Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2016-02-17T22:41:57Z No. of bitstreams: 1 VitorLopesDosSantos_TESE.pdf: 14542830 bytes, checksum: 6152cf87bb56d50e13c1279c3841c19e (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2016-02-19T22:43:00Z (GMT) No. of bitstreams: 1 VitorLopesDosSantos_TESE.pdf: 14542830 bytes, checksum: 6152cf87bb56d50e13c1279c3841c19e (MD5) / Made available in DSpace on 2016-02-19T22:43:00Z (GMT). No. of bitstreams: 1 VitorLopesDosSantos_TESE.pdf: 14542830 bytes, checksum: 6152cf87bb56d50e13c1279c3841c19e (MD5) Previous issue date: 2015-02-25 / Os recentes avan?os t?cnicos das duas ?ltimas d?cadas para o registro de sinais neuroeletrofisiol?gicos foram essenciais para que se testassem hip?teses h? muito propostas acerca de como c?lulas nervosas processam e armazenam informa??o. No entanto, ao permitir maior detalhamento dos dados coletados, as novas tecnologias levam inevitavelmente ao aumento de sua complexidade estat?stica e, consequentemente, ? necessidade de novas ferramentas matem?tico-computacionais para sua an?lise. Nesta tese, apresentamos novos m?todos para a an?lise de dois componentes fundamentais nas atuais teorias da codifica??o neural: (1) assembleias celulares, definidas pela co-ativa??o de subgrupos neuronais; e (2) o padr?o temporal de atividade de neur?nios individuais. Em rela??o a (1), desenvolvemos um m?todo baseado em an?lise de componentes independentes para identificar e rastrear padr?es de co-ativa??o significativos com alta resolu??o temporal. Superamos limita??es de m?todos anteriores, ao efetivamente isolar assembleias e abrir a possibilidade de analisar simultaneamente grandes popula??es neuronais. Em rela??o a (2), apresentamos uma nova t?cnica para a extra??o de padr?es de atividade em trens de disparo baseada na decomposi??o wavelet. Demonstramos, por meio de simula??es e de aplica??o a dados reais, que nossa ferramenta supera as mais utilizadas atualmente para decodificar respostas de neur?nios e estimar a informa??o de Shannon entre trens de disparos e est?mulos externos.

Submitted by Isaac Francisco de Souza Dias (isaac.souzadias@ufpe.br) on 2016-04-26T17:47:29Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) DISSERTACAO Jose Rodrigues de Oliveira Neto.pdf: 16621430 bytes, checksum: fb1803a2a724e072379eae9f12089387 (MD5) / Made available in DSpace on 2016-04-26T17:47:29Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) DISSERTACAO Jose Rodrigues de Oliveira Neto.pdf: 16621430 bytes, checksum: fb1803a2a724e072379eae9f12089387 (MD5) Previous issue date: 2015-07-28 / CNPQ / O presente trabalho descreve a investigação e desenvolvimento de soluções para excitação Redes Neurais Pulsadas de tempo real a partir de grandezas físicas transduzidas e sinais simulados. Para isso foi desenvolvido um hardware dedicado de baixo custo capaz de transformar dados em trens de spikes, que são processados por essas redes. O sistema visa converter sinais digitais em spikes de neurônios artificiais, que são pulsos de 1 ms de duração. O sistema ainda pode organizar neurônios que disparam conjuntamente, a fim de gerar os três códigos neurais mais importantes descritos na literatura da neurociência: codificação por taxa de disparos, codificação por populações e codificação temporal. São descritas ainda duas topologias de amostradores (samplers) que discretizam representações na forma de populações neurais, que devem ser processadas segundo a abordagem Computação por Assembleias Neurais. Uma das topologias recolhe amostras na forma de populações de neurônios ativos durante um período definido (codificação por população), enquanto a outra recolhe amostras baseada na diferença temporal entre spikes (codificação temporal). Os sinais resultantes da amostragem podem ser utilizados internamente na rede como representações discretas de informações. Os sinais amostrados podem ainda ser utilizados como entradas de circuitos de tomada de decisão, cuja descrição das características e simulações também é parte deste trabalho. / This work describes the research and development of solutions for excitement of real-time Spiking Neural Networks from transduced physical quantities and simulated signals. For this it developed a dedicated low cost hardware able to turn data into spike trains, which are processed by these networks. The systemaims to convert digital signals into spikes of artificial neurons, which are pulses of 1 ms. The system can even arrange neurons that fire together to generate the three most important neural codes described in the neuroscience literature: rate coding, populations coding and temporal coding. Two topologies of samplers are described; these topologies discretize representations in the form of neural populations that should be processed according to Neural Assembly Computing approach. One of these topologies collects samples as populations of neurons active during a defined period (population coding), while the other topology collects samples based on the time difference between spikes (temporal coding). The signals resulting from the sample can be used internally in the network as discrete representations of information. The sampled signals may also be used as inputs of decision-making circuits, the description of the characteristics and simulation of these circuits is also part of this work.

