While motor neuron activity has been studied for many decades, the relative contribution of synaptic and non-synaptic mechanisms underlying this activity during natural behaviors is not well understood. Thus, the goal of this dissertation was to further understand the role of non-synaptic properties of motor neurons during voluntary activity. In particular, I considered three non-synaptic properties: persistent inward currents (PICs) that boost synaptic inputs, spike-threshold accommodation that affects recruitment threshold as excitation rates of rise slow, and spike-frequency adaptation that leads to a decrease in firing rate despite constant excitation levels. Computer simulations were employed to understand the potential effect that these properties could have on firing rate behavior. In particular, the focus was on paired motor unit recordings where a lower threshold motor unit’s firing rate served as a proxy for synaptic drive, and differences in firing rate (ΔF) were compared at a higher threshold unit’s recruitment and derecruitment. While ΔF has been used by others to estimate PIC activation, the simulation results indicated that each of these non-synaptic mechanisms could lead to positive ΔF. Furthermore, by varying contraction speed and duration it seemed possible to determine which property contributes to ΔF in vivo. The results from human experiments indicated that adaptation is most likely the predominant contributor to ΔF during natural behaviors. Additionally, positive ΔF was even observed in the genioglossus muscle of the tongue, where the role of PICs has been debated. These results suggested that ΔF may not the best method to detect PICs during natural behaviors. As such, I also considered whether there might be another metric to infer PIC activation during natural behaviors. Motor unit firing rates tend to plateau, or saturate, despite continued force increase, and one hypothesis is that PICs contribute to this behavior. Indeed, motor unit firing rate saturation was diminished by the addition of inhibition, which should have limited PIC activation. Therefore, this final study provided possible evidence for PIC activation during natural behaviors. Overall, this dissertation highlights that non-synaptic properties of motor neurons are activated during natural behaviors and that they contribute significantly to firing rate output.

Les propriétés géochimiques particulières du molybdène vis-à-vis du potentiel d’oxygénation en milieu aqueux en font un élément particulièrement utilisé pour déterminer le degré d’oxygénation du milieu dans lequel un dépôt a sédimenté. Dans un premier temps, ce travail de thèse présente une alternative aux méthodes d’extraction du molybdène utilisées jusqu’ici. Les principales méthodes de purification couramment utilisées sur échantillons géologiques se décomposent en deux phases : une purification au travers d’une résine anionique, puis une purification au travers d’une résine cationique. La méthode proposée ici, initialement prévue pour permettre des analyses en Sample-Standard-Bracketing (SSB), permet d’isoler quantitativement le molybdène du reste de la matrice géologique en un seul passage sur résine anionique. La purification s’avère également suffisamment efficace pour effectuer des analyses en utilisant la méthode du double-spike. Une comparaison de ces deux méthodes de correction du fractionnement instrumental a permis d’évaluer les limites de l’utilisation du SSB par rapport au double-spike. Dans un second temps, cette chimie a été appliquée dans le cadre d’une étude géochimique approfondie d’un gisement fossilifère extrêmement riche: le Lagerstätte de Sirius Passet situé au nord du Groenland. Associée aux critères paléontologiques, cette étude indique que ces sédiments se sont déposés dans un milieu au moins partiellement oxygéné, sous forme de boues sédimentaires très peu propices aux échanges avec la colonne d’eau. Le contexte paléogéographique, les analyses chimiques et les observations microscopiques suggèrent que ces boues sont principalement d’origine éolienne. / The particular behaviour of molybdenum towards the oxygenation potential in aqueous environments makes this element very useful to infer the oxygenation level under which a sediment is deposited. The first part of this PhD thesis is dedicated to the development of a new protocol for the extraction of molybdenum from geological samples. Until now, most extractions are performed using two ion exchange resin columns: an anionic one, then a cationic one. The protocol proposed here, initially set to allow measurements using a classical Sample-Standard-Bracketing (SSB) method, allows good purification and recovery of molybdenum through a single pass in an anion exchange resin. This purification is good enough to perform analyses using the double-spike method. A comparison between these two methods to correct the instrumental mass bias allowed us to better constrain SSB limits versus the double-spike method. The second part of this work aimed at geochemically characterizing an extraordinary fossil assemblage deposit located in North Greenland: the Sirius Passet Lagerstätte. In association with paleontological criteria, this study shows that these sediments were deposited in an environment at least partially oxygenated, in the form of muds enabling little exchange with the water column. The paleogeographical context, chemical analyses and thin-section observations suggest that these muds are mainly aeolian in origin.

