Electrophysiological recordings are a valuable tool for neuroscience in order to monitor the activity of multiple or even single neurons. Significant insights into the nervous system have been gained by analyses of resulting data; in particular, many findings were gained from spike trains whose correlations can give valuable indications about neural interplay. But detecting, specifying, and representing neural interactions is mathematically challenging. Further, recent advances of recording techniques led to an increase in volume of collected data, which often poses additional computational problems. These developments call for new, improved methods in order to extract crucial information. The matter of this thesis is twofold: It presents a novel method for the analysis of neural spike train data, as well as a generic framework in order to assess the new and related techniques. The new computational method, the Snap Shot Score, can be used to inspect spike trains with respect to temporal dependencies, which are visualised as an information flow network. These networks can specify the relationships in the data, indicate changes in dependencies, and point to causal interactions. The Snap Shot Score is demonstrated to reveal plausible networks both in a variety of simulations and for real data, which indicate its value for understanding neural dynamics. Additional to the Snap Shot Score, a neural simulation framework is suggested, which facilitates the assessment of neural network inference techniques in a highly automated fashion. Due to a new formal concept to rate learned networks, the framework can be used to test techniques under partial observability conditions. In the presence of hidden units quantification of results has been a tedious task that had to be done by hand, but which can now be automated. Thereby high throughput assessments become possible, which facilitate a comprehensive simulation-based characterisation of new methods.

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal.

Tittle: Evaluating the with successfulness of playing actions of an individual in women's volleyball at the Olympic Games in Beijing 2008 Author: Eva Němcová Department: Department of physical training Supervisor: PaedDr. Ladislav Pokorný, Katedra tělesné výchovy Pedagogické fakulty Univerzity Karlovy v Praze, M. D. Rettigové 4, 116 39 Praha 1 Supervisor's e-mail adress: pok.lad@email.cz Abstract: This thesis is intended for interested people from general public, firstly as educational materials for a narrow group of teachers and coaches of volleyball on high schools, sports grammar schools, etc. The thesis includes chapters focused on history, characteristics of particular playing actions of an individual, playing systems, combinations and playing achievements. The research deals with successfulness of playing actions of an individual in women's volleyball at the Olympic Games in Beijing 2008. Keywords: volleyball, playing actions of an individual, spike, serv, block, digging, reception

Foram submetidos à análise molecular, 15 isolados do vírus da bronquite infecciosa (VBI) obtidos durante o período de 1988 a 2000, de surtos à campo da Bronquite Infecciosa (BI), em aves de corte ou de postura das regiões Sul e Sudeste do Brasil. Os resultados obtidos da análise filogenética das sequências parciais dos genes da glicoproteína de espícula (S1) e da nucleoproteína (N) evidenciaram que a maior parte dos isolados estão distribuídos em dois grandes grupos; o primeiro deles mais estreitamente relacionado às estirpes do genótipo Massachusetts e o segundo constituído apenas por isolados brasileiros autóctones com uma grande diversidade em relação às estirpes ou isolados do grupo Massachusetts e de outros países ou continentes. Os sítios polimórficos mais importantes formaram-se em locais específicos e de maneira agrupada nas sequências dos genes S1 ou N e predominam em regiões codificadoras das cadeias polipeptídicas S1 e N que configuram sítios estruturais e antigênicos importantes envolvidos, na expressão de propriedades biológicas relevantes. / Fifteen Brazilian field isolates of infectious bronchitis virus (IBV); were recovered, between 1988 and 2000, from commercial broiler or layer flocks located in South and Southeast Brazilian regions. Molecular and phylogenetic analysis of partial sequences of 5\'-proximal of S1 gene and 3\'-terminus of N gene from these IBV isolates, identified two main groups; the Massachusetts group and a Brazilian indigenous group, which presenting a high diversity regarding the first group or other IBV strains from different countries and continents. The major polymorphic sites are arranged in clusters and predominate in the regions of S1 and N genes which code for relevant structural and antigenic sites responsible for the expression of important biological properties.

