Les explorations scientifiques menées depuis une quarantaine d’années ont permis d'identifier la diversité et la complexité des processus géologiques et géochimiques conduisant à la concentration des métaux dans les grands fonds océaniques. Les dépôts métallifères riches en hydroxydes de Fer et de Manganèse, tels que les encroûtements hydrogénétiques et hydrothermaux et les nodules polymétalliques, présentent des enrichissements variés en éléments d'intérêts économiques tels que le Ni, Cu, Co, Te, Pt et les Terres Rares. Bien que la minéralogie et la géochimie de ces dépôts aient été largement étudiées dans la littérature, les sources de métaux restent encore mal déterminées. Par conséquent, comprendre la géochimie de ces dépôts implique d’une part, de connaître les processus participant à leur genèse, et d’autre part d’avoir de meilleures connaissances sur les sources impliquées (par ex. flux continental et hydrothermal) et leur importance dans les grands cycles biogéochimiques des métaux dans les océans. Afin d’apporter de nouveaux éléments de réponse, notre approche a consisté à utiliser les compositions isotopiques des métaux comme traceurs biogéochimiques. Ce projet est structuré autour de deux hypothèses, (1) le développement et l’utilisation d’un nouveau outil géochimique que sont les isotopes du Ni pour tracer les sources et les processus d’enrichissements en métaux dans les dépôts métallifères océaniques ; (2) la combinaison de plusieurs systèmes isotopiques tels que Fe, Pb, Cu et Zn (et Ni) dans les encroûtements de fer-manganèse comme proxy de la composition isotopique de l’eau de mer profonde. Après avoir développé une méthode d’analyse des isotopes du Ni par MC-ICP-MS et estimé la variabilité isotopique du Ni dans les systèmes naturels par la caractérisation des grands réservoirs terrestres, nous avons évalué expérimentalement le fractionnementisotopique du Ni lors de son adsorption sur les oxyhydroxydes de Fe et Mn comme analogue à ce qui pourrait se produire dans les dépôts de Fe-Mn naturels. Les résultats indiquent que lors de l’adsorption du Ni, la phase solide est enrichie en isotopes légers par rapport à la solution avec des facteurs de fractionnement (Δ60/58Nimin/sol) variant de -1 ‰ pour la birnessite, -0.9 ‰ pour la goethite et -0.4 ‰ pour la ferrihydrite. A partir de ces résultats et d’autres études récentes, nous avons pu appuyer l’hypothèse selon laquelle d’un point de vue global la variabilité isotopique du Ni dans les dépôts métallifères océaniques riches en Fe et Mn s’explique par des processus d’enrichissement et de formation lors de l’incorporation des métaux dans les phases minérales de Fe et Mn plutôt que par des variations des compositions isotopiques des sources. Ainsi les encroûtements hydrogénétiques formés lentement à partir de l’eau de mer ne montrent pas de fractionnement isotopique du Ni, tandis que les dépôts hydrothermaux formés par des processus rapides liés aux apports hydrothermaux montrent des fractionnements du Ni plus importants. Puis, afin d’évaluer la possibilité d’utiliser les signatures isotopiques du Ni comme nouveaux traceurs paléocéanographiques, nous avons mené une étude comparative sur des encroûtements collectés dans le Pacifique Nord (proche de Hawaii) et le Pacifique Sud (proche de Tahiti). Dans ce contexte, les ncroûtements de fer-manganèse formés par précipitation très lente de l’ordre de quelques mm/Ma entre 1500 et 3000 m de profondeur, fournissent un enregistrement de plusieurs millions d’années des métaux dissous dans l’eau de mer. Après avoir réalisé une étude minéralogique et géochimique (éléments majeurs et traces) et calibré les taux de croissance des encroûtements, nous avons mesuré pour la première fois les compositions isotopiques du Ni, Fe, Zn, Cu et Pb sur la même série temporelle. / Scientific explorations implemented for around forty years allow to identifying the diversity and the complexity of geological and geochemical processes conducting to metals concentration on the deep seafloor. Fe- and Mn-rich metalliferous deposits such as hydrogenetic and hydrothermal ferromanganese (Fe-Mn) crusts and polymetallic nodules, present various enrichment in elements of economic interests like Ni, Cu, Co, Te, Pt and Rare Earth Elements. Although the mineralogy and geochemistry of these deposits have been largely studied in the literature, metal sources remain poorly determined. Accordingly, understanding the geochemistry of these deposits implies to know which processes are involved in their formation but also to have a better knowledge of the sources (e.g. the continental and hydrothermal fluxes) and their importance in the global oceanic metal biogeochemical cycles. In order to fill this gap, our approach consisted in using metal stable isotope compositions as biogeochemical tracers. This project is organized around two hypotheses, (1) development and utilization of a new geochemical tool, namely Ni isotopes, for tracing metal enrichment sources and processes in oceanic metalliferous deposits; (2) combination of several isotope systematics such as Fe, Pb, Cu, Zn (and Ni) in Fe-Mn crusts as proxies of the deep seawater isotope composition. Upon developing an analytical method for measuring Ni isotopes by MC-ICP-MS and estimating the Ni isotopes variability in natural systems through the characterization of terrestrial reservoirs, we experimentally evaluated Ni isotope fractionation during adsorption on Fe- and Mn-oxyhydroxides since similar processes may potentially occur in natural Fe-Mn deposits. Results indicate that after Ni adsorption, the solid phase is enriched in light Ni isotopes relatively to the solution with fractionation factors (Δ60/58Nimin/sol) varying from -1 ‰ for birnessite, -0.9 ‰ for goethite and -0.4 ‰ for ferrihydrite. These results, and other recent studies, strengthen our hypothesis according to which Ni isotopes variability in Fe- and Mn-rich metalliferous deposits can be explained by enrichment and formation processes during metal incorporation in Fe and Mn mineral phases rather than variations in the isotopic composition of the sources. Thus, hydrogenetic Fe-Mn crusts formed slowly from seawater dissolved metals do not show significant Ni isotope fractionation, whereas hydrothermal deposits formed by relatively rapid processes as a result of hydrothermal inputs exhibit important Ni isotope fractionation.

