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A novel power-saving and small-area digital output cell is proposed in the first topic of this thesis. The new output cell dramatically reduces the output power consumption by filtering pre-defined spikes, which have been considered as one of the major power dissipation sources of the whole chip, with little sacrifice of speed or delay. The bound of the spikes to be removed can be pre-defined either dynamically by digital selection signals or permanently by fuses to be burned. The maximum operating clock is 200 MHz given a 10 pF off-chip load based on testing result of the testkey chip with an almost 28 % power reduction at all PVT corners. The second topic presents a design of a 19-T (19 transistors) full adder with high impedance circuit and conflict circuit. The transistor count is dramatically reduced such that the power dissipation as well as the area on chip is very small .

Background: Despite the so-called "generalised" nature of many epileptic seizures, patient specific spatio-temporal properties have been shown using imaging data at the macroscopic level of the cortex. Previous computational models have failed to account for spatial heterogeneities at the scale of the entire cortex. Furthermore, one of they key benefits of developing a model is the ability to easily test stimulation protocols. Previous studies of generalised spike-wave (the hallmark of absence epilepsy) have abstracted away from this.METHODSIn this work we develop a set of models of epileptic activity, one of which is at the scale of the entire cortex and incorporates anatomically relevant connectivity from human subjects. A similar model incorporating physiologically relevant thalamocortical circuitry is developed in order to test hypotheses regarding stimulation protocols.RESULTSWe show that the model can account for large-scale spatio-temporal dynamics similar to those seen in epileptic patients. We demonstrate, using the model of thalamocortical interaction, that such a modelling approach can be used for the evaluation of stimulation protocols which are shown to successfully abort the seizure prematurely.CONCLUSIONThis work highlights the importance of computational modelling to support existing data and to make specific predictions regarding testable hypotheses. For example, a stimulus given at the correct time with the correct amplitude will stop the seizure.

A new parallel solver based on SPIKE-TA algorithm has been developed using OpenMP API for solving diagonally-dominant banded linear systems on shared memory architectures. The results of the numerical experiments carried out for different test cases demonstrate high-performance and scalability on current multi-core platforms and highlight the time savings that SPIKE-TA OpenMP offers in comparison to the LAPACK BLAS-threaded LU model. By exploiting algorithmic parallelism in addition to threaded implementation, we obtain greater speed-ups in contrast to the threaded versions of sequential algorithms. For non-diagonally dominant systems, we implement the SPIKE-RL scheme and a new Spike-calling-Spike (SCS) scheme using OpenMP. The timing results for solving the non-diagonally dominant systems using SPIKE-RL show extremely good scaling in comparison to LAPACK and modified banded-primitive library.

The human sense of touch integrates feedback from a multitude of touch receptors, but how this information is represented in the neural responses such that it can be extracted quickly and reliably is still largely an open question. At the same time, dexterous robots equipped with touch sensors are becoming more common, necessitating better methods for representing sequentially updated information and new control strategies that aid in extracting relevant features for object manipulation from the data. This thesis uses information theoretic methods for two main aims: First, the neural code for tactile processing in humans is analyzed with respect to how much information is transmitted about tactile features. Second, machine learning approaches are used in order to influence both what data is gathered by a robot and how it is represented by maximizing information theoretic quantities. The first part of this thesis contains an information theoretic analysis of data recorded from primary tactile neurons in the human peripheral somatosensory system. We examine the differences in information content of two coding schemes, namely spike timing and spike counts, along with their spatial and temporal characteristics. It is found that estimates of the neurons’ information content based on the precise timing of spikes are considerably larger than for spikes counts. Moreover, the information estimated based on the timing of the very first elicited spike is at least as high as that provided by spike counts, but in many cases considerably higher. This suggests that first spike latencies can serve as a powerful mechanism to transmit information quickly. However, in natural object manipulation tasks, different tactile impressions follow each other quickly, so we asked whether the hysteretic properties of the human fingertip affect neural responses and information transmission. We find that past stimuli affect both the precise timing of spikes and spike counts of peripheral tactile neurons, resulting in increased neural noise and decreased information about ongoing stimuli. Interestingly, the first spike latencies of a subset of afferents convey information primarily about past stimulation, hinting at a mechanism to resolve ambiguity resulting from mechanical skin properties. The second part of this thesis focuses on using machine learning approaches in a robotics context in order to influence both what data is gathered and how it is represented by maximizing information theoretic quantities. During robotic object manipulation, often not all relevant object features are known, but have to be acquired from sensor data. Touch is an inherently active process and the question arises of how to best control the robot’s movements so as to maximize incoming information about the features of interest. To this end, we develop a framework that uses active learning to help with the sequential gathering of data samples by finding highly informative actions. The viability of this approach is demonstrated on a robotic hand-arm setup, where the task involves shaking bottles of different liquids in order to determine the liquid’s viscosity from tactile feedback only. The shaking frequency and the rotation angle of shaking are optimized online. Additionally, we consider the problem of how to better represent complex probability distributions that are sequentially updated, as approaches for minimizing uncertainty depend on an accurate representation of that uncertainty. A mixture of Gaussians representation is proposed and optimized using a deterministic sampling approach. We show how our method improves on similar approaches and demonstrate its usefulness in active learning scenarios. The results presented in this thesis highlight how information theory can provide a principled approach for both investigating how much information is contained in sensory data and suggesting ways for optimization, either by using better representations or actively influencing the environment.

