Neste trabalho estudam-se alguns novos métodos de seleção de variáveis no contexto da regressão linear que surgiram nos últimos 15 anos, especificamente o LARS - Least Angle Regression, o NAMS - Noise Addition Model Selection, a Razão de Falsa Seleção - RFS (FSR em inglês), o LASSO Bayesiano e o Spike-and-Slab LASSO. A metodologia foi a análise e comparação dos métodos estudados e aplicações. Após esse estudo, realizam-se aplicações em bases de dados reais e um estudo de simulação, em que todos os métodos se mostraram promissores, com os métodos Bayesianos apresentando os melhores resultados. / In this work, some new variable selection methods that have appeared in the last 15 years in the context of linear regression are studied, specifically the LARS - Least Angle Regression, the NAMS - Noise Addition Model Selection, the False Selection Rate - FSR, the Bayesian LASSO and the Spike-and-Slab LASSO. The methodology was the analysis and comparison of the studied methods. After this study, applications to real data bases are made, as well as a simulation study, in which all methods are shown to be promising, with the Bayesian methods showing the best results.

Neuronen sind erregbare Systeme. Ihre Antwort auf Anregungen oberhalb eines bestimmten Schwellwertes sind Pulse. Häufig wird die Pulserzeugung von verschiedenen Rückkopplungsmechanismen beeinflusst, die auf langsamen Zeitskalen agieren. Das kann zu Phänomenen wie Feuerraten-Adaptation, umgekehrter Feuerraten-Adaptation oder zum Feuern von Pulsen in Salven führen. Weiterhin sind Neuronen verschiedenen Rauschquellen ausgesetzt und wechselwirken mit anderen Neuronen, in neuronalen Netzen. Doch wie beeinflusst das Zusammenspiel von Rückkopplungsmechanismen, Rauschen und der Wechselwirkung mit anderen Neuronen die Pulserzeugung? Diese Arbeit untersucht, wie die Pulserzeugung in rauschgetriebenen erregbaren Systemen von langsamen Rückkopplungsmechanismen und der Wechselwirkung mit anderen erregbaren Systemen beeinflusst wird. Dabei wird die Pulserzeugung in drei Szenarien betrachtet: (i) in einem einzelnen erregbaren System, das um einen langsamen Rückkopplungsmechanismus erweitert wurde, (ii) in gekoppelten erregbaren Systemen und (iii) in stark gekoppelten salvenfeuernden Neuronen. In jedem dieser Szenarien wird die Pulsstatistik mit Hilfe von analytischen Methoden und Computersimulationen untersucht. Das wichtigste Resultat im ersten Szenario ist, dass das Zusammenspiel von einer stark anregenden Rückkopplung und Rauschen zu rauschkontrollierter Bistabilität führt. Das erlaubt es dem System zwischen verschiedenen Modi der Pulserzeugung zu wechseln. In (ii) wird die Pulserzeugung stark von der Wahl der Kopplungsstärken und der Anzahl der Verbindungen beeinflusst. Analytische Näherungen werden abgeleitet, die einen Zusammenhang zwischen der Anzahl der Verbindungen und der Pulsrate, sowie der Pulszugvariabilität herstellen. In (iii) wird festgestellt, dass eine hemmende Rückkopplung zu sehr unregelmäßigem Verhalten der isolierten Neuronen führt, wohingegen eine starke Kopplung mit dem Netzwerk ein regelmäßigeres Feuern von Salven hervorruft. / Neurons are excitable systems. Their responses to excitations above a certain threshold are spikes. Usually, spike generation is shaped by several feedback mechanisms that can act on slow time scales. These can lead to phenomena such as spike-frequency adaptation, reverse spike-frequency adaptation, or bursting. In addition to these, neurons are subject to several sources of noise and interact with other neurons, in the connected complexity of a neural network. Yet how does the interplay of feedback mechanisms, noise as well as interaction with other neurons affect spike generation? This thesis examines how spike generation in noise-driven excitable systems is influenced by slow feedback processes and coupling to other excitable systems. To this end, spike generation in three setups is considered: (i) in a single excitable system, which is complemented by a slow feedback mechanism, (ii) in a set of coupled excitable systems, and (iii) in a set of strongly-coupled bursting neurons. In each of these setups, the statistics of spiking is investigated by a combination of analytical methods and computer simulations. The main result of the first setup is that the interplay of strong positive (excitatory) feedback and noise leads to noise-controlled bistability. It enables excitable systems to switch between different modes of spike generation. In (ii), spike generation is strongly affected by the choice of the coupling strengths and the number of connections. Analytical approximations are derived that relate the number of connections to the firing rate and the spike train variability. In (iii), it is found that negative (inhibitory) feedback causes very irregular behavior of the isolated bursters, while strong coupling to the network regularizes the bursting.

