Proprioception is the ability to sense body position necessary for coordinate precise movements. Despite the low complexity of insect neuronal systems, scientists are studying their motor control system. Researchers performed experiments in desert locusts by stimulating their apodeme and recording the neuronal response. Previous studies reported variations in neuronal spiking rates related to acceleration, velocity and position sensitivity. Their results led us to the assumption that either there are different kinds of sensory neurons, or there is only one type of neuron responding to various Physical quantities. Therefore, this research intends to investigate the different spiking rates. We also want to study the influence of apodemes excitations in sensory neurons with information theoretical measures. However, the way signals were recorded does not allow the calculation of delayed transfer entropy (DTE) between sensory neurons. To solve that problem we propose a method to estimate parameters of connections in such scenarios. Our analysis will model the time spent between spikes with survival functions. The influence of excitation in the neuronal response will be analyzed with DTE, which will also be used to validate the methods of simulation. Results show that there is evidence to support the assumption of different spiking rates among sensory neurons. DTE suggests the existence of intermediate processing nodes between excitation and some sensory neurons. A further simulation joining the methods proposed and neuronal signals showed that models considering intermediate pathways present a good fit to the data. We suggest that the different responses of sensory neurons are not due to various types of neurons, but to a preprocessing layer. / Propriocepção é a capacidade de monitorar a posição do corpo necessária para coordenar movimentos precisos. Apesar da baixa complexidade dos sistemas neuronais de insetos, cientistas têm estudado seu controle motor. Pesquisadores realizaram experimentos em gafanhotos estimulando mecanicamente seu apódema e registrando a resposta neuronal. Estudos anteriores relatam variações nas taxas de spiking, e as relacionam com sensibilidades à aceleração, à velocidade e à posição. Seus resultados nos levaram às suposições de que ou existem diferentes tipos de neurônios sensores ou há apenas um tipo de neurônio sensível à diferentes grandezas físicas. Portanto, esta pesquisa pretende investigar as diferentes taxas de spiking e estudar a influência das excitações do apódema em neurônios sensores com medidas de teoria da informação. No entanto, a forma como os sinais foram gravados não permite calcular-se a transferência de entropia atrasada (DTE) entre neurônios sensores. Para tanto, propôs-se um método de estimação de parâmetros para ligações em tais cenários. As análises modelarão o tempo gasto entre spikings com funções de sobrevida. Além disso, a influência da excitação sobre a resposta neuronal será analisada com DTE, a qual também será utilizada para validar os métodos de simulação. Os resultados mostram que há evidências para suportar a hipótese de diferentes taxas de spiking. A DTE sugere a existência de nós intermediários (entre excitação e alguns neurônios sensoriais). Posteriormente, uma simulação juntando os métodos propostos e os sinais neuronais mostrou que modelos considerando caminhos intermediários se ajustam bem aos dados. Por fim, os resultados sugerem que as diferentes respostas de neurônios sensores não acontecem devido a diferentes tipos de neurônios, mas sim à uma camada de pré-processamento.