O registro de sinais neurais através de matrizes de microeletrodos implantáveis no meio extracelular do córtex cerebral tem-se tornado um paradigma experimental para a neurociência. Por outro lado, a pesquisa recente sobre neuropróteses motoras tem mostrado que é possível decodificar comandos motores a partir dos sinais registrados no meio extracelular do córtex cerebral. Em ambos os contextos, neurociência experimental e desenvolvimento de neuropróteses motoras, um dos aspectos encontrados no estado da arte ´e a utilização de circuitos integrados (chips) implantados no cérebro. Nesses chips, os sinais neurais medidos com os microeletrodos são amplificados, filtrados, processados e transmitidos a um computador externo mediante fios que atravessam o crânio. Existe o interesse em desenvolver chips implantáveis que transmitam os sinais ao computador externo sem a necessidade de fios que atravessem o crânio. Na pesquisa do estado da arte tem-se encontrado a utilização de tais chips implantáveis sem fio em ratos e macacos, porém até a data da elaboração deste texto não foram encontrados relatos da aplicação em humanos. Um dos aspectos que deve se levar em consideração no desenvolvimento de interfaces neurais implantáveis sem fio é a largura de banda do canal de comunicação. Quanto maior a quantidade de dados a serem transmitidos, maior a largura de banda necessária e maior o aquecimento do chip devido à dissipação de potência. Esta tese aborda sistemas de processamento de sinais neurais extracelulares que tem como objetivo reduzir a quantidade de dados a serem transmitidos e assim viabilizar a transmissão sem fio. Para poder ser integrados dentro do chip implantável, esses sistemas de processamento devem estar otimizados em termos de área e consumo de potência. Dois processamentos encontrados na pesquisa de interfaces neurais implantáveis são a detecção de impulsos elétricos e a separação de impulsos elétricos (Spike Sorting). Nesta tese apresentam-se soluções para esses tipos de processamentos visando a implementação mediante tecnologia CMOS (Complementary Metal Oxide Semiconductor). Para o caso da detecção de impulsos elétricos (spikes), nesta tese apresenta-se uma alternativa de implementação em hardware de um operador matemático conhecido como operador não linear de energia (NEO do inglês Nonlinear Energy Operator) ou operador Teager. Através da aplicação desse operador a um sinal neural evidencia-se a presença de spikes e atenua-se o ruído. Uma das características inovadoras da implementação apresentada nesta tese é a utilização de um circuito elevador ao quadrado que consiste de apenas três transistores, como bloco funcional básico para a realização da operação NEO. O circuito NEO desenvolvido consome 300 pJ no processamento de um spike e foi caracterizado por simulação até em 30 kHz, frequência que é compatível com as taxas de amostragem encontradas na literatura. O outro processamento abordado nesta tese, conhecido como separação de impulsos elétricos ou Spike Sorting, consiste no agrupamento dos impulsos elétricos registrados por um eletrodo em categorias, de maneira que em uma categoria estejam os impulsos gerados por um mesmo neurônio. Em outras palavras, o objetivo é reconhecer quais dos impulsos elétricos medidos pelo eletrodo pertencem a um mesmo neurônio, sendo possível que vários neurônios influenciem na medida realizada por um único eletrodo. Uma solução para a separação de impulsos, apropriada no contexto de sistemas implantáveis, é o template matching. Essa técnica baseia-se na geração de modelos (templates) durante uma fase inicial ao final da qual o número de templates gerados corresponde ao número de neurônios identificados pelo eletrodo. Numa fase seguinte, o sistema associa cada impulso elétrico detectado a um dos modelos inicialmente gerados. Nesta tese propõe-se um sistema de classificação que executa essa segunda fase do processo de spike sorting. Nesta tese apresenta-se o projeto de um sistema de classificação de spikes baseado na t écnica template matching, implementado com tecnologia CMOS. A implementação proposta nesta tese baseia-se na representação de amostras analógicas mediante o tempo. Esse tipo de representação de sinais analógicos mediante atrasos de pulsos digitais está sendo muito utilizado como alternativa à representação no domínio da tensão, da corrente ou da carga elétrica. A vantagem desse tipo de representação é que não se vê severamente afetada pela redução da tensão de alimentação dos circuitos integrados fabricados em tecnologias submicrométricas. A taxa de acerto na classificação do sistema desenvolvido é maior que 99% inclusive considerando um offset de até 20mV no comparador de saída. Os circuitos apresentados neste trabalho foram projetados considerando dispositivos da tecnologia TSMC de 90nm. / Neural signals recording through implantable microelectrode arrays in cortex extracellular medium has become an experimental paradigm for neuroscience. Moreover, recent research about motor neuroprostheses has shown that it is possible to decode motor commands from the signals recorded in the cerebral cortex extracellular medium. In both situations, experimental neuroscience and motor neuroprostheses development, one of the issues encountered in the state-of-the-art is the use of integrated circuits (chips) implanted in the brain. In these chips, neural signals measured with microelectrodes are amplified, filtered, processed, and transmitted to an external computer through wires that run through the skull. There is interest in developing implantable chips that transmit signals to the external computer without the need for wires passing through the skull. In the survey of the state-of-the-art it has found the use of such implantable wireless chips in rats and monkeys, but until the date of this writing we have not found reports of application in humans. One of the aspects that must be taken into account in the development of wireless implantable neural interfaces is the bandwidth of the communication channel. The greater the amount of data to be transmitted, the greater the bandwidth required and higher chip heating due to power dissipation. This