Une nouvelle méthode de haute précision pour analyser les isotopes de Sn à avec double-spike 117Sn-122Sn a été développée. / A new high-precision isotope method for analyzing Sn using the 117Sn-122Sn double-spike technique was developed.

Neste trabalho estudam-se alguns novos métodos de seleção de variáveis no contexto da regressão linear que surgiram nos últimos 15 anos, especificamente o LARS - Least Angle Regression, o NAMS - Noise Addition Model Selection, a Razão de Falsa Seleção - RFS (FSR em inglês), o LASSO Bayesiano e o Spike-and-Slab LASSO. A metodologia foi a análise e comparação dos métodos estudados e aplicações. Após esse estudo, realizam-se aplicações em bases de dados reais e um estudo de simulação, em que todos os métodos se mostraram promissores, com os métodos Bayesianos apresentando os melhores resultados. / In this work, some new variable selection methods that have appeared in the last 15 years in the context of linear regression are studied, specifically the LARS - Least Angle Regression, the NAMS - Noise Addition Model Selection, the False Selection Rate - FSR, the Bayesian LASSO and the Spike-and-Slab LASSO. The methodology was the analysis and comparison of the studied methods. After this study, applications to real data bases are made, as well as a simulation study, in which all methods are shown to be promising, with the Bayesian methods showing the best results.

