhamos resultados desejados na saída. Para que essa saída seja satisfatória a rede neural passa por um processo de treinamento. Sendo assim, usase os dados de comportamento do SDRE para a realização do treinamento da ESN. Depois disso, realizam-se testes quanto à eficiência da rede neural no controle do sistema a ser controlado e ao custo computacional. Os resultados são comparados aos obtidos com o controle ESN. Este teste foi realizado para um sistema micro eletromecânico e o controle da suspensão ativa de um half-car. Os resultados obtidos foram positivos, pois a ESN conseguiu realizar o controle utilizando menos tempo de processamento em relação ao SDRE, além de possuir uma estrutura base fixa, possibilitando ajustes para realização de diferentes tipos de controle. / In around 1764, it emerged a new branch of science – the theory of control - when James Watt was given a model Newcomen engine to repair. He realised that it was hopelessly inefficient and began to work to improve the design. He did a velocit controller to solve the problem. The evolution of these systems is shown in State-dependent Riccati equation (SDRE) techniques. Although it is a very advanced technique in relation to the capacity of performing control, some problems have to be faced for its use, as the necessity of computational resources of high level and cost, which may impede the use of SDRE in some systems. The solution for these problems is pointed out in this study by the use of Echo State Neural Networks (ESNs). These neural networks have inputs and outputs and the inputs are processed through the use of algorithms in order to reach the desired results, and for that the neural network has to be under a task of training. After that we use the behavioral SDRE data for the training followed by the neural network efficiency test for the system control and for the computational cost. The results are compared to the ones obtained with the ESN control. This test was realized for a micro eletromechanical system and the control of the active suspension of a half-car. The results were positive as the ESN could perform the control in a short time in relation to the SDRE. There is also a fixed structure which makes possible some adjusts for different kinds of control.

This thesis describes the development of an optimized method for culturing dissociated, monolayer neuronal networks from murine frontal cortex and midbrain. It is presented as a guidebook for use by cell culture specialists and laboratory personnel who require updated and complete procedures for use with microelectrode array (MEA) recording technology. Specific cell culture protocols, contamination prevention and control, as well common problems encountered within the cell culture facility, are discussed. This volume offers value and utility to the rapidly expanding fields of MEA recording and neuronal cell culture. Due to increasing interest in determining the mechanisms underlying Parkinson's disease, the newly developed procedures for mesencephalon isolation and culture on MEAs are an important research contribution.