thesis deals with extracellular neural signals processing systems that aim to reduce the amount of data to be transmitted and in this way to enable wireless transmission. In order to integrate them into an implantable chip, those processing systems must be optimized in terms of area and power consumption. Two processes found in the research of implantable neural interfaces are spike detection and spike sorting. In this thesis solutions for these types of processing are presented considering their implementation by CMOS (Complementary Metal Oxide Semiconductor). For the case of spike detection in this thesis it is presented an alternative for the hardware implementation of a mathematical operator known as NEO (Nonlinear Energy Operator). Through the application of this operator to a neural signal the presence of spikes becomes evident and the noise is attenuated. One of the innovative characteristics of the implementation presented in this thesis is the use of a squarer circuit which consists of only three transistors, as a basic function block for performing operation of NEO. NEO circuit consumes 300 pJ in processing a spike, and was characterized by simulation up to 30 kHz, frequency which is compatible with sampling rates found in the literature. The other processing discussed in this thesis, known as Spike Sorting, is the grouping of electrical impulses recorded by an electrode into categories so that the spikes belonging to the same category were generated by a single neuron. In other words, the goal is to recognize which of the spikes measured by the electrode belong to the same neuron, given that it is possible that several neurons influence the measure performed by a single electrode. A solution for the Spike Sorting suitable in the context of implantable systems, is the template matching. This technique is based on generating templates during an initial phase at the end of which the number of generated templates corresponds to the number of neurons identified by the electrode. In the next phase, the system associates each detected spike to one of the templates generated initially. In this thesis it is proposed a classification systems which performs that second phase of the spike sorting process. This thesis presents the design of a spike classification system based on template matching technique, implemented in CMOS technology. The processing proposed in this work is based on the time-based representation of the analog samples. This kind of representation of analog signals by delays of digital pulses is being widely used as an alternative to the classical representation of samples by voltage, current or electric charge. The advantage of this time-mode representation is that it is not severely affected by reduced supply voltage of integrated circuits manufactured in sub-micrometer technologies. The classification hit rate of the developed system is greater than 99% even when an offset of 20 mV is assumed for the output comparator. All the circuits presented in this work were designed using devices from TSMC 90nm technology.

O registro de sinais neurais através de matrizes de microeletrodos implantáveis no meio extracelular do córtex cerebral tem-se tornado um paradigma experimental para a neurociência. Por outro lado, a pesquisa recente sobre neuropróteses motoras tem mostrado que é possível decodificar comandos motores a partir dos sinais registrados no meio extracelular do córtex cerebral. Em ambos os contextos, neurociência experimental e desenvolvimento de neuropróteses motoras, um dos aspectos encontrados no estado da arte ´e a utilização de circuitos integrados (chips) implantados no cérebro. Nesses chips, os sinais neurais medidos com os microeletrodos são amplificados, filtrados, processados e transmitidos a um computador externo mediante fios que atravessam o crânio. Existe o interesse em desenvolver chips implantáveis que transmitam os sinais ao computador externo sem a necessidade de fios que atravessem o crânio. Na pesquisa do estado da arte tem-se encontrado a utilização de tais chips implantáveis sem fio em ratos e macacos, porém até a data da elaboração deste texto não foram encontrados relatos da aplicação em humanos. Um dos aspectos que deve se levar em consideração no desenvolvimento de interfaces neurais implantáveis sem fio é a largura de banda do canal de comunicação. Quanto maior a quantidade de dados a serem transmitidos, maior a largura de banda necessária e maior o aquecimento do chip devido à dissipação de potência. Esta tese aborda sistemas de processamento de sinais neurais extracelulares que tem como objetivo reduzir a quantidade de dados a serem transmitidos e assim viabilizar a transmissão sem fio. Para poder ser integrados dentro do chip implantável, esses sistemas de processamento devem estar otimizados em termos de área e consumo de potência. Dois processamentos encontrados na pesquisa de interfaces neurais implantáveis são a detecção de impulsos elétricos e a separação de impulsos elétricos (Spike Sorting). Nesta tese apresentam-se soluções para esses tipos de processamentos visando a implementação mediante tecnologia CMOS (Complementary Metal Oxide Semiconductor). Para o caso da detecção de impulsos elétricos (spikes), nesta tese apresenta-se uma alternativa de implementação em hardware de um operador matemático conhecido como operador não linear de energia (NEO do inglês Nonlinear Energy Operator) ou operador Teager. Através da aplicação desse operador a um sinal neural evidencia-se a presença de spikes e atenua-se o ruído. Uma das características inovadoras da implementação apresentada nesta tese é a utilização de um circuito elevador ao quadrado que consiste de apenas três transistores, como bloco funcional básico para a realização da operação NEO. O circuito NEO desenvolvido consome 300 pJ no processamento de um spike e foi caracterizado por simulação até em 30 kHz, frequência que é compatível com as taxas de amostragem encontradas na literatura. O outro processamento abordado nesta tese, conhecido como separação de impulsos elétricos ou Spike Sorting, consiste no agrupamento dos impulsos elétricos registrados por um