Made available in DSpace on 2017-07-21T19:25:53Z (GMT). No. of bitstreams: 1 Rafael R Borges.pdf: 2438169 bytes, checksum: 7ddddb02731c3c29cfcf61b843e0f82d (MD5) Previous issue date: 2016-05-11 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / In this thesis, it was investigated the influence of synaptic plasticity in the dynamics of neuronal networks. Specifically, we analyzed the effect on creation and suppression of synchronization spikes in networks composed of neurons with excitatory and inhibitory synapses. The spike timing-dependent plasticity (STDP) changes the strength of existing synapses in the neuronal network. To simulate the dynamics of each neuron, we considered the Hodgkin and Huxley model (HH), that is able to provide the main features of the temporal evolution of the membrane potential of each cell. The Kuramoto order parameter was utilized as synchronization diagnostic. First of all, we have studied the dynamics spikes in neuronal networks on a global and random topology with excitatory synapses with plasticity (STDP). It was observed that the STDP improves synchronization in a sufficiently dense neuronal networks. However, this effect is maximized by the insertion of an external perturbation of moderate intensity. Further, the system behavior was analyzed using the combination of inhibitory and excitatory synapses, both with spike timing-dependent plasticity. Our results indicated that the network becomes desynchronized when the intensity of inhibitory synapses is increased. Nevertheless, for small intensities of these synapses there was an increase in the values of the order parameter when the system with STDP was perturbed. / Nesta tese foi investigada a influência dos modelos de plasticidade sináptica na dinâmica de redes neuronais. Especificamente, foi analisado o efeito da plasticidade na criação e supressão da sincronização ao de disparos em redes compostas por neurônios com sinapses excitatórias e inibitórias. O modelo de plasticidade sináptica dependente do tempo entre disparos (do inglês: Spike-timing-dependent plasticity: STDP), modifica a intensidade das sinapses existentes na rede neuronal. Para simular a dinâmica de cada neurônio foi utilizado o modelo de Hodgkin e Huxley (HH), que ´e capaz de fornecer as principais características da evolução ao temporal do potencial de membrana de cada célula. Como diagnóstico de sincronização foi utilizado o parâmetro de ordem de Kuramoto. Primeiramente foi investigada a dinâmica de disparos em redes neuronais com topologia global e aleatória com sinapses excitatórias com plasticidade (STDP). Observou-se que a STDP contribui para a sincronização ao do sistema em redes neuronais suficientemente densas. No entanto, este efeito é maximizado com a inserção de uma perturbação o externa de intensidade moderada. Na sequência, foi analisado o comportamento do sistema com a combinação de sinapses excitatórias e inibitórias, ambas com STDP. Os resultados indicaram que a rede torna-se não sincronizada com o aumento da intensidade das sinapses inibitórias. Entretanto, para pequenas intensidades destas sinapses, observou-se um acréscimo nos valores do parâmetro de ordem quando o sistema com STDP foi perturbado.