Made available in DSpace on 2014-12-17T14:55:49Z (GMT). No. of bitstreams: 1 VitorLS_DISSERT.pdf: 1833534 bytes, checksum: 72ebc7d9d8be6ba8ae53eaad106afa8d (MD5) Previous issue date: 2011-03-03 / Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico / Following the new tendency of interdisciplinarity of modern science, a new field called neuroengineering has come to light in the last decades. After 2000, scientific journals and conferences all around the world have been created on this theme. The present work comprises three different subareas related to neuroengineering and electrical engineering: neural stimulation; theoretical and computational neuroscience; and neuronal signal processing; as well as biomedical engineering. The research can be divided in three parts: (i) A new method of neuronal photostimulation was developed based on the use of caged compounds. Using the inhibitory neurotransmitter GABA caged by a ruthenium complex it was possible to block neuronal population activity using a laser pulse. The obtained results were evaluated by Wavelet analysis and tested by non-parametric statistics. (ii) A mathematical method was created to identify neuronal assemblies. Neuronal assemblies were proposed as the basis of learning by Donald Hebb remain the most accepted theory for neuronal representation of external stimuli. Using the Marcenko-Pastur law of eigenvalue distribution it was possible to detect neuronal assemblies and to compute their activity with high temporal resolution. The application of the method in real electrophysiological data revealed that neurons from the neocortex and hippocampus can be part of the same assembly, and that neurons can participate in multiple assemblies. (iii) A new method of automatic classification of heart beats was developed, which does not rely on a data base for training and is not specialized in specific pathologies. The method is based on Wavelet decomposition and normality measures of random variables. Throughout, the results presented in the three fields of knowledge represent qualification in neural and biomedical engineering / Following the new tendency of interdisciplinarity of modern science, a new field called neuroengineering has come to light in the last decades. After 2000, scientific journals and conferences all around the world have been created on this theme. The present work comprises three different subareas related to neuroengineering and electrical engineering: neural stimulation; theoretical and computational neuroscience; and neuronal signal processing; as well as biomedical engineering. The research can be divided in three parts: (i) A new method of neuronal photostimulation was developed based on the use of caged compounds. Using the inhibitory neurotransmitter GABA caged by a ruthenium complex it was possible to block neuronal population activity using a laser pulse. The obtained results were evaluated by Wavelet analysis and tested by non-parametric statistics. (ii) A mathematical method was created to identify neuronal assemblies. Neuronal assemblies were proposed as the basis of learning by Donald Hebb remain the most accepted theory for neuronal representation of external stimuli. Using the Marcenko-Pastur law of eigenvalue distribution it was possible to detect neuronal assemblies and to compute their activity with high temporal resolution. The application of the method in real electrophysiological data revealed that neurons from the neocortex and hippocampus can be part of the same assembly, and that neurons can participate in multiple assemblies. (iii) A new method of automatic classification of heart beats was developed, which does not rely on a data base for training and is not specialized in specific pathologies. The method is based on Wavelet decomposition and normality measures of random variables. Throughout, the results presented in the three fields of knowledge represent qualification in neural and biomedical engineering