Made available in DSpace on 2016-06-02T19:29:23Z (GMT). No. of bitstreams: 1 3080.pdf: 3999883 bytes, checksum: 69813765b74ed7c5691286be1b8d6314 (MD5) Previous issue date: 2010-06-01 / Universidade Federal de Sao Carlos / This work aimed evaluates the Alto Jacaré-Guaçu rivers basin quality through physical, chemical and biological and ecotoxicological analysis. Samples of water and sediment were collected in April /07; July/07; October/07 and January/08 in fourteen locations. Moreover studies about survey/growth and gills morphology were performed in Danio rerio and Poecilia reticulata fishes exposed in the toxicity tests. The limnological data revealed the higher concentrations of metals and other variables in the October/07 and January/08 seasons, being over the limits established for aquatic organism s protection. In the water samples all metals were above the CONAMA 357/05, highlighting the values of cadmium and chromium. In the sediment, only cadmium and zinc values above the TEL however, the influence of high values of chromium in the collection sites were discriminated by multivariate analysis. The partial chronic toxicity tests performed using samples obtained from de natural environment revealed significant toxicity for a last one fish species and one parameter. Sublethal effects in the survey/growth and gills morphology were detected in the tests organisms on the different seasons to the each one specie. The sediment samples from July/07 and April/07 were more toxic to P. reticulate specie; however January/08 samples were more toxic to D. rerio. Chromium was chosen for the evaluation of toxicity in laboratory by spiking in artificial sediments. The results presented the suitability of the artificial sediments as to a substrate for toxicity tests and assisted in the interpretation of the chromium toxicity. The LC50;96h of chromium spiked in inorganic and organic sediments was 340,56 and <1440,0 mg.Kg-1 (C. xanthus); 1731,04 and 2263,54 mg.Kg-1 (D. rerio); 2263,54 mg/Kg and 1377,55 mg.Kg-1 (P. reticulata), respectively. The sublethal effects were detected at very low concentrations of bioavaliable chromium in sediments (0,8 to 1,425 mg/Kg) and dissolved in water (0,0275 to1,138 mg/L). The results suggesting that the chromium concentrations detected in the Alto Jacaré-Guaçu basin may present a risk to aquatic life. / O presente estudo teve como objetivo avaliar a qualidade dos rios da bacia hidrográfica do Alto Jacaré-Guaçu por meio de análises físicas, químicas, biológicas e ecotoxicológicas. Para tanto foram realizadas quatro amostragens (abril/07; julho/07; outubro/07 e janeiro/08) nos quais foram amostrados água e sedimento em quatorze pontos distribuídos na bacia. A sobrevivência/crescimento e a morfologia das brânquias foram variáveis consideradas nos testes de toxicidade, utilizando-se as espécies Danio rerio e Poecilia reticulada como organismos-teste. Os resultados limnológicos revelaram que, em geral, os períodos de outubro/07 e janeiro/08 foram encontradas as concentrações mais elevadas das diferentes variáveis analisadas, incluindo-se os metais em concentrações acima dos limites recomendados para a proteção da vida aquática. Na água, todos os metais avaliados estiveram acima do valor estabelecido pelo CONAMA 357/05 para proteção da vida aquática, com destaque para os valores de cádmio e cromo. No sedimento, apenas o cádmio e zinco apresentaram valores acima da TEL, no entanto, a influência dos valores elevados de cromo nos pontos de coleta foi discriminada pela análise multivariada. Nos testes de toxicidade crônica parcial com sedimentos, ao menos uma das espécies e variáveis analisadas indicaram a toxicidade dos rios da bacia. Efeitos subletais na sobrevivência/crescimento e na morfologia das brânquias foram detectados nos organismosteste em períodos de amostragem diferentes para cada espécie. As amostras de sedimento de abril/07 e julho/07 foram mais tóxicas para P. reticulata e as amostras de janeiro/08 foram mais tóxicas para D. rerio. Foi escolhido o cromo para a avaliação da toxicidade em laboratório, por meio da fortificação ( spiking ) em sedimentos artificiais com formulação simples. Os resultados demonstraram a adequabilidade dos sedimentos artificiais como substrato para testes de toxicidade e auxiliaram na interpretação da toxicidade do cromo. A CL50;96h de cromo adicionado a sedimentos sem MO e com MO para C. xanthus; D. rerio e P. reticulata foi de 340,56 e <1440,0 mg/Kg; 1731,04 e 2263,54 mg/Kg e 1377,55 e 2244,48 mg/Kg, respectivamente. Os efeitos subletais foram detectados em concentrações muito baixas de cromo biodisponível nos sedimentos (0,8 a 1,425 mg/Kg) e dissolvido na água (0,0275 a 1,138 mg/L). Esses resultados permitiram considerar que as concentrações de cromo detectadas na bacia do Alto Jacaré-Guaçu podem apresentar riscos para a vida aquática.