D’après le postulat de Hebb, les circuits neuronaux ajustent et modifient durablement leurs poids synaptiques en fonction des patrons de décharges de part et d’autre de la synapse. La « spike-timing-dependent plasticity » (STDP) est une règle d’apprentissage synaptique hebbienne dépendante de la séquence temporelle précise (de l’ordre de la milliseconde) des activités appariées des neurones pré- et post-synaptiques. Le striatum, le principal noyau d’entrée des ganglions de la base, reçoit des afférences excitatrices provenant du cortex cérébral et du thalamus dont les activités peuvent être concomitantes ou décalées dans le temps. Ainsi, l’encodage temporal des informations corticales et thalamiques via la STDP pourrait être crucial pour l’implication du striatum dans l’apprentissage procédural. Nous avons exploré les plasticités synaptiques cortico- et thalamo-striatales puis leurs interactions à travers le paradigme de la STDP. Les principaux résultats sont :1. Les « spike-timing-dependent plasticity » opposées cortico-striatales et thalamo-striatales induisent des plasticités hétérosynaptiques. Si la très grande majorité des études sont consacrées à la plasticité synaptique cortico-striatale, peu ont exploré les règles de plasticité synaptique aux synapses thalamo-striatale et leurs interactions avec la plasticité cortico-striatale. Nous avons étudié la STDP thalamo-striatale et comment les plasticités synaptiques thalamo- et cortico-striatales interagissent… / According to Hebbian postulate, neural circuits tune their synaptic weights depending on patterned firing of action potential on either side of the synapse. Spike-timing-dependent plasticity (STDP) is an experimental implementation of Hebbian plasticity that relies on the precise order and the millisecond timing of the paired activities in pre- and postsynaptic neurons. The striatum, the primary entrance to basal ganglia, integrates excitatory inputs from both cerebral cortex and thalamus whose activities can be concomitant or delayed. Thus, temporal coding of cortical and thalamic information via STDP paradigm may be crucial for the role of the striatum in procedural learning. Here, we explored cortico-striatal and thalamo-striatal synaptic plasticity and their interplay through STDP paradigm. The main results described here are:1. Opposing spike-timing dependent plasticity at cortical and thalamic inputs drive heterosynaptic plasticity in striatumIf the vast majority of the studies focused on cortico-striatal synaptic plasticity, much less is known about thalamo-striatal plasticity rules and their interplay with cortico-striatal plasticity. Here, we explored thalamo-striatal STDP and how thalamo-striatal and cortico-striatal synaptic plasticity interplay. a) While bidirectional and anti-Hebbian STDP was observed at cortico-striatal synapses, thalamo-striatal exhibited bidirectional and hebbian STDP...