In this work, the problem of detecting neuronal spikes or action potentials (AP) in noisy recordings from a Microelectrode Array (MEA) is investigated. In particular, the spike detection algorithms should be less complex and with low computational complexity so as to be amenable for real time applications. The use of the MEA is that it allows collection of extracellular signals from either a single unit or multiple (45) units within a small area. The noisy MEA recordings then undergo basic filtering, digitization and are presented to a computer for further processing. The challenge lies in using this data for detection of spikes from neuronal firings and extracting spatiotemporal patterns from the spike train which may allow control of a robotic limb or other neuroprosthetic device directly from the brain. The aim is to understand the spiking action of the neurons, and use this knowledge to devise efficient algorithms for Brain Machine Interfaces (BMIs). An effective BMI will require a realtime, computationally efficient implementation which can be carried out on a DSP board or FPGA system. The aim is to devise algorithms which can detect spikes and underlying spatio-temporal correlations having computational and time complexities to make a real time implementation feasible on a specialized DSP chip or an FPGA device. The time-frequency localization, multiresolution representation and analysis properties of wavelets make them suitable for analysing sharp transients and spikes in signals and distinguish them from noise resembling a transient or the spike. Three algorithms for the detection of spikes in low SNR MEA neuronal recordings are proposed: 1. A wavelet denoising method based on the Discrete Wavelet Transform (DWT) to suppress the noise power in the MEA signal or improve the SNR followed by standard thresholding techniques to detect the spikes from the denoised signal. 2. Directly thresholding the coefficients of the Stationary (Undecimated) Wavelet Transform (SWT) to detect the spikes. 3. Thresholding the output of a Teager Energy Operator (TEO) applied to the signal on the discrete wavelet decomposed signal resulting in a multiresolution TEO framework. The performance of the proposed three wavelet based algorithms in terms of the accuracy of spike detection, percentage of false positives and the computational complexity for different types of wavelet families in the presence of colored AR(5) (autoregressive model with order 5) and additive white Gaussian noise (AWGN) is evaluated. The performance is further evaluated for the wavelet family chosen under different levels of SNR in the presence of the colored AR(5) and AWGN noise. Chapter 1 gives an introduction to the concept behind Brain Machine Interfaces (BMIs), an overview of their history, the current state-of-the-art and the trends for the future. It also describes the working of the Microelectrode Arrays (MEAs). The generation of a spike in a neuron, the proposed mechanism behind it and its modeling as an electrical circuit based on the Hodgkin-Huxley model is described. An overview of some of the algorithms that have been suggested for spike detection purposes whether in MEA recordings or Electroencephalographic (EEG) signals is given. Chapter 2 describes in brief the underlying ideas that lead us to the Wavelet Transform paradigm. An introduction to the Fourier Transform, the Short Time Fourier Transform (STFT) and the Time-Frequency Uncertainty Principle is provided. This is followed by a brief description of the Continuous Wavelet Transform and the Multiresolution Analysis (MRA) property of wavelets. The Discrete Wavelet Transform (DWT) and its filter bank implementation are described next. It is proposed to apply the wavelet denoising algorithm pioneered by Donoho, to first denoise the MEA recordings followed by standard thresholding technique for spike detection. Chapter 3 deals with the use of the Stationary or Undecimated Wavelet Transform (SWT) for spike detection. It brings out the differences between the DWT and the SWT. A brief discussion of the analysis of non-stationary time series using the SWT is presented. An algorithm for spike detection based on directly thresholding the SWT coefficients without any need for reconstructing the denoised signal followed by thresholding technique as in the first method is presented. In chapter 4 a spike detection method based on multiresolution Teager Energy Operator is discussed. The Teager Energy Operator (TEO) picks up localized spikes in signal energy and thus is directly used for spike detection in many applications including R wave detection in ECG and various (alpha, beta) rhythms in EEG. Some basic properties of the TEO are discussed followed by the need for a multiresolution approach to TEO and the methods existing in literature. The wavelet decomposition and the subsampled signal involved at each level naturally lends it to a multiresolution TEO framework at the same time significantly reducing the computational complexity due the subsampled signal at each level. A wavelet-TEO algorithm for spike detection with similar accuracies as the previous two algorithms is proposed. The method proposed here differs significantly from that in literature since wavelets are used instead of time domain processing. Chapter 5 describes the method of evaluation of the three algorithms proposed in the previous chapters. The spike templates are obtained from MEA recordings, resampled and normalized for use in spike trains simulated as Poisson processes. The noise is modeled as colored autoregressive (AR) of order 5, i.e AR(5), as well as Additive White Gaussian Noise (AWGN). The noise in most human and animal MEA recordings conforms to the autoregressive model with orders of around 5. The AWGN Noise model is used in most spike detection methods in the literature. The performance of the proposed three wavelet based algorithms is measured in terms of the accuracy of spike detection, percentage of false positives and the computational complexity for different types of wavelet families. The optimal wavelet for this purpose is then chosen from the wavelet family which gives the best results. Also, optimal levels of decomposition and threshold factors are chosen while maintaining a balance between accuracy and false positives. The algorithms are then tested for performance under different levels of SNR with the noise modeled as AR(5) or AWGN. The proposed wavelet based algorithms exhibit a detection accuracy of approximately 90% at a low SNR of 2.35 dB with the false positives below 5%. This constitutes a significant improvement over the results in existing literature which claim an accuracy of 80% with false positives of nearly 10%. As the SNR increases, the detection accuracy increases to close to 100% and the false alarm rate falls to 0. Chapter 6 summarizes the work. A comparison is made between the three proposed algorithms in terms of detection accuracy and false positives. Directions in which future work may be carried out are suggested.

The main goal of the work presented in this thesis is to improve the reliability of existing methods for speciation analysis of organotin compounds Species-specific isotope dilution (SSID) calibration in combination with gas chromatography – inductively coupled plasma mass spectrometry was used to investigate the transformation of phenyltin species during sample preparation. Isotope-enriched phenyltin species were synthesized from corresponding isotope-enriched tin metals. SSID with a mixture of phenyltin species (PhTs) from one isotope was used to evaluate different extraction procedures for the determination of PhTs in fresh water sediment. Preparative liquid chromatography was used to produce single isotope-enriched phenyltin species making a multi-isotope spike (MI) SSID calibration possible. Different extraction procedures for the analysis of phenyltin species in biological samples were evaluated by applying MI-SSID. Degradation of TPhT and DPhT during sample extraction was observed and quantified. Accurate results were therefore obtained. A sample preparation procedure using mild extraction conditions with reasonable recoveries is described. The stability of organotin standards was investigated under different storage conditions. Mono- and diphenyltin were found to be redistributed and degraded during storage in methanol but were stabilized in sodium acetate/ acetic acid. A fast redistribution between monobutyl- and diphenyl tin has been observed and therefore it is therefore recommended that standards be derivatized as soon as possible after butyl- and phenyltin standards are mixed. Included in the thesis is also an investigation of the analytical potential of using instrumentation based on atomic absorption spectrometry (AAS) for speciation analysis of organotin compounds. The method was based on gas chromatographic separation, atomization in a quartz tube and detection by line source (LS) AAS and for comparison, by state of the art continuum source (CS) AAS. Analytical performances of CSAAS system were found to be better compared to LSAAS.