Proprioception is the ability to sense body position necessary for coordinate precise movements. Despite the low complexity of insect neuronal systems, scientists are studying their motor control system. Researchers performed experiments in desert locusts by stimulating their apodeme and recording the neuronal response. Previous studies reported variations in neuronal spiking rates related to acceleration, velocity and position sensitivity. Their results led us to the assumption that either there are different kinds of sensory neurons, or there is only one type of neuron responding to various Physical quantities. Therefore, this research intends to investigate the different spiking rates. We also want to study the influence of apodemes excitations in sensory neurons with information theoretical measures. However, the way signals were recorded does not allow the calculation of delayed transfer entropy (DTE) between sensory neurons. To solve that problem we propose a method to estimate parameters of connections in such scenarios. Our analysis will model the time spent between spikes with survival functions. The influence of excitation in the neuronal response will be analyzed with DTE, which will also be used to validate the methods of simulation. Results show that there is evidence to support the assumption of different spiking rates among sensory neurons. DTE suggests the existence of intermediate processing nodes between excitation and some sensory neurons. A further simulation joining the methods proposed and neuronal signals showed that models considering intermediate pathways present a good fit to the data. We suggest that the different responses of sensory neurons are not due to various types of neurons, but to a preprocessing layer. / Propriocepção é a capacidade de monitorar a posição do corpo necessária para coordenar movimentos precisos. Apesar da baixa complexidade dos sistemas neuronais de insetos, cientistas têm estudado seu controle motor. Pesquisadores realizaram experimentos em gafanhotos estimulando mecanicamente seu apódema e registrando a resposta neuronal. Estudos anteriores relatam variações nas taxas de spiking, e as relacionam com sensibilidades à aceleração, à velocidade e à posição. Seus resultados nos levaram às suposições de que ou existem diferentes tipos de neurônios sensores ou há apenas um tipo de neurônio sensível à diferentes grandezas físicas. Portanto, esta pesquisa pretende investigar as diferentes taxas de spiking e estudar a influência das excitações do apódema em neurônios sensores com medidas de teoria da informação. No entanto, a forma como os sinais foram gravados não permite calcular-se a transferência de entropia atrasada (DTE) entre neurônios sensores. Para tanto, propôs-se um método de estimação de parâmetros para ligações em tais cenários. As análises modelarão o tempo gasto entre spikings com funções de sobrevida. Além disso, a influência da excitação sobre a resposta neuronal será analisada com DTE, a qual também será utilizada para validar os métodos de simulação. Os resultados mostram que há evidências para suportar a hipótese de diferentes taxas de spiking. A DTE sugere a existência de nós intermediários (entre excitação e alguns neurônios sensoriais). Posteriormente, uma simulação juntando os métodos propostos e os sinais neuronais mostrou que modelos considerando caminhos intermediários se ajustam bem aos dados. Por fim, os resultados sugerem que as diferentes respostas de neurônios sensores não acontecem devido a diferentes tipos de neurônios, mas sim à uma camada de pré-processamento.