We study two methods of data analysis which are common tools for the analysis of neuronal data. In particular, we examine how causal interactions between brain regions can be investigated using time series reflecting the neural activity in these regions. Furthermore, we analyze a method used to study the neural code that evaluates the discrimination of the responses of single neurons elicited by different stimuli. This discrimination analysis is based on the quantification of the similarity of the spike trains with time scale parametric spike train distances. In each case we describe the methods used for the analysis of the neuronal data and we characterize their specificity using simulated or exemplary experimental data. Taking into account our results, we comment the previous studies in which the methods have been applied. In particular, we focus on the interpretation of the statistical measures in terms of underlying neuronal causal connectivity and properties of the neural code, respectively. / Estudiem dos mètodes d'anàlisi de dades que són eines habituals per a l'anàlisi de dades neuronals. Concretament, examinem la manera en què les interaccions causals entre regions del cervell poden ser investigades a partir de sèries temporals que reflecteixen l'activitat neuronal d'aquestes regions. A més a més, analitzem un mètode emprat per estudiar el codi neuronal que avalua la discriminació de les respostes de neurones individuals provocades per diferents estímuls. Aquesta anàlisi de la discriminació es basa en la quantificació de la similitud de les seqüències de potencials d'acció amb distàncies amb un paràmetre d'escala temporal. Tenint en compte els nostres resultats, comentem els estudis previs en els quals aquests mètodes han estat aplicats. Concretament, ens centrem en la interpretació de les mesures estadístiques en termes de connectivitat causal neuronal subjacent i propietats del codi neuronal, respectivament.

A aquisição experimental de sinais neuronais é um dos principais avanços da neurociência. Por meio de observações da corrente e do potencial elétricos em uma região cerebral, é possível entender os processos fisiológicos envolvidos na geração do potencial de ação, e produzir modelos matemáticos capazes de simular o comportamento de uma célula neuronal. Uma prática comum nesse tipo de experimento é obter leituras a partir de um arranjo de eletrodos posicionado em um meio compartilhado por diversos neurônios, o que resulta em uma mistura de sinais neuronais em uma mesma série temporal. Este trabalho propõe um modelo linear de tempo discreto para o sinal produzido durante o disparo do neurônio. Os coeficientes desse modelo são calculados utilizando-se amostras reais dos sinais neuronais obtidas in vivo. O processo de modelagem concebido emprega técnicas de identificação de sistemas e processamento de sinais, e é dissociado de considerações sobre o funcionamento biofísico da célula, fornecendo uma alternativa de baixa complexidade para a modelagem do disparo neuronal. Além disso, a representação por meio de sistemas lineares permite idealizar um sistema inverso, cuja função é recuperar o sinal original de cada neurônio ativo em uma mistura extracelular. Nesse contexto, são discutidas algumas soluções baseadas em filtros adaptativos para a simulação do sistema inverso, introduzindo uma nova abordagem para o problema de separação de spikes neuronais. / The experimental acquisition of neuronal signals is a major advance in neuroscience. Through observations of electric current and potential in a brain region, it is possible to understand the physiological processes involved in the action potential generation, and create mathematical models capable of simulating the behavior of the neuronal cell. A common practice in this kind of experiment is to obtain readings from an array of electrodes positioned in a medium shared by several neurons, which results in a mixture of neuronal signals in the same time series. This work proposes a discrete-time linear model of the neuronal signal during the firing of the cell. The coefficients of this model are estimated using real samples of the neuronal signals obtained in vivo. The conceived modeling process employs system identification and signal processing concepts, and is dissociated from any considerations about the biophysical function of the neuronal cell, providing a low-complexity alternative to model the neuronal spike. In addition, the use of a linear representation allows the idealization of an inverse system, whose main purpose is to recover the original signal of each active neuron in a given extracellular mixture. In this context, some solutions based on adaptive filters are discussed for the inverse model simulation, introducing a new approach to the problem of neuronal spike separation.

The brain is a complex organ that is computationally strong. Recent research in the field of neurobiology help scientists to better understand the working of the brain, especially how the brain represents or codes external signals. The research shows that the neural code is sparse. A sparse code is a code in which few neurons participate in the representation of a signal. Neurons communicate with each other by sending pulses or spikes at certain times. The spikes send between several neurons over time is called a spike train. A spike train contains all the important information about the signal that it codes. This thesis shows how sparse coding can be used to do speech recognition. The recognition process consists of three parts. First the speech signal is transformed into a spectrogram. Thereafter a sparse code to represent the spectrogram is found. The spectrogram serves as the input to a linear generative model. The output of themodel is a sparse code that can be interpreted as a spike train. Lastly a spike train model recognises the words that are encoded in the spike train. The algorithms that search for sparse codes to represent signals require many computations. We therefore propose an algorithm that is more efficient than current algorithms. The algorithm makes it possible to find sparse codes in reasonable time if the spectrogram is fairly coarse. The system achieves a word error rate of 19% with a coarse spectrogram, while a system based on Hidden Markov Models achieves a word error rate of 15% on the same spectrograms. / Thesis (PhD)--University of Pretoria, 2008. / Electrical, Electronic and Computer Engineering / unrestricted