Infectious bronchitis virus (IBV) is the causative agent of an economically important disease of poultry. In Brazil this disease causes respiratory, renal and reproductive problems in birds of all ages, despite constant vaccination with the Massachusetts strain H120. This lack of immunological protection is known to be due the genetic variation in the spike glycoprotein of IBV, which is involved in host cell attachment, neutralization and the induction of protective immunity. Brazilian IBV variants resulting of this genetic variation are present since the 80s and this study aimed to epidemiologicaly analyze and molecularly characterize the existing variants during 2010-2015 and perform a bioinformatics analysis of the available sequences of IBV variants in a 40 year period. Of the 453 samples tested, 61.4% were positive for IBV and 75.9% of them were considered variants and were detected in birds of all ages, distributed in all five Brazilian regions. A fragment of 559-566 bp was obtained from 12 isolates, where BR-I was the predominant variant while only one isolate belonged to the BR-II genotype. Bioinformatics analysis of the sequences of 40 years of Brazilian IBV variants was performed and the ratio of non-synonymous substitutions per non-synonymous site (dn) to synonymous substitutions per synonymous site (ds) dN/dS was calculated. It revealed a predominance of codons with non-synonymous substitutions in the first third of the S1 gene and a dN/dS ratio of 0.6757, indicating that this portion of the gene was under negative selection. Additionally prediction of N-glycosilation sites showed that most of the BR-I variants (from 2003 to early 2014) present an extra site at animoacid position 20, while the newest ones lack this feature.Together these results suggest that IBV Brazilian variants had probably suffered drastic mutations in some points between the years 1983 to 2003 and after achieving an antigenic structure effective enough for invasion and replication in their hosts, the selection processes became silent. / O vírus da bronquite infecciosa das galinhas (IBV) é o agente causador de uma doença aviária economicamente importante. No Brasil, esta doença ocasiona problemas respiratórios, renais e reprodutivos em aves de todas as idades, apesar da vacinação constante com a cepa Massachusetts H120. Esta falha na proteção conferida pela vacina é ocasionada por mutações nos nucleotídeos do gene da glicoproteína da espícula, a qual está envolvida no processo de interação comas células do hospedeiro, a neutralização e a indução de imunidade protetora. As variantes brasileiras resultantes dessa mutação genética estão presentes desde os anos 80 e este estudo teve como objetivo analisar epidemiologicamente e caracterizar molecularmente os vírus variantes existentes durante 2010-2015 e realizar uma análise bioinformática das sequências disponíveis no GenBank em um período de 40 anos. Das 453 amostras analisadas, 61,4% foram positivas para IBV e 75,9% delas foram consideradas variantes e foram detectados em aves de todas as idades, distribuídos em todas as 5 regiões do Brasil. Um fragmento de 559-566 pb foi obtido a partir de 12 isolados, onde BR-I foi a variante predominante ao contrario que apenas um isolado pertencia ao genótipo BR-II. Análise bioinformática de 40 anos de variantes do IBV brasileiros revelou uma predominância de codões com as substituições não sinónimos no primeiro terço do gene S1 e uma relação dN / dS de 0,6757, indicando que esta porção do gene estava sob selecção negativa. Além disso a previsão de pontos de de N-glicosilação mostrou que a maioria das amostras variantes BR-I (entre o 2003 e início de 2014) apresentam um ponto adicional na posição 20, enquanto as variantes mais novas não apresentam esse ponto de nglicosilação. Estes resultados sugerem que as variantes brasileiras teriam sofrido mutações provavelmente drásticas em alguns pontos do genoma, entre os anos de 1983 a 2003 e depois de atingir uma estrutura antigênica eficaz o suficiente para a invasão e replicação em seus hospedeiros, o processo de seleção mudou para seleção negativa.

Neuromorphic Engineering (NE) has led to the development of biologically-inspired computer architectures whose long-term goal is to approach the performance of the human brain in terms of energy efficiency and cognitive capabilities. Although there are a number of neuromorphic platforms available for large-scale Spiking Neural Network (SNN) simulations, the problem of programming these brain-like machines to be competent in cognitive applications still remains unsolved. On the other hand, Deep Learning has emerged in Artificial Neural Network (ANN) research to dominate state-of-the-art solutions for cognitive tasks. Thus the main research problem emerges of understanding how to operate and train biologically-plausible SNNs to close the gap in cognitive capabilities between SNNs and ANNs. SNNs can be trained by first training an equivalent ANN and then transferring the tuned weights to the SNN. This method is called ‘off-line’ training, since it does not take place on an SNN directly, but rather on an ANN instead. However, previous work on such off-line training methods has struggled in terms of poor modelling accuracy of the spiking neurons and high computational complexity. In this thesis we propose a simple and novel activation function, Noisy Softplus (NSP), to closely model the response firing activity of biologically-plausible spiking neurons, and introduce a generalised off-line training method using the Parametric Activation Function (PAF) to map the abstract numerical values of the ANN to concrete physical units, such as current and firing rate in the SNN. Based on this generalised training method and its fine tuning, we achieve the state-of-the-art accuracy on the MNIST classification task using spiking neurons, 99.07%, on a deep spiking convolutional neural network (ConvNet). We then take a step forward to ‘on-line’ training methods, where Deep Learning modules are trained purely on SNNs in an event-driven manner. Existing work has failed to provide SNNs with recognition accuracy equivalent to ANNs due to the lack of mathematical analysis. Thus we propose a formalised Spike-based Rate Multiplication (SRM) method which transforms the product of firing rates to the number of coincident spikes of a pair of rate-coded spike trains. Moreover, these coincident spikes can be captured by the Spike-Time-Dependent Plasticity (STDP) rule to update the weights between the neurons in an on-line, event-based, and biologically-plausible manner. Furthermore, we put forward solutions to reduce correlations between spike trains; thereby addressing the result of performance drop in on-line SNN training. The promising results of spiking Autoencoders (AEs) and Restricted Boltzmann Machines (SRBMs) exhibit equivalent, sometimes even superior, classification and reconstruction capabilities compared to their non-spiking counterparts. To provide meaningful comparisons between these proposed SNN models and other existing methods within this rapidly advancing field of NE, we propose a large dataset of spike-based visual stimuli and a corresponding evaluation methodology to estimate the overall performance of SNN models and their hardware implementations.