In order to understand how the neural system encodes and processes information, research has focused on the study of neural representations of simple stimuli, paying no particular attention to it's temporal structure, with the assumption that a deeper understanding of how the neural system processes simpli fied stimuli will lead to an understanding of how the brain functions as a whole [1]. However, time is intrinsically bound to neural processing as all sensory, motor, and cognitive processes are inherently dynamic. Despite the importance of neural and stimulus dynamics, little is known of how the neural system represents rich spatio-temporal stimulus, which ultimately link the neural system to a continuously changing environment. The purpose of this thesis is to understand whether and how temporally-structured neural activity modulates the processing of information within the brain, proposing in turn that, the precise interaction between the spatio-temporal structure of the stimulus and the neural system is particularly relevant, particularly when considering the ongoing plasticity mechanisms which allow the neural system to learn from experience. In order to answer these questions, three studies were conducted. First, we studied the impact of spiking temporal structure on a single neuron spiking response, and explored in which way the functional connections to pre-synaptic neurons are modulated through adaptation. Our results suggest that, in a generic spiking neuron, the temporal structure of pre-synaptic excitatory and inhibitory neurons modulate both the spiking response of that same neuron and, most importantly, the speed and strength of learning. In the second, we present a generic model of a spiking neural network that processes rich spatio-temporal stimuli, and explored whether the processing of stimulus within the network is modulated due to the interaction with an external dynamical system (i.e. extracellular media), as well as several plasticity mechanisms. Our results indicate that the memory capacity, that re ects a dynamic short-term memory of incoming stimuli, can be extended on the presence of plasticity and through the interaction with an external dynamical system, while maintaining the network dynamics in a regime suitable for information processing. Finally, we characterized cortical signals of human subjects (electroencephalography, EEG) associated to a visual categorization task. Among other aspects, we studied whether changes in the dynamics of the stimulus leads to a changes in the neural processing at the cortical level, and introduced the relevance of large-scale integration for cognitive processing. Our results suggest that the dynamic synchronization across distributed cortical areas is stimulus specific and specifically linked to perceptual grouping. Taken together, the results presented here suggest that the temporal structure of the stimulus modulates how the neural system encodes and processes information within single neurons, network of neurons and cortical areas. In particular, the results indicate that timing modulates single neuron connectivity structures, the memory capability of networks of neurons, and the cortical representation of a visual stimuli. While the learning of invariant representations remains as the best framework to account for a number of neural processes (e.g. long-term memory [2]), the reported studies seem to provide support the idea that, at least to some extent, the neural system functions in a non-stationary fashion, where the processing of information is modulated by the stimulus dynamics itself. Altogether, this thesis highlights the relevance of understanding adaptive processes and their interaction with the temporal structure of the stimulus, arguing that a further understanding how the neural system processes dynamic stimuli is crucial for the further understanding of neural processing itself, and any theory that aims to understand neural processing should consider the processing of dynamic signals. 1. Frankish, K., and Ramsey, W. The Cambridge Handbook of Cognitive Science. Cambridge University Press, 2012. // 2. McGaugh, J. L. Memory{a Century of Consolidation. Science 287, 5451 (Jan. 2000), 248{251.