eletrodo em categorias, de maneira que em uma categoria estejam os impulsos gerados por um mesmo neurônio. Em outras palavras, o objetivo é reconhecer quais dos impulsos elétricos medidos pelo eletrodo pertencem a um mesmo neurônio, sendo possível que vários neurônios influenciem na medida realizada por um único eletrodo. Uma solução para a separação de impulsos, apropriada no contexto de sistemas implantáveis, é o template matching. Essa técnica baseia-se na geração de modelos (templates) durante uma fase inicial ao final da qual o número de templates gerados corresponde ao número de neurônios identificados pelo eletrodo. Numa fase seguinte, o sistema associa cada impulso elétrico detectado a um dos modelos inicialmente gerados. Nesta tese propõe-se um sistema de classificação que executa essa segunda fase do processo de spike sorting. Nesta tese apresenta-se o projeto de um sistema de classificação de spikes baseado na t écnica template matching, implementado com tecnologia CMOS. A implementação proposta nesta tese baseia-se na representação de amostras analógicas mediante o tempo. Esse tipo de representação de sinais analógicos mediante atrasos de pulsos digitais está sendo muito utilizado como alternativa à representação no domínio da tensão, da corrente ou da carga elétrica. A vantagem desse tipo de representação é que não se vê severamente afetada pela redução da tensão de alimentação dos circuitos integrados fabricados em tecnologias submicrométricas. A taxa de acerto na classificação do sistema desenvolvido é maior que 99% inclusive considerando um offset de até 20mV no comparador de saída. Os circuitos apresentados neste trabalho foram projetados considerando dispositivos da tecnologia TSMC de 90nm. / Neural signals recording through implantable microelectrode arrays in cortex extracellular medium has become an experimental paradigm for neuroscience. Moreover, recent research about motor neuroprostheses has shown that it is possible to decode motor commands from the signals recorded in the cerebral cortex extracellular medium. In both situations, experimental neuroscience and motor neuroprostheses development, one of the issues encountered in the state-of-the-art is the use of integrated circuits (chips) implanted in the brain. In these chips, neural signals measured with microelectrodes are amplified, filtered, processed, and transmitted to an external computer through wires that run through the skull. There is interest in developing implantable chips that transmit signals to the external computer without the need for wires passing through the skull. In the survey of the state-of-the-art it has found the use of such implantable wireless chips in rats and monkeys, but until the date of this writing we have not found reports of application in humans. One of the aspects that must be taken into account in the development of wireless implantable neural interfaces is the bandwidth of the communication channel. The greater the amount of data to be transmitted, the greater the bandwidth required and higher chip heating due to power dissipation. This thesis deals with extracellular neural signals processing systems that aim to reduce the amount of data to be transmitted and in this way to enable wireless transmission. In order to integrate them into an implantable chip, those processing systems must be optimized in terms of area and power consumption. Two processes found in the research of implantable neural interfaces are spike detection and spike sorting. In this thesis solutions for these types of processing are presented considering their implementation by CMOS (Complementary Metal Oxide Semiconductor). For the case of spike detection in this thesis it is presented an alternative for the hardware implementation of a mathematical operator known as NEO (Nonlinear Energy Operator). Through the application of this operator to a neural signal the presence of spikes becomes evident and the noise is attenuated. One of the innovative characteristics of the implementation presented in this thesis is the use of a squarer circuit which consists of only three transistors, as a basic function block for performing operation of NEO. NEO circuit consumes 300 pJ in processing a spike, and was characterized by simulation up to 30 kHz, frequency which is compatible with sampling rates found in the literature. The other processing discussed in this thesis, known as Spike Sorting, is the grouping of electrical impulses recorded by an electrode into categories so that the spikes belonging to the same category were generated by a single neuron. In other words, the goal is to recognize which of the spikes measured by the electrode belong to the same neuron, given that it is possible that several neurons influence the measure performed by a single electrode. A solution for the Spike Sorting suitable in the context of implantable systems, is the template matching. This technique is based on generating templates during an initial phase at the end of which the number of generated templates corresponds to the number of neurons identified by the electrode. In the next phase, the system associates each detected spike to one of the templates generated initially. In this thesis it is proposed a classification systems which performs that second phase of the spike sorting process. This thesis presents the design of a spike classification system based on template matching technique, implemented in CMOS technology. The processing proposed in this work is based on the time-based representation of the analog samples. This kind of representation of analog signals by delays of digital pulses is being widely used as an alternative to the classical representation of samples by voltage, current or electric charge. The advantage of this time-mode representation is that it is not severely affected by reduced supply voltage of integrated circuits manufactured in sub-micrometer technologies. The classification hit rate of the developed system is greater than 99% even when an offset of 20 mV is assumed for the output comparator. All the circuits presented in this work were designed using devices from TSMC 90nm technology.