Orientadores: José Antônio Brum, Marcus Aloizio Martinez de Aguiar / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Física Gleb Wataghin / Made available in DSpace on 2018-08-29T15:40:55Z (GMT). No. of bitstreams: 1 Reis_ElohimFonsecados_M.pdf: 2277988 bytes, checksum: 08f2c3b84a391217d575c0f425159fca (MD5) Previous issue date: 2015 / Resumo: Este trabalho é dividido em duas partes. Na primeira parte, uma rede de correlação é construída baseada em um modelo de Ising em diferentes temperaturas, crítica, subcrítica e supercrítica, usando um algorítimo de Metropolis Monte-Carlo com dinâmica de \textit{single-spin-flip}. Este modelo teórico é comparado com uma rede do cérebro construída a partir de correlações das séries temporais do sinal BOLD de fMRI de regiões do cérebro. Medidas de rede, como coeficiente de aglomeração, mínimo caminho médio e distribuição de grau são analisadas. As mesmas medidas de rede são calculadas para a rede obtida pelas correlações das séries temporais dos spins no modelo de Ising. Os resultados da rede cerebral são melhor explicados pelo modelo teórico na temperatura crítica, sugerindo aspectos de criticalidade na dinâmica cerebral. Na segunda parte, é estudada a dinâmica temporal da atividade de um população neural, ou seja, a atividade de células ganglionares da retina gravadas em uma matriz de multi-eletrodos. Vários estudos têm focado em descrever a atividade de redes neurais usando modelos de Ising com desordem, não dando atenção à estrutura dinâmica. Tratando o tempo como uma dimensão extra do sistema, a dinâmica temporal da atividade da população neural é modelada. O princípio de máxima entropia é usado para construir um modelo de Ising com interação entre pares das atividades de diferentes neurônios em tempos diferentes. O ajuste do modelo é feito com uma combinação de amostragem de Monte-Carlo e método do gradiente descendente. O sistema é caracterizado pelos parâmetros aprendidos, questões como balanço detalhado e reversibilidade temporal são analisadas e variáveis termodinâmicas, como o calor específico, podem ser calculadas para estudar aspectos de criticalidade / Abstract: This work is divided in two parts. In the first part, a correlation network is build based on an Ising model at different temperatures, critical, subcritical and supercritical, using a Metropolis Monte-Carlo algorithm with single-spin-flip dynamics. This theoretical model is compared with a brain network built from the correlations of BOLD fMRI temporal series of brain regions activity. Network measures, such as clustering coefficient, average shortest path length and degree distributions are analysed. The same network measures are calculated to the network obtained from the time series correlations of the spins in the Ising model. The results from the brain network are better explained by the theoretical model at the critical temperature, suggesting critical aspects in the brain dynamics. In the second part, the temporal dynamics of the activity of a neuron population, that is, the activity of retinal ganglion cells recorded in a multi-electrode array was studied. Many studies have focused on describing the activity of neural networks using disordered Ising models, with no regard to the dynamic nature. Treating time as an extra dimension of the system, the temporal dynamics of the activity of the neuron population is modeled. The maximum entropy principle approach is used to build an Ising model with pairwise interactions between the activities of different neurons at different times. Model fitting is performed by a combination of Metropolis Monte Carlo sampling with gradient descent methods. The system is characterized by the learned parameters, questions like detailed balance and time reversibility are analysed and thermodynamic variables, such as specific heat, can be calculated to study critical aspects / Mestrado / Física / Mestre em Física / 2013/25361-6 / FAPESP

The interest in complex systems has increased exponentially during the past years because it was found helpful in addressing many of today's challenges. The study of the brain, biology, earthquakes, markets and social sciences are only a few examples of the fields that have benefited from the investigation of complex systems. Internet, the increased mobility of people and the raising energy demand are among the factors that brought in contact complex systems that were isolated till a few years ago. A theory for the interaction between complex systems is becoming more and more urgent to help mankind in this transition. The present work builds upon the most recent results in this field by solving a theoretical problem that prevented previous work to be applied to important complex systems, like the brain. It also shows preliminary laboratory results of perturbation of in vitro neural networks that were done to test the theory. Finally, it gives a preview of the studies that are being done to create a theory that is even closer to the interaction between real complex systems.

Superbursts (SBs) are large, seemingly spontaneous activity fluctuations often encountered in high density neural networks in vitro. Little effort has been put forth to define and analyze SBs which are paroxysmal bursting discharges. Through qualitative and quantitative means, I have described specific occurrences of superbursting activity. A complex of paroxysmal bursting has been termed a "superburst episode," and each individual SB is a "superburst event" which is comprises a fine burst structure. Quantitative calculations (employing overall spike summations and coefficient of variation (CV) calculations), reveal three distinct phases. Phase 1 is a "build up" phase of increasingly strong, coordinated bursting with an average of a 17.6% ± 13.7 increase in activity from reference. Phase 2, the "paroxysmal" phase, is comprised of massive coordinated bursting with high frequency spike content. Individual spike activity increases by 52.9% ± 14.6. Phase 3 is a "recovery phase" of lower coordination and an average of a 50.1% ± 35.6 decrease in spike production from reference. SBs can be induced and terminated by physical manipulation of the medium. Using a peristaltic pump with a flow rate of 0.4ml/min, superbursting activity ceases approximately 28.3 min after the introduction of flow. Alternatively, upon cessation of medium flow superbursting activity reemerges after approximately 8.5 min. Additionally, this study explored other methods capable of inducing superbursting activity using osmotic shocks. The induction and termination of SBs demonstrates that the cell culture environment plays a major role in generating this phenomenon. The observations that high density multi-layer neuronal networks in culture are more likely to enter paroxysmal bursting also supports the hypothesis that enrichment and depletion layers of metabolites and ionic species are involved in such unusual activity. The dynamic similarity of the SB phenomenon with epileptiform discharges make further quantification on the spike pattern level pertinent and important.