Electrical activity is omnipresent throughout the brain, and it varies dependant on the brain region. Areal hierarchy has been suggested to be one of the main principles of the organization of the brain, but there is not a lot of evidence available related to the specialization of the brain’s regions in the temporal domain, that is, how the activity evolves over time. It has been suggested that there is a relationship between spatial location and timescale [1] and that the timescales of neuronal activity in rodents change according to the hierarchical position (derived from anatomical connectivity measurements) of the brain region [2]. Timescale is related to to the capability of a neuron to maintain the same firing rate over a time period. This firing rate can be measured as decay time constant of an auto-correlation matrix of spiking activity, referred to as the timescale of a single neuron [3]. In this thesis, timescales of spontaneous brain activity were measured in eight regions of the mouse prefrontal cortex (PFC) (data obtained in the Carlén Laboratory) and compared to the timescales of eight visual areas (Neuropixels Visual Coding dataset, Allen Institute for Brain Science) [4]. The results showed that cortical regions hold varying timescales, but that there is no clear correspondence of the timescales of spontaneous activity to the anatomical hierarchies. Instead, we show that the PFC regions have a greater variability in their respective timescales compared to visual cortical regions. The analysis was done using two different approaches, where for some regions the measured timescales significantly differs, due to the difference in the use of the magnitudes of the correlation. This work highlights how neuronal timescales measurements can be approached in cortical regions and used for the future work investigating their functional role and the mechanisms of generation of distinct neuronal timescales in the brain. / Elektrisk aktivitet är allestädes närvarande i hela hjärnan, och den varierar beroende på hjärnregionen. Arealhierarki har föreslagits vara en av huvudprinciperna för hjärnans organisation, men det finns inte mycket bevis tillgängligt relaterat till specialiseringen av hjärnans regioner i den temporala domänen, det vill säga hur aktiviteten utvecklas över tiden . Det har föreslagits att det finns ett samband mellan rumslig plats och tidsskala [1] och att tidsskalorna för neuronal aktivitet hos gnagare ändras beroende på den hierarkiska positionen (härledd från anatomiska anslutningsmätningar) i hjärnregionen [2]. Tidsskala är relaterat till förmågan hos ett neuron att bibehålla samma fyrningshastighet under en tidsperiod. Denna avfyrningshastighet kan mätas som fallstidskonstant för en autokorrelationsmatris av spikaktivitet, kallad tidsskalan för en enda neuron [3]. I denna avhandling mättes tidsskalor för spontan hjärnaktivitet i åtta regioner i musens prefrontala kortex (PFC) (data erhållen av Carlén Laboratory) och jämfört med tidsskalorna för åtta visuella områden (Neuropixels Visual Coding dataset, Allen Institute for Brain Science) [4]. Resultaten visade att kortikala regioner har olika tidsskalor, men att det inte finns någon tydlig överensstämmelse mellan tidsskalorna för spontan aktivitet med de anatomiska hierarkierna. Istället visar vi att PFC-regionerna har större variation i sina respektive tidsskalor jämfört med visuella kortikala regioner. Analysen gjordes med hjälp av två olika tillvägagångssätt, där de uppmätta tidsskalorna för vissa regioner skiljer sig avsevärt på grund av skillnaden i användning av storleken på korrelationen. Detta arbete belyser hur neuronala tidsskalemätningar kan beaktas i kortikala regioner och användas för det framtida arbetet med att undersöka deras funktionella roll och mekanismerna för generering av distinkta neuronala tidsskalor i hjärnan.