Le coronavirus humain 229E (HCoV-229E) est responsable de rhumes mais peut entraîner de graves complications respiratoires chez les personnes âgées ou atteintes d’une maladie Chronique. Les coronavirus sont des virus enveloppés avec un génome à ARN positif simple brin. Trois protéines virales sont ancrées dans l'enveloppe virale : la protéine spike (S), la protéine de membrane (M) et la protéine d’enveloppe (E). Les protéines M et E sont impliquées dans l'assemblage viral et la sécrétion. La protéine S s'assemble en trimères à la surface des virions et joue un rôle-clé dans l’entrée du virus dans sa cellule-cible. Elle est constituée de deux domaines, le domaine S1 responsable de la liaison du virus à son récepteur et le domaine S2 responsable de la fusion de l’enveloppe virale avec une membrane cellulaire. La fusion est activée par des protéases cellulaires par clivage de la protéine S. Dans un premier temps nous avons caractérisé ce mécanisme. Pour cela, nous avons d'abord cloné la protéine S d’un isolat circulant de HCoV-229E. Nous avons analysé le clivage protéolytique de la protéine S par des sérine-protéases de type trypsine conduisant au processus de fusion à l’aide de particules pseudotypées rétrovirales. Les Résidus arginine, sites potentiels de reconnaissance par les protéases et présents au niveau de la jonction S1/S2 ou de la région S2’ ont été mutés individuellement (R565N, R679N, R683N ou R687N) afin d’étudier leur rôle lors de l'activation de la fusion. Contrairement à d'autres coronavirus, l'activation permettant la fusion de HCoV-229E semble être un processus en une seule étape. En effet, seule la mutation R683N inhibe l’infection médiée par des sérine-protéases et le clivage à l'interface S1/S2 ne semble pas être un pré-requis. Les protéines S de coronavirus sont fortement N-glycosylées et constituent la principale cible des anticorps neutralisants. Nous avons analysé le rôle de la N-glycosylation du domaine S1 dans les mécanismes d'entrée et dans la neutralisation par des anticorps. L'analyse de la séquence de la protéine clonée montre la présence de 33 sites potentiels de N-glycosylation, dont 18 dans le domaine S1 qui ont été numérotés de N1 à N18. Ces 18 sites de N-glycosylation ont été abolis individuellement par mutagenèse dirigée. L’effet des mutations sur l'infectiosité virale a été évalué en utilisant des particules pseudotypées rétrovirales. L'infectiosité des mutants N6, N7 ou N9 est diminuée tandis que deux mutants N12 et N15 montrent une augmentation de l'infectiosité. Nous n'avons détecté aucune différence d'interaction de ces mutants avec une forme soluble du récepteur, l'aminopeptidase N (APN). Des expériences d’activation de la fusion virale à la surface cellulaire par la trypsine suggèrent que les glycanes présents aux positions 6, 7 et 9 sont impliquées dans la fusion virale, cependant nous n’avons détecté aucune différence de clivage de ces mutants par la trypsine. Pour le mutant N17 uniquement, la diminution partielle de l'infectiosité pourrait s'expliquer par une diminution de l'incorporation de la protéine S dans les pseudoparticules, due au mauvais repliement de la protéine, comme le montre le profil du mutant en western blot en conditions réductrices ou non.Nous avons ensuite évalué si les N-glycanes pouvaient moduler la reconnaissance de la protéine S par des anticorps neutralisants. Des pseudoparticules contenant les différents mutants ont été produites et utilisées pour infecter des cellules en présence d'anticorps neutralisants. Nos données montrent que les mutants N4, N10, N11, N12, N15, N16, N17, N18 réduisent la sensibilité des pseudoparticules à la neutralisation des anticorps. Dans ensemble, nos résultats suggèrent que les N-glycanes de la protéine S jouent un rôle important dans l'entrée virale et modulent la reconnaissance de la protéine par des anticorps neutralisants. / The human coronavirus 229E (HCoV-229E) is a causative agent of common colds and can lead to severe respiratory complications in elderly persons and those with underlying disease. Coronavirus are enveloped viruses with a single stranded, positive-sense RNA genome. Three viral proteins are anchored in the viral enveloppe : the spike (S) protein, the membrane (M) protein and the enveloppe (E) protein. The M and E proteins are involved in viral assembly and secretion. The spike proteins assemble into trimers at the surface of the virions and play a key role in the early steps of viral infection. The spike protein comprised two domains, the S1 domain responsible for receptor binding and the S2 domain responsible for fusion of the viral enveloppe with the host cell membrane. Coronavirus fusion is activated by the proteolytic processing of the spike protein. First, we charaterized the proteolytic processing of the HCoV-229E spike protein by trypsin-like serine-proteases. To do so, we first cloned the spike protein of a circulating isolate of HCoV-229E. To investigate the role of the S1/S2 junction and the specific role of the 3 arginine residues located in the S2’ region in the proteolytic activation of HCoV-229E spike protein, the arginine residues present at these positions were mutated individually (R565N, R679N, R683N or R687N). Our results show that unlike other coronaviruses, HCoV-229E fusion activation appears to be a one step process. Indeed, the cleavage of the S1/S2 interface does not seem to be a pre-requisite, and the fusion activation strongly relies on the S2’ region, with R683 acting as the cleavage site.The spike protein is highly N-glycosylated and is the main target of neutralizing antibodies. We analysed the role of S1 domain N-glycosylation in the entry functions of the S protein and in neutralization by antibodies. Analysis of the sequence of the cloned protein shows the presence of 33 potential N-glycosylation sites, 18 being located in the S1 domain (numbered from N1 to N18). We mutated the 18 N-glycosylation sites of S1 individually by site-directed mutagenesis and studied the effect of the mutations using retroviral pseudotyped particles. Infectivity of the spike proteins with mutation either at the N6, N7 or N9 glycosylation site was strongly impaired. We did not detect any difference of interaction of these mutants with the soluble form of the receptor, the aminopeptidase N (APN). Results obtained by inducing the fusion of pseudoparticles at the cell surface with trypsin suggest that N-glycans located at the position N6, N7 and N9 are involved in viral fusion. However, the proteolytic processing of the protein required for fusion activation does not seem to be affected. Two mutants N12 and N15 show an increase of infectivity. Mutation of the N-glycosylation site N17 induces a partial decrease in infectivity. Indeed a decrease of spike protein incorporation into pseudoparticles was observed likely due to misfolding of the protein as shown by the profile of the mutant in western blot under reducing and non-reducing conditions. We next assessed if N-glycans can modulate the recognition of the spike protein by neutralizing antibodies. Pseudoparticles harbouring the different mutants were produced and used to infect cells in presence or absence of neutralizing antibodies. Our data demonstrate that mutants N4, N10, N11, N12, N15, N16, N17, N18 reduce the sensitivity of pseudoparticules to antibody neutralization. Taken together our results suggest that N-glycans of the S protein play an important role in viral entry and modulate the recognition of the protein by neutralizing antibodies.

Over the last decade advances in multineuron simultaneous recording techniques have produced huge amounts of data. This has led to the investigation of probable temporal relationships between spike times of neurons as manifestations of the underlying network structure. But the huge dimensionality of data makes the search for patterns difficult. Although this difficulty may be surpassed by employing massive computing resources, understanding the significance and relation of these temporal patterns to the underlying network structure and the causative activity is still difficult. To find such relationships in networks of excitatory neurons, a simplified network structure of feedforward chains called "Synfire chains" has been frequently employed. But in a recurrently connected network where activity from feedback connections is comparable to the feedforward chain, the basic assumptions underlying synfire chains are violated. In the first part of this thesis we propose the first-spike-latency based analysis as a low complexity method of studying the temporal relationships between neurons. Firstly, spike latencies being temporal delays measured at a particular epoch of time (onset of activity after a quiescent period) are a small subset of all the temporal information available in spike trains, thereby hugely reducing the amount of data that needs to be analyzed. We also define for the first time, "Synconset waves and chains" as a sequence of first-spike-times and the causative neuron chain. Using simulations, we show the efficacy of the synconset paradigm in unraveling feedforward chains of excitatory neurons even in a recurrent network. We further create a framework for going back and forth between network structure and the observed first-spike-latency patterns. To quantify these associations between network structure and dynamics we propose a likelihood measure based on Bayesian reasoning. This quantification is agnostic to the methods of association used and as such can be used with any of the existing approaches. We also show the benefits of such an analysis when the recorded data is subsampled, as is the case with most physiological recordings. In the subsequent part of our thesis we show two sample applications of first-spike-latency analysis on data acquired from multielectrode arrays. Our first application dwells on the intricacies of extracting first-spike-latency patterns from multineuron recordings using recordings of glutamate injured cultures. We study the significance of these patterns extracted vis-a-vis patterns that may be obtained from exponential spike latency distributions and show the differences between patterns obtained in injured and control cultures. In a subsequent application, we study the evolution of latency patterns over several days during the lifetime of a dissociated hippocampal culture.