No description available.

In the middle of the 1990’s the microarray technology was introduced. The technology allowed for genome wide analysis of gene expression in one experiment. Since its introduction similar high through-put methods have been developed in other fields of molecular biology. These high through-put methods provide measurements for hundred up to millions of variables in a single experiment and a rigorous data analysis is necessary in order to answer the underlying biological questions. Further complications arise in data analysis as technological variation is introduced in the data, due to the complexity of the experimental procedures in these experiments. This technological variation needs to be removed in order to draw relevant biological conclusions from the data. The process of removing the technical variation is referred to as normalization or pre-processing. During the last decade a large number of normalization and data analysis methods have been proposed. In this thesis, data from two types of high through-put methods are used to evaluate the effect pre-processing methods have on further analyzes. In areas where problems in current methods are identified, novel normalization methods are proposed. The evaluations of known and novel methods are performed on simulated data, real data and data from an in-house produced spike-in experiment.

Dans cette thèse, nous étudions les applications potentielles des nano-dispositifs mémoires émergents dans les architectures de calcul. Nous montrons que des architectures neuro-inspirées pourraient apporter l'efficacité et l'adaptabilité nécessaires à des applications de traitement et de classification complexes pour la perception visuelle et sonore. Cela, à un cout moindre en termes de consommation énergétique et de surface silicium que les architectures de type Von Neumann, grâce à une utilisation synaptique de ces nano-dispositifs. Ces travaux se focalisent sur les dispositifs dit "memristifs", récemment (ré)-introduits avec la découverte du memristor en 2008 et leur utilisation comme synapse dans des réseaux de neurones impulsionnels. Cela concerne la plupart des technologies mémoire émergentes : mémoire à changement de phase - "Phase-Change Memory" (PCM), "Conductive-Bridging RAM" (CBRAM), mémoire résistive - "Resistive RAM" (RRAM)... Ces dispositifs sont bien adaptés pour l'implémentation d'algorithmes d'apprentissage non supervisés issus des neurosciences, comme "Spike-Timing-Dependent Plasticity" (STDP), ne nécessitant que peu de circuit de contrôle. L'intégration de dispositifs memristifs dans des matrices, ou "crossbar", pourrait en outre permettre d'atteindre l'énorme densité d'intégration nécessaire pour ce type d'implémentation (plusieurs milliers de synapses par neurone), qui reste hors de portée d'une technologie purement en "Complementary Metal Oxide Semiconductor" (CMOS). C'est l'une des raisons majeures pour lesquelles les réseaux de neurones basés sur la technologie CMOS n'ont pas eu le succès escompté dans les années 1990. A cela s'ajoute la relative complexité et inefficacité de l'algorithme d'apprentissage de rétro-propagation du gradient, et ce malgré tous les aspects prometteurs des architectures neuro-inspirées, tels que l'adaptabilité et la tolérance aux fautes. Dans ces travaux, nous proposons des modèles synaptiques de dispositifs memristifs et des méthodologies de simulation pour des architectures les exploitant. Des architectures neuro-inspirées de nouvelle génération sont introduites et simulées pour le traitement de données naturelles. Celles-ci tirent profit des caractéristiques synaptiques des nano-dispositifs memristifs, combinées avec les dernières avancées dans les neurosciences. Nous proposons enfin des implémentations matérielles adaptées pour plusieurs types de dispositifs. Nous évaluons leur potentiel en termes d'intégration, d'efficacité énergétique et également leur tolérance à la variabilité et aux défauts inhérents à l'échelle nano-métrique de ces dispositifs. Ce dernier point est d'une importance capitale, puisqu'il constitue aujourd'hui encore la principale difficulté pour l'intégration de ces technologies émergentes dans des mémoires numériques.