L’intéraction entre la biologie et l’électronique est une discpline en pleine essort. De nom-breux systèmes électroniques tentent de s’interconnecter avec des tissus ou des cellules vivantesafin de décoder l’information biologique. Le Potentiel d’action (PA) est au coeur de codagebiologique et par conséquent il est nécéssaire de pouvoir les repérer sur tout type de signal bio-logique. Par conséquent, nous étudions dans ce manuscrit la possibilité de concevoir un circuitélectronique couplé à un système de microélectrodes capable d’effectuer une acquisition, unedétection des PAs et un enregistrement des signaux biologiques. Que ce soit en milieu bruitéou non, nous considérons le taux de détection de PA et la contrainte de temps réel commedes notions primordiales et la consommation en silicium comme un prix à payer. Initialementdéveloppés pour l’étude de signaux neuronaux et pancréatiques, ces systèmes conviennent par-faitement pour d’autres type de cellules. / Interaction between biology and electronic is in expansion. Many electronic systems aretrying to interconnect with tissues or living cells to decode biological information. The ActionPotential (AP) is the heart of biological coding and therefore it is necessary to be able to locateit from any type of biological signal. Therefore, we study in this manuscript the possibility ofdesigning an electronic circuit coupled to microelectrodes capable of acquisition, detection ofPAs and recording of biological signals. Whether or not in a noisy environment, we consider thedetection rate of PA and the real time-computing constraint as an hard specificationand andsilicon area as a price to pay. Initially developed for the study of neural signals and pancreatic,these systems are ideal for other types of cells.

Spiking neural networks using Leaky-Integrate-and-Fire (LIF) neurons and Spike-timing-depend Plasticity (STDP) learning, are commonly used as more biological possible networks. Compare to DNNs and RNNs, the LIF-STDP networks are models which are closer to the biological cortex. LIF-STDP neurons use spikes to communicate with each other, and they learn through the correlation among these pre- and post-synaptic spikes. Simulation of such networks usually requires high-performance supercomputers which are almost all based on von Neumann architecture that separates storage and computation. In von Neumann architecture solutions, memory access is the bottleneck even for highly optimized Application-Specific Integrated Circuits (ASICs). In this thesis, we propose an optimization method that can reduce the memory access cost by avoiding a dual-access pattern. In LIF-STDP networks, the weights usually are stored in the form of a two-dimensional matrix. Pre- and post-synaptic spikes trigger row and column access correspondingly. But this dual-access pattern is very costly for DRAM. We eliminate the column access by introducing a post-synaptic buffer and an approximation function. The post-synaptic spikes are recorded in the buffer and are processed at pre-synaptic spikes together with the row updates. This column update elimination method will introduce errors due to the limited buffer size. In our error analysis, the experiments show that the probability of introducing intolerable errors can be bounded to a very small number with proper buffer size and approximation function. We also present a performance analysis of the Column Update Elimination (CUE) optimization. The error analysis of the column updates elimination method is the main contribution of our work. / Spikande neurala nätverk som använder LIF-neuroner och STDP-inlärning, används vanligtvis som ett mer biologiskt möjligt nätverk. Jämfört med DNN och RNN är LIF-STDP-nätverken modeller närmare den biologiska cortex. LIFSTDP-neuroner använder spikar för att kommunicera med varandra, och de lär sig genom korrelationen mellan dessa pre- och postsynaptiska spikar. Simulering av sådana nätverk kräver vanligtvis högpresterande superdatorer som nästan alla är baserade på von Neumann-arkitektur som separerar lagring och beräkning. I von Neumanns arkitekturlösningar är minnesåtkomst flaskhalsen även för högt optimerade Application-Specific Integrated Circuits (ASIC). I denna avhandling föreslår vi en optimeringsmetod som kan minska kostnaden för minnesåtkomst genom att undvika ett dubbelåtkomstmönster. I LIF-STDPnätverk lagras vikterna vanligtvis i form av en tvådimensionell matris. Preoch postsynaptiska toppar kommer att utlösa rad- och kolumnåtkomst på motsvarande sätt. Men detta mönster med dubbel åtkomst är mycket dyrt i DRAM. Vi eliminerar kolumnåtkomsten genom att införa en postsynaptisk buffert och en approximationsfunktion. De postsynaptiska topparna registreras i bufferten och bearbetas vid presynaptiska toppar tillsammans med raduppdateringarna. Denna metod för eliminering av kolumnuppdatering kommer att introducera fel på grund av den begränsade buffertstorleken. I vår felanalys visar experimenten att sannolikheten för att införa oacceptabla fel kan begränsas till ett mycket litet antal med korrekt buffertstorlek och approximationsfunktion. Vi presenterar också en prestandaanalys av CUE-optimeringen. Felanalysen av elimineringsmetoden för kolumnuppdateringar är det huvudsakliga bidraget från vårt arbete