Während seiner normalen Funktion generiert das Gehirn starke elektrische Signale, die technisch gemessen werden können. Das schon seit über einem Jahrhundert bekannte Phänomen ermöglicht es die Signalverarbeitung im Gehirn räumlich und zeitlich zu beobachten. Heute versteht man die zellulären Prozesse die zur Generierung der elektrischen Signale in einzelnen Neuronen führen. Jedoch rekrutieren die meisten neuronalen Ereignisse große Populationen von Zellen, dessen Aktivität zeitlich und räumlich koordiniert ist. Diese Koordinierung führt dazu, dass ihre elektrische Aktivität auch weit von den Quellen gemessen werden kann, sodass die Beobachtung des Gehirns auch nicht invasiv auf der Schädeloberfläche mittels dem sogenannten Elektroenzephalogramm (EEG) möglich ist. Der zeitliche Verlauf des Signals hängt nicht nur von den Eigenschaften einzelner Zellen ab sondern auch von ihrer Wechselwirkung mit anderen Neuronen, die oft komplex oder gar nicht bekannt ist. Diese Komplexität verhindert die Auswertung der gemessen Signale im Bezug auf die Anzahl von aktiven Neuronen, die Art der Antwort (Inhibition, Exzitation), die Synchronisationsstärke und den Einfluss anderer aktiver Prozesse (wie zum Beispiel: Lernen, Aufmerksamkeit usw.). In dieser Arbeit werden die Zusammenhänge zwischen diesen mikroskopischen Parametern (einzelne Neurone) und ihrer makroskopischen Wirkung (EEG) experimentell, datenanalytisch und theoretisch untersucht. / During its normal function the brain generates strong and measurable electric signals. This phenomenon, which has been known for more than a century, makes it possible to investigate the signal processing in the brain. Nowadays the cellular processes taking part in the generation of the electric signals are well understood. However, most of the neuronal events recruit large populations of cells, whose activities are coordinated spatially and temporally. This coordination allows for summation of activities generated by many neurons leading to extracellular electric signals that can be recorded non-invasively from the scalp by means of electroencephalography (EEG). The temporal structure of the EEG signal does not depend only on the properties of single neurons, but also on their interactions that may be very complex. The complexity hinders the evaluation of the recoded signal with respect to the number of active neurons, the type of response, the degree of synchronisation and the contribution of other processes (such as, learning and attention). In the thesis, the relations between the microscopic (single-neuron) and their macroscopic (EEG) properties will be investigated by means of experimental, data-analytic and theoretical approaches.

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.

Dans cette thèse, nous étudions les applications potentielles des nano-dispositifs mémoires émergents dans les architectures de calcul. Nous montrons que des architectures neuro-inspirées pourraient apporter l'efficacité et l'adaptabilité nécessaires à des applications de traitement et de classification complexes pour la perception visuelle et sonore. Cela, à un cout moindre en termes de consommation énergétique et de surface silicium que les architectures de type Von Neumann, grâce à une utilisation synaptique de ces nano-dispositifs. Ces travaux se focalisent sur les dispositifs dit «memristifs», récemment (ré)-introduits avec la découverte du memristor en 2008 et leur utilisation comme synapse dans des réseaux de neurones impulsionnels. Cela concerne la plupart des technologies mémoire émergentes : mémoire à changement de phase – «Phase-Change Memory» (PCM), «Conductive-Bridging RAM» (CBRAM), mémoire résistive – «Resistive RAM» (RRAM)... Ces dispositifs sont bien adaptés pour l'implémentation d'algorithmes d'apprentissage non supervisés issus des neurosciences, comme «Spike-Timing-Dependent Plasticity» (STDP), ne nécessitant que peu de circuit de contrôle. L'intégration de dispositifs memristifs dans des matrices, ou «crossbar», pourrait en outre permettre d'atteindre l'énorme densité d'intégration nécessaire pour ce type d'implémentation (plusieurs milliers de synapses par neurone), qui reste hors de portée d'une technologie purement en «Complementary Metal Oxide Semiconductor» (CMOS). C'est l'une des raisons majeures pour lesquelles les réseaux de neurones basés sur la technologie CMOS n'ont pas eu le succès escompté dans les années 1990. A cela s'ajoute la relative complexité et inefficacité de l'algorithme d'apprentissage de rétro-propagation du gradient, et ce malgré tous les aspects prometteurs des architectures neuro-inspirées, tels que l'adaptabilité et la tolérance aux fautes. Dans ces travaux, nous proposons des modèles synaptiques de dispositifs memristifs et des méthodologies de simulation pour des architectures les exploitant. Des architectures neuro-inspirées de nouvelle génération sont introduites et simulées pour le traitement de données naturelles. Celles-ci tirent profit des caractéristiques synaptiques des nano-dispositifs memristifs, combinées avec les dernières avancées dans les neurosciences. Nous proposons enfin des implémentations matérielles adaptées pour plusieurs types