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Este trabalho descreve o desenvolvimento e a validação de um gerador de estímulos visuais naturalísticos, GEN, utilizado em experimentos com o sistema visual de moscas. Tal gerador projeta a imagem em um anteparo que abrange todo o campo visual da mosca. Um espelho acoplado a um motor linear movimenta a imagem horizontalmente de acordo com velocidades predefinidas pelo experimentador. Ao contrário dos tradicionais geradores com monitores de raios catódicos - que apresentam imagens através de uma sequencia de quadros entre 60 e 200Hz - o GEN apresenta a imagem de forma contínua. Além de eliminar o problema das altas taxas de quadros por segundo exigidas nestes experimentos, o novo sistema também gera imagens com uma maior resolução, brilho e contraste. Durante a apresentação dos estímulos visuais foram registrados os potenciais de ação do neurônio H1 da mosca Chrysomya megacephala, localizado na placa lobular e responsável pela detecção de movimentos horizontais. Os resultados deste trabalho mostram como a resposta do neurônio estudado depende de parâmetros do estímulo tais como: velocidade, luminância, campo visual estimulado e frequência espacial da imagem utilizada. A influência destes parâmetros na resposta neural demonstram a importância de possuir geradores capazes de simular em laboratório estímulos que a mosca encontra em seu habitat natural. / This work presents the development and the validation of a naturalistic visual stimuli generator, NSG, to be used in neuroscience experiment with the flys visual system. To satisfy the requirement of naturalistic stimuli, the new generator projects a slide onto a screen covering the whole visual field of the fly. A mirror controlled by a linear electrical motor moves the image horizontally according to a predefined velocity profile. In addition, the NSG shows the image continuously as opposed to usual stimuli generators with cathode ray monitors having a frame rate between 60 and 200Hz. A further advantage of the NSG is its high luminance, brightness and contrast. During the stimulus presentations, the activity of the H1 neuron of the blowfly Chrysomya megacephala was recorded, this neuron is sensitive to horizontal image displacements. The results show how the response of the studied neuron depends on stimulus parameters such as speed, luminance, visual field stimulated and the spatial frequency of the image used. The influence of these parameters on neural response demonstrates the importance of generators that reproduces in laboratory the flys natural habitat.