No description available.

Muscarinic acetylcholine receptors (mAChRs) mediate effects of acetylcholine (ACh) in many systems, including those involved in locomotion. In the stage 37/38 Xenopus laevis tadpole, a well-understood model system of vertebrate locomotion, mAChRs have been found to be located on motor neurons with evidence suggesting that mAChRs are involved in swimming behaviour. The current study aimed to further investigate the role of mAChR-mediated cholinergic transmission by employing extracellular and whole-cell patch clamp recordings to examine the effects of mAChR activation on the properties of different types of neurons in the Xenopus laevis tadpole swimming circuit. It was found that mAChR activation can increase the threshold for initiating swimming by skin stimulation and can lead to the generation of spontaneous motor output in the absence of physical stimuli. These effects were found to be a result of direct inhibition of dorsolateral sensory interneurons of the mechanosensory pathway, direct inhibition of glycinergic inhibitory interneurons in the CPG and a decrease in CPG neuron firing reliability during swimming. The data presented here comprise the first whole-cell patch-clamp investigation into mAChR-mediated cholinergic transmission in the Xenopus laevis tadpole swimming circuit and provide novel evidence that mAChRs modulate the properties of mechanosensory pathway and CPG neurons in this model system of vertebrate locomotion.

Ma Ting. / "July 2004." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (p. 181-196). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

An Active Microscaffold System with Fluid Delivery and Stimulation/Recording Functionalities for Culturing 3-D Neuronal Networks Laura Elizabeth Rowe 215 Pages Directed by Dr. A. Bruno Frazier An active microscaffold system with fluid delivery and electrical stimulation/recording functionalities for 3-D neuronal culture studies is presented. The microscaffolds presented in this dissertation consist of an array of microfabricated towers with integrated microfluidic channels, fluid ports, and electrodes. The microfluidic channels and ports allow for perfusion of nutrients, gas exchange, and biochemical control of the extracellular environment throughout the 3-D culture, while the electrodes allow for active stimulation/recording of the 3-D neuronal network. In essence, the microscaffold serves as an artificial circulatory system to enable 3-D in vitro growth and proliferation of re-aggregate neuronal cell cultures. Increased cell survival on microscaffolds with nutrient perfusion at 14 and 21 days in vitro (DIV) is presented. Additionally, the microtower scaffold is built upon a substrate that is compatible with the Multi Channel Systems preamplifier setup to enable electrical stimulation/recording of the cultured network in a 3-D mutilelectrode array (MEA) environment. Impedance measurements on the functioning microtower electrodes were obtained. The overall goal of this research was to develop a BioMEMS technology to provide neuroscientists with a better investigative tool for studying 3-D in vitro neuronal networks than is currently available.

Rhythmic neural activity is thought to underlie many high-level functions of the nervous system. Our goals are to understand rhythmic activity starting with small networks, using theoretical and experimental tools. Phase resetting theory describes essential properties that cause and destroy rhythms. We validate and extend one branch of this theory, testing it in bursting neurons coupled by excitation and then extending the theory to account for temporal variability found in our experimental data. We show that the theory makes good predictions of rhythmic activity in heterogeneous networks. We also note differences in mathematical structure between inhibition- and excitation-coupling that cause them to behave differently in noisy contexts and may explain why all central pattern generators (CPGs) found in nature are dominated by inhibition. Our extension of the theory gives a method that is useful to compare experimental and model data and shows that noise may either create or destroy a rhythm. Finally, we described the cellular mechanisms in Aplysia that switch the feeding CPG from arrhythmic to rhythmic behavior in response to reward stimuli. Previous studies showed that a Dopamine reward signal is correlated to changes in electrical coupling and excitability in several important neurons in the CPG. Using the dynamic clamp and an in vitro analog of the full behavioral system, we were able to determine that electrical coupling alone controls rhythmicity, while excitability independently controls the rate of activity. These results beg for further study, including new theory to explain them fully.