de dispositifs. Nous évaluons leur potentiel en termes d'intégration, d'efficacité énergétique et également leur tolérance à la variabilité et aux défauts inhérents à l'échelle nano-métrique de ces dispositifs. Ce dernier point est d'une importance capitale, puisqu'il constitue aujourd'hui encore la principale difficulté pour l'intégration de ces technologies émergentes dans des mémoires numériques. / In this thesis, we study the potential applications of emerging memory nano-devices in computing architecture. More precisely, we show that neuro-inspired architectural paradigms could provide the efficiency and adaptability required in some complex image/audio processing and classification applications. This, at a much lower cost in terms of power consumption and silicon area than current Von Neumann-derived architectures, thanks to a synaptic-like usage of these memory nano-devices. This work is focusing on memristive nano-devices, recently (re-)introduced by the discovery of the memristor in 2008 and their use as synapses in spiking neural network. In fact, this includes most of the emerging memory technologies: Phase-Change Memory (PCM), Conductive-Bridging RAM (CBRAM), Resistive RAM (RRAM)... These devices are particularly suitable for the implementation of natural unsupervised learning algorithms like Spike-Timing-Dependent Plasticity (STDP), requiring very little control circuitry.The integration of memristive devices in crossbar array could provide the huge density required by this type of architecture (several thousand synapses per neuron), which is impossible to match with a CMOS-only implementation. This can be seen as one of the main factors that hindered the rise of CMOS-based neural network computing architectures in the nineties, among the relative complexity and inefficiency of the back-propagation learning algorithm, despite all the promising aspects of such neuro-inspired architectures, like adaptability and fault-tolerance. In this work, we propose synaptic models for memristive devices and simulation methodologies for architectural design exploiting them. Novel neuro-inspired architectures are introduced and simulated for natural data processing. They exploit the synaptic characteristics of memristives nano-devices, along with the latest progresses in neurosciences. Finally, we propose hardware implementations for several device types. We assess their scalability and power efficiency potential, and their robustness to variability and faults, which are unavoidable at the nanometric scale of these devices. This last point is of prime importance, as it constitutes today the main difficulty for the integration of these emerging technologies in digital memories.

En utilisant les méthodes d’apprentissages tirées des récentes découvertes en neuroscience, les réseaux de neurones impulsionnels ont démontrés leurs capacités à analyser efficacement les grandes quantités d’informations provenant de notre environnement. L’implémentation de ces circuits à l’aide de processeurs classiques ne permet pas d’exploiter efficacement leur parallélisme. L’utilisation de mémoire numérique pour implémenter les poids synaptique ne permet pas la lecture ou la programmation parallèle des synapses et est limité par la bande passante reliant la mémoire à l’unité de calcul. Les technologies mémoire de type memristive pourrait permettre l’implémentation de ce parallélisme au coeur de la mémoire.Dans cette thèse, nous envisageons le développement d’un réseau de neurones impulsionnels dédié au monde de l’embarqué à base de dispositif mémoire émergents. Dans un premier temps, nous avons analysé un réseau impulsionnel afin d’optimiser ses différentes composantes : neurone, synapse et méthode d’apprentissage STDP en vue d’une implémentation numérique. Dans un second temps, nous envisageons l’implémentation de la mémoire synaptique par des dispositifs memristifs. Enfin, nous présentons le développement d’une puce co-intégrant des neurones implémentés en CMOS avec des synapses en technologie CBRAM. / By using learning mechanisms extracted from recent discoveries in neuroscience, spiking neural networks have demonstrated their ability to efficiently analyze the large amount of data from our environment. The implementation of such circuits on conventional processors does not allow the efficient exploitation of their parallelism. The use of digital memory to implement the synaptic weight does not allow the parallel reading or the parallel programming of the synapses and it is limited by the bandwidth of the connection between the memory and the processing unit. Emergent memristive memory technologies could allow implementing this parallelism directly in the heart of the memory.In this thesis, we consider the development of an embedded spiking neural network based on emerging memory devices. First, we analyze a spiking network to optimize its different components: the neuron, the synapse and the STDP learning mechanism for digital implementation. Then, we consider implementing the synaptic memory with emergent memristive devices. Finally, we present the development of a neuromorphic chip co-integrating CMOS neurons with CBRAM synapses.