Submitted by Maria Aparecida (cidazen@gmail.com) on 2017-09-04T15:04:55Z No. of bitstreams: 1 Marcos Vinicius Lopes.pdf: 7214975 bytes, checksum: 3d8e5de44c75de5f02b3f6101759f37a (MD5) / Made available in DSpace on 2017-09-04T15:04:55Z (GMT). No. of bitstreams: 1 Marcos Vinicius Lopes.pdf: 7214975 bytes, checksum: 3d8e5de44c75de5f02b3f6101759f37a (MD5) Previous issue date: 2013-02-27 / CAPES / Independent component analysis (ICA) is a method which objective is to find a non gaussian, linear or non linear representation such that the components are statistically independent. As a representation, tries to capture the input data essential structure. One of ICA applications is feature extraction. A main digital signal processing issue is finding a satisfactory representation, whether for image, speech signal or any signal type for purposes such as compression and de-noise. ICA can be aplied in this direction to propose generative models of the phenomena to be represented. This work presents the problem of spike classification in extracellular records, denominated spike sorting. It is assumed that the waveforms of spikes depend on factors such as the morphology of the neuron and the distance from the electrode, so that different neurons will present different forms of spikes. However, since different neurons may have similar spikes, what makes classification very difficult, the problem is even worse due to background noise and variation os spikes of the same neuron. The spike sorting algorithm is usually divided into three parts: firstly, the spikes are detected, then projected into a feature space (with possible dimensionality reduction) to facilitate differentiation between the waveforms from different neurons, finally the cluster algorithm is run for identifying these characteristics so the spikes from the same neuron. Here, we propose the use of ICA in feature extraction stage, being this step critical to the spike sorting process, thus distinguishing the activity of each neuron detected, supporting the analysis of neural population activity near the electrode. The method was compared with conventional techniques such as Principal Component Analysis and Wavelets, demonstrating a significant improvement in results. / A análise de componentes independentes (ICA, do inglês Indepdendent Component Analysis) é um método no qual o objetivo é encontrar uma representação linear ou não linear, não-gaussiana, tal que as componentes sejam estatisticamente independentes. Como uma representação busca capturar a estrutura essencial dos dados de entrada. Uma das aplicações de ICA é em extração de características. Um grande problema no processamento digital de sinais é encontrar uma representação adequada, seja para imagem, sinal de fala ou qualquer outro tipo de sinal para objetivos como compressão e remoção de ruído. ICA pode ser aplicada nesta direção ao tentar propor modelos geradores para os fenômenos a serem representados. Neste trabalho é apresentado o problema da classificação de espículas em gravações extracelulares, denominado spike sorting. Assume-se que as formas de onda das espículas dependem de fatores como a morfologia do neurônio e da distância deste para o eletrodo, então diferentes neurônios irão apresentar diferentes formas de espículas. Contudo diferentes neurônios podem apresentar espículas semelhantes, tornando a classificação mais difícil, o problema ainda é agravado devido ao ruído de fundo e a variação das espículas de um mesmo neurônio. O algoritmo de spike sorting geralmente é dividido em três partes: inicialmente as espículas são detectadas, em seguida são projetadas em um espaço de características (podendo haver redução de dimensionalidade) para facilitar a diferenciação entre as formas de onda de diferentes neurônios, por fim é feito o agrupamento dessas características identificando assim as espículas pertencentes ao mesmo neurônio. Aqui propomos a utilização de ICA na etapa de extração de características das espículas, sendo esta etapa crítica para o processo de spike sorting, permitindo assim distinguir a atividade de cada neurônio detectado, auxiliando a análise da atividade da população neural próxima ao eletrodo. O método foi comparado com técnicas convencionais como Análise de componentes principais (PCA, do inglês Principal Component Analysis) e Wavelets, demonstrando significativa melhora nos resultados.

Este trabalho descreve o desenvolvimento e a validação de um gerador de estímulos visuais naturalísticos, GEN, utilizado em experimentos com o sistema visual de moscas. Tal gerador projeta a imagem em um anteparo que abrange todo o campo visual da mosca. Um espelho acoplado a um motor linear movimenta a imagem horizontalmente de acordo com velocidades predefinidas pelo experimentador. Ao contrário dos tradicionais geradores com monitores de raios catódicos - que apresentam imagens através de uma sequencia de quadros entre 60 e 200Hz - o GEN apresenta a imagem de forma contínua. Além de eliminar o problema das altas taxas de quadros por segundo exigidas nestes experimentos, o novo sistema também gera imagens com uma maior resolução, brilho e contraste. Durante a apresentação dos estímulos visuais foram registrados os potenciais de ação do neurônio H1 da mosca Chrysomya megacephala, localizado na placa lobular e responsável pela detecção de movimentos horizontais. Os resultados deste trabalho mostram como a resposta do neurônio estudado depende de parâmetros do estímulo tais como: velocidade, luminância, campo visual estimulado e frequência espacial da imagem utilizada. A influência destes parâmetros na resposta neural demonstram a importância de possuir geradores capazes de simular em laboratório estímulos que a mosca encontra em seu habitat natural. / This work presents the development and the validation of a naturalistic visual stimuli generator, NSG, to be used in neuroscience experiment with the flys visual system. To satisfy the requirement of naturalistic stimuli, the new generator projects a slide onto a screen covering the whole visual field of the fly. A mirror controlled by a linear electrical motor moves the image horizontally according to a predefined velocity profile. In addition, the NSG shows the image continuously as opposed to usual stimuli generators with cathode ray monitors having a frame rate between 60 and 200Hz. A further advantage of the NSG is its high luminance, brightness and contrast. During the stimulus presentations, the activity of the H1 neuron of the blowfly Chrysomya megacephala was recorded, this neuron is sensitive to horizontal image displacements. The results show how the response of the studied neuron depends on stimulus parameters such as speed, luminance, visual field stimulated and the spatial frequency of the image used. The influence of these parameters on neural response demonstrates the importance of generators that reproduces in laboratory the flys natural habitat.

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