[pt] O envelhecimento da voz, conhecido como presbifonia, é um processo natural que pode causar grande modificação na qualidade vocal do indivíduo. A sua identificação precoce pode trazer benefícios, buscando tratamentos que possam prevenir o seu avanço. Esse trabalho tem como motivação a identificação de vozes com sinais de envelhecimento através de redes neurais do tipo Spiking (SNN). O objetivo principal é o de construir dois novos modelos, denominados híbridos, utilizando SNN para problemas de agrupamento, onde os atributos de entrada e os parâmetros que configuram a SNN são otimizados por algoritmos evolutivos. Mais especificamente, os modelos neuro-evolucionários propostos são utilizados com o propósito de configurar corretamente a SNN, e selecionar os atributos mais relevantes para a formação dos grupos. Os algoritmos evolutivos utilizados foram o Algoritmo Evolutivo com Inspiração Quântica com representação Binário-Real (AEIQ-BR) e o Optimization by Genetic Programming (OGP). Os modelos resultantes foram nomeados Quantum-Inspired Evolution of Spiking Neural Networks with Binary-Real (QbrSNN) e Spiking Neural Network Optimization by Genetic Programming (SNN-OGP). Foram utilizadas oito bases benchmark e duas bases de voz, masculinas e femininas, a fim de caracterizar o envelhecimento. Para uma análise funcional da SNN, as bases benchmark forma testadas com uma abordagem clássica de agrupamento (kmeans) e com uma SNN sem evolução. Os modelos propostos foram comparados com uma abordagem clássica de Algoritmo Genético (AG). Os resultados mostraram a viabilidade do uso das SNNs para agrupamento de vozes envelhecidas. / [en] The aging of the voice, known as presbyphonia, is a natural process that can cause great change in vocal quality of the individual. Its early identification can benefit, seeking treatments that could prevent their advance. This work is motivated by the identification of voices with signs of aging through neural networks of spiking type (SNN). The main objective is to build two new models, called hybrids, using SNN for clustering problems where the input attributes and parameters that configure the SNN are optimized by evolutionary algorithms. More specifically, the proposed neuro-evolutionary models are used in order to properly configure the SNN, and select the most relevant attributes for the formation of groups. Evolutionary algorithms used were the Evolutionary Algorithm with Quantum Inspiration with representation Binary-Real (AEIQ-BR) and the Optimization by Genetic Programming (OGP). The resulting models were named Quantum-Inspired Spiking Neural Evolution of Networks with Binary-Real (QbrSNN) and Spiking Neural Network Optimization by Genetic Programming (SNN-OGP). Eight bases were used, and two voice benchmark bases, male and female, in order to characterize aging. NNS for functional analysis, the tested benchmark base form with a classical clustering approach (kmeans) and a SNN without change. The proposed models were compared with a classical approach of Genetic Algorithm (GA). The results showed the feasibility of using the SNN to agrupamentode aged voices.

In dieser Arbeit versuche ich, den neuronalen Code, d. h. die Art und Weise, wie die Nervenzellen des Gehirns Informationen in ihrer Aktivität übertragen und verarbeiten, besser zu verstehen, indem ich die Kodierung von Stimuli in neuronalen Systemen untersuche. Zu diesem Zweck analysiere ich die Veränderungen in der Dynamik von neuronalen Standardmodellen, die im Rahmen der statistischen Physik entwickelt wurden, in Bezug auf Veränder- ungen der Parameter und der Konnektivität bei Vorhandensein bzw. Fehlen eines Reizes. Ich verwende informationstheoretische Maße, um die Fähigkeit neuronaler Populationen, empfangene Informationen durch ihren Output zu übertragen, zu quantifizieren. Die vorgestellten Ergebnisse bauen auf einer Vielzahl früherer Studien über unverbundene und rekurrente neuronale Pop- ulationen auf. Einige dieser Studien heben zwei neuronale Code-Kandidaten hervor, die unterschiedliche Profile der Informationsfilterung aufweisen: einen Integrationscode, der als Tiefpass-Informationsfilter fungiert, und einen Synchroniecode, der als Bandpassfilter fungiert. Das Ziel der vorliegenden Arbeit ist es, die Ergebnisse dieser Studien auf Netzwerke mit einem höheren Konnektivitätsgrad, wie er im Kortex beobachtet wird, auszuweiten. / In this thesis I attempt to better understand the neural code, or the way in which the nerve cells of the brain transmit and process information in their activity, through the investigation of stimulus encoding in neural systems. To this end, I analyze changes in the dynamics of standard neuronal models, de- veloped in the framework of statistical physics, to variations in parameters and connectivity in the presence versus the absence of a stimulus. In conjunction, information theoretical measures are utilized to quantify the ability of neu- ronal populations to transmit received information through their output. The presented results build upon a multitude of previous studies of both uncon- nected and recurrent neural populations. Some of these studies highlight two neural code candidates that have distinct information filtering profiles: an in- tegration code that acts as a low-pass information filter and a synchrony code that acts as a bandpass filter. In the following, synaptic connectivity is added in diverse ways in order to extend results of these studies to networks with a higher level of connectivity, as observed in the cortex.