Hebbian learning based neural network learning rules when implemented on hardware, store their synaptic weights in the form of a two-dimensional matrix. The storage of synaptic weights demands large memory bandwidth and storage. While memory units are optimized for only row-wise memory access, Hebbian learning rules, like the spike-timing dependent plasticity, demand both row and column-wise access of memory. This dual pattern of memory access accounts for the dominant cost in terms of latency as well as energy for realization of large scale spiking neural networks in hardware. In order to reduce the memory access cost in Hebbian learning rules, a Column Update Elimination optimization has been previously implemented, with great efficacy, on the Bayesian Confidence Propagation neural network, that faces a similar challenge of dual pattern memory access. This thesis explores the possibility of extending the column update elimination optimization to spike-timing dependent plasticity, by simulating the learning rule on a two layer network of leaky integrate-and-fire neurons on an image classification task. The spike times are recorded for each neuron in the network, to derive a suitable probability distribution function for spike rates per neuron. This is then used to derive an ideal postsynaptic spike history buffer size for the given algorithm. The associated memory access reductions are analysed based on data to assess feasibility of the optimization to the learning rule. / Hebbiansk inlärning baserat på neural nätverks inlärnings regler används vid implementering på hårdvara, de lagrar deras synaptiska vikter i form av en tvådimensionell matris. Lagringen av synaptiska vikter kräver stor bandbredds minne och lagring. Medan minnesenheter endast är optimerade för radvis minnesåtkomst. Hebbianska inlärnings regler kräver som spike-timing-beroende plasticitet, både rad- och kolumnvis åtkomst av minnet. Det dubbla mönstret av minnes åtkomsten står för den dominerande kostnaden i form av fördröjning såväl som energi för realiseringen av storskaliga spikande neurala nätverk i hårdvara. För att minska kostnaden för minnesåtkomst i hebbianska inlärnings regler har en Column Update Elimination-optimering tidigare implementerats, med god effektivitet på Bayesian Confidence Propagation neurala nätverket, som står inför en liknande utmaning med dubbel mönster minnesåtkomst. Denna avhandling undersöker möjligheten att utöka ColumnUpdate Elimination-optimeringen till spike-timing-beroende plasticitet. Detta genom att simulera inlärnings regeln på ett tvålagers nätverk av läckande integrera-och-avfyra neuroner på en bild klassificerings uppgift. Spike tiderna registreras för varje neuron i nätverket för att erhålla en lämplig sannolikhetsfördelning funktion för frekvensen av toppar per neuron. Detta används sedan för att erhålla en idealisk postsynaptisk spike historisk buffertstorlek för den angivna algoritmen. De associerade minnesåtkomst minskningarna analyseras baserat på data för att bedöma genomförbarheten av optimeringen av inlärnings regeln.

Dans cette thèse je propose plusieurs mécanismes de plasticité synaptique qui pourraient expliquer la rapidité, la sélectivité et l'invariance des réponses neuronales dans le cortex visuel. Leur plausibilité biologique est discutée. J'expose également les résultats d'une expérience de psychophysique pertinente, qui montrent que la familiarité peut accélérer les traitements visuels. Au delà de ces résultats propres au système visuel, les travaux présentés ici créditent l'hypothèse de l'utilisation des dates de spikes pour encoder, décoder, et traiter l'information dans le cerveau – c'est la théorie dite du ‘codage temporel'. Dans un tel cadre, la Spike Timing Dependent Plasticity pourrait jouer un rôle clef, en détectant des patterns de spikes répétitifs et en permettant d'y répondre de plus en plus rapidement.