Analyzing the brain's behavior in terms of its neuronal activity is the fundamental purpose of Brain-Machine Interfaces (BMIs). Neuronal activity is often assumed to be encoded in the rate of neuronal action potential spikes. Successful performance of a BMI system is tied to the efficiency of its individual processing elements such as spike detection, sorting and decoding. To achieve reliable operation, BMIs are equipped with hundreds of electrodes at the neural interface. While a single electrode/tetrode communicates with up to four neurons at a given instant of time, a typical interface communicates with an ensemble of hundreds or even thousands of neurons. However, translation of these signals (data) into usable information for real-time BMIs is bottlenecked due to the lack of efficient real-time algorithms and real-time hardware that can handle massively parallel channels of neural data. The research presented here addresses this issue by developing real-time neural processing algorithms that can be implemented in reconfigurable hardware and thus, can be scaled to handle thousands of channels in parallel. The developed reconfigurable system serves as an evaluation platform for investigating the fundamental design tradeoffs in allocating finite hardware resources for a reliable BMI. In this work, the generic architectural layout needed to process neural signals in a massive scale is discussed. A System-on-Chip design with embedded system architecture is presented for FPGA hardware realization that features (a) scalability (b) reconfigurability, and (c) real-time operability. A prototype design incorporating a dual processor system and essential neural signal processing routines such as real-time spike detection and sorting is presented. Two kinds of spike detectors, a simple threshold-based and non-linear energy operator-based, were implemented. To achieve real-time spike sorting, a fuzzy logic-based spike sorter was developed and synthesized in the hardware. Furthermore, a real-time kernel to monitor the high-level interactions of the system was implemented. The entire system was realized in a platform FPGA (Xilinx Virtex-5 LX110T). The system was tested using extracellular neural recordings from three different animals, a owl monkey, a macaque and a rat. Operational performance of the system is demonstrated for a 300 channel neural interface. Scaling the system to 900 channels is trivial. / Electrical and Computer Engineering

A terrestrial robotic electrophysiology platform has been developed that can hold a moth (<italic>Manduca sexta</italic>), record signals from its brain or muscles, and use these signals to control the rotation of the robot. All signal processing (electrophysiology, spike detection, and robotic control) was performed onboard the robot with custom designed electronic circuits. Wireless telemetry allowed remote communication with the robot. In this study, we interfaced directionally-sensitive visual neurons and pleurodorsal steering muscles of the mesothorax with the robot and used the spike rate of these signals to control its rotation, thereby emulating the classical optomotor response known from studies of the fly visual system. The interfacing of insect and machine can contribute to our understanding of the neurobiological processes underlying behavior and also suggest promising advancements in biosensors and human brain-machine interfaces.

The activities of pairs of mammalian motor neurons (MNs) receiving varying degrees of common excitatory synaptic input were simulated to study the relationship between nearly-coincident spiking and common excitatory drive. The somatic membrane potential of each MN was modeled using a single compartment model. Each MN was modeled toreceive synaptic contacts from hundreds of pre-synaptic fibers. The percentage of pre-synaptic fibers that diverged to supply both MNs of a pair with common synaptic input could be varied from 0 (no common inputs) to 100% (all common inputs). Spikes trains on separate re-synaptic fibers were independent of one another and were modeled as realizations of renewal processes with mean firing rates (10 - 50Hz) resembling that associated with supra-spinal input. Maximum synaptic conductances and time constants were varied across synapsesto match experimentally observed somatic EPSPs. The number of active pre-synaptic fibers to each MN was adjusted in order that the firingrates of MNs were between 8 and 15 Hz. For each common input condition, 100 s of concurrent spiking activity of the MNs was usedto construct cross-correlation histograms. The sizes of the central peaks in the histograms were quantified using both the k' (Ellaway and Murthy 1985) and CIS (Nordstrom et al. 1992) indices ofsynchrony. Monotonically increasing linear relationships between the proportion of common synaptic input and the magnitude of synchronywere observed for both indices. For example, the model predicted that 10% common input would yield a CIS value of 0.27 whereas 100% commoninput would yield a CIS value of 1.5. These values are within the range of values observed experimentally. These results, therefore,provide a means to translate measures of nearly-coincident spiking into plausible renditions of synaptic connectivity.

Humans and animals can plan and execute movements much more adaptably and reliably than current computers can calculate robotic limb trajectories. Over recent decades, it has been suggested that our brains use motor primitives as blocks to build up movements. In broad terms a primitive is a segment of pre-optimised movement allowing a simplified movement planning solution. This thesis explores a generative model of handwriting based upon the concept of motor primitives. Unlike most primitive extraction studies, the primitives here are time extended blocks that are superimposed with character specific offsets to create a pen trajectory. This thesis shows how handwriting can be represented using a simple fixed function superposition model, where the variation in the handwriting arises from timing variation in the onset of the functions. Furthermore, it is shown how handwriting style variations could be due to primitive function differences between individuals, and how the timing code could provide a style invariant representation of the handwriting. The spike timing representation of the pen movements provides an extremely compact code, which could resemble internal spiking neural representations in the brain. The model proposes an novel way to infer primitives in data, and the proposed formalised probabilistic model allows informative priors to be introduced providing a more accurate inference of primitive shape and timing.