An Information Theoretic approach is used for studying the effect of noise on various spiking neural systems. Detailed statistical analyses of neural behaviour under the influence of stochasticity are carried out and their results related to other work and also biological neural networks. The neurocomputational capabilities of the neural systems under study are put on an absolute scale. This approach was also used in order to develop an optimisation framework. A proof-of-concept algorithm is designed, based on information theory and the coding fraction, which optimises noise through maximising information throughput. The algorithm is applied with success to a single neuron and then generalised to an entire neural population with various structural characteristics (feedforward, lateral, recurrent connections). It is shown that there are certain positive and persistent phenomena due to noise in spiking neural networks and that these phenomena can be observed even under simplified conditions and therefore exploited. The transition is made from detailed and computationally expensive tools to efficient approximations. These phenomena are shown to be persistent and exploitable under a variety of circumstances. The results of this work provide evidence that noise can be optimised online in both single neurons and neural populations of varying structures.

Spike-timing dependent plasticity is a learning mechanism used extensively within neural modelling. The learning rule has been shown to allow a neuron to find the onset of a spatio-temporal pattern repeated among its afferents. In this thesis, the first question addressed is ‘what does this neuron learn?’ With a spiking neuron model and linear prediction, evidence is adduced that the neuron learns two components: (1) the level of average background activity and (2) specific spike times of a pattern. Taking advantage of these findings, a network is developed that can train recognisers for longer spatio-temporal input signals using spike-timing dependent plasticity. Using a number of neurons that are mutually connected by plastic synapses and subject to a global winner-takes-all mechanism, chains of neurons can form where each neuron is selective to a different segment of a repeating input pattern, and the neurons are feedforwardly connected in such a way that both the correct stimulus and the firing of the previous neurons are required in order to activate the next neuron in the chain. This is akin to a simple class of finite state automata. Following this, a novel resource-based STDP learning rule is introduced. The learning rule has several advantages over typical implementations of STDP and results in synaptic statistics which match favourably with those observed experimentally. For example, synaptic weight distributions and the presence of silent synapses match experimental data.

We introduce a novel system for recognition of partially occluded and rotated images. The system is based on a hierarchical network of integrate-and-fire spiking neurons with random synaptic connections and a novel organization process. The network generates integrated output sequences that are used for image classification. The network performed satisfactorily given appropriate topology, i.e. the number of neurons and synaptic connections, which corresponded to the size of input images. Comparison of Synaptic Plasticity Activity Rule (SAPR) and Spike Timing Dependant Plasticity (STDP) rules, used to update connections between the neurons, indicated that the SAPR gave better results and thus was used throughout. Test results showed that the network performed better than Support Vector Machines. We also introduced a stopping criterion based on entropy, which significantly shortened the iterative process while only slightly affecting classification performance.

Multisensory processing in the brain underlies a wide variety of perceptual phenomena, but little is known about the underlying mechanisms of how multisensory neurons are generated and how the neurons integrate sensory information from environmental events. This lack of knowledge is due to the difficulty of biological experiments to manipulate and test the characteristics of multisensory processing. By using a computational model of multisensory processing this research seeks to provide insight into the mechanisms of multisensory processing. From a computational perspective, modeling of brain functions involves not only the computational model itself but also the conceptual definition of the brain functions, the analysis of correspondence between the model and the brain, and the generation of new biologically plausible insights and hypotheses. In this research, the multisensory processing is conceptually defined as the effect of multisensory convergence on the generation of multisensory neurons and their integrated response products, i.e., multisensory integration. Thus, the computational model is the implementation of the multisensory convergence and the simulation of the neural processing acting upon the convergence. Next, the most important step in the modeling is analysis of how well the model represents the target, i.e., brain function. It is also related to validation of the model. One of the intuitive and powerful ways of validating the model is to apply methods standard to neuroscience for analyzing the results obtained from the model. In addition, methods such as statistical and graph-theoretical analyses are used to confirm the similarity between the model and the brain. This research takes both approaches to provide analyses from many different perspectives. Finally, the model and its simulations provide insight into multisensory processing, generating plausible hypotheses, which will need to be confirmed by real experimentation.