SYNGAP1 haploinsufficiency is one of the most common monogenic causes of nonsyndromic moderate to severe intellectual disability (NSID) and autism (Hamdan et al., 2009; Pinto et al., 2010). De novo truncating or frameshift mutations in the SYNGAP1 gene lead to the loss of the encoded protein Synaptic GTPase activating protein (SynGAP), one of the most abundant of postsynaptic proteins (Hamdan et al., 2011). SynGAP, present at excitatory and inhibitory synapses (Kim et al., 1998), acts as a key regulator of highly conserved signaling pathways linked to AMPA- and NMDA-receptor dependent plasticity at the post synaptic density (Krapivisky et al., 2004; Vazquez et al., 2004). The Syngap mouse model has been extensively used to understand the pathophysiology underlying abnormal SynGAP-mediated signaling. Syngap heterozygous (het) mice demonstrate a range of physiological and behavioural abnormalities from development to adulthood (Komiyama et al., 2002; Muhia et al., 2010). However, recent advances in techniques for genome manipulation have allowed for the generation of rat models of neurodevelopmental disorders, including Syngap; enabling phenotypes to be validated across species and to address cognitive and social dysfunction, using paradigms that are more difficult to assess in mice. In this study, we examined the pathophysiology associated with a heterozygous deletion of the C2 and catalytic GAP domain of the protein, in Long-Evans rats (het). In contrast with het mice, het rats do not present with hyperactivity and can be habituated to an open field environment. To examine associative recognition memory, we tested the rats in five spontaneous exploration tasks for short-term and long-term memory, object-recognition (OR), object-location (OL), object-place (OP), object-context (OC) and object-place-context (OPC). Both groups were able to perform short-term memory tasks, but only wild type rats performed above chance in OL with a 24hour delay, suggesting deficits in long- term spatial memory. We also tested if partial loss of the GAP domain in SynGAP affects social behaviour in rats and we found that het rats exhibited impaired short- term social memory, with no signs of social isolation. These findings do not fully recapitulate previous abnormalities reported in the mouse model of SYNGAP1 haploinsufficiency, suggesting that some key behavioural phenotypes may be species-specific. Furthermore, based on physiological deficits that Syngap het mice exhibit, such as alterations in mEPSC/mIPSC amplitude and frequency and evoked cortical hyperexcitability in vitro (Guo et al., 2009; Ozkan et al., 2014), we also aimed to test if in vivo neuronal activity and circuit properties are altered. Using two-photon calcium imaging in awake mice, we focused on two areas of the cortex; a primary sensory area, the binocular region of the visual cortex (V1), and an association area, the medial posterior parietal cortex (PPC). Both areas have been found to maintain activity during visual discrimination tasks but to present with divergent activity trajectories (Harvey et al., 2012; Goard et al., 2016). We found preliminary evidence that neurons in layer 2-3 of the PPC of Syngap mice are hypoactive in basal conditions when animals are still in the dark, compared to wild type controls. When we assessed whether that changes when animals are running, we found that during locomotion neurons of both genotypes increase their activity, consistent with previous findings in wild type mice (McGinley et al., 2015; Pakan et al., 2016). However, this response gain is exaggerated in Syngap het neurons of the PPC. In contrast to above findings in PPC, results in V1 show that layer 2-3 neurons are hyperactive during both behavioural states, suggesting seemingly different computations of these two cortical areas. This work provides the first evidence for a dysregulated neuronal circuit in vivo in both visual and parietal cortex of Syngap mice, two areas critical for sensory processing that has been found to be affected in individuals with NSID and autism (Joosten and Bundy, 2010). We also provide first evidence of the effect of loss of SynGAP activity in behaviour of rats, complimenting existing data in the literature in a species-specific manner and providing greater insight into sensory and cognitive dysfunction associated with dysregulation in SynGAP-mediated signaling.

The design of power amplifiers within a circuit simulator requires a good non-linear model that accurately predicts the electormagnetic behaviour of the power transistor. In recent years, a certain class of large signal frequency-dependent black-box behavioural modeling techniques known as Poly-Harmonic Distortion (PHD) models has been devised to mimic the non-linear unmatched RF transistor. These models promise a good prediction of the device behaviour under multi-harmonic periodic continuous wave inputs. This thesis describes the capabilities of the PHD modeling framework and the theoretical type of behaviour that it is capable of predicting. Specifically, the PHD framework cannot necessarily predict the response of a broadband aperiodic signal. This analysis will be performed by deriving the PHD modeling framework as a simplification of the Volterra series kernel functions under the assumption that the power transistor is operating under continuous periodic multi-harmonic voltage and current signals in a stable circuit. A PHD model will be seen as a set of describing functions that predict the response of the Device Under Test (DUT) for any given non-linear periodic continuous-wave inputs that have a specific fundamental frequency. Two popular implementations of PHD models that can be found in the literature are the X-parameter and Cardiff models. Each model formulates the describing functions of the general PHD model differently. The mathematical formulation of the X-parameter and Cardiff models will be discussed in order to provide a theoretical ground for comparing their robustness. The X-parameter model will be seen as the first-order Taylor series approximation of the PHD model describing functions around a Large Signal Operating Point (LSOP) of the device under test. The Cardiff large-signal model uses Fourier series coefficient functions that vary with the magnitude of the large signal(s) as the PHD model describing functions. This thesis will provide a breakdown of the measurement procedure required for the extraction of these models, the challenges involved in the measurement, as well as the mathematical extraction of the model coe cients from measurement data. As each of these models contain have extended versions that enhance the predictive capability of the model under stronger nonlinear modes of operation, a comparison is used to represent the cost of increasing model accuracy as a function of the increasing model complexity for each model. The order of complexity of each model can manifest itself in terms of the mathematical formulation, the number of parameters required and the measurement time that is required to extract each model for a given DUT. This comparison will fairly assess the relative strengths and weaknesses of each model.

The design of power amplifiers within a circuit simulator requires a good non-linear model that accurately predicts the electormagnetic behaviour of the power transistor. In recent years, a certain class of large signal frequency-dependent black-box behavioural modeling techniques known as Poly-Harmonic Distortion (PHD) models has been devised to mimic the non-linear unmatched RF transistor. These models promise a good prediction of the device behaviour under multi-harmonic periodic continuous wave inputs. This thesis describes the capabilities of the PHD modeling framework and the theoretical type of behaviour that it is capable of predicting. Specifically, the PHD framework cannot necessarily predict the response of a broadband aperiodic signal. This analysis will be performed by deriving the PHD modeling framework as a simplification of the Volterra series kernel functions under the assumption that the power transistor is operating under continuous periodic multi-harmonic voltage and current signals in a stable circuit. A PHD model will be seen as a set of describing functions that predict the response of the Device Under Test (DUT) for any given non-linear periodic continuous-wave inputs that have a specific fundamental frequency. Two popular implementations of PHD models that can be found in the literature are the X-parameter and Cardiff models. Each model formulates the describing functions of the general PHD model differently. The mathematical formulation of the X-parameter and Cardiff models will be discussed in order to provide a theoretical ground for comparing their robustness. The X-parameter model will be seen as the first-order Taylor series approximation of the PHD model describing functions around a Large Signal Operating Point (LSOP) of the device under test. The Cardiff large-signal model uses Fourier series coefficient functions that vary with the magnitude of the large signal(s) as the PHD model describing functions. This thesis will provide a breakdown of the measurement procedure required for the extraction of these models, the challenges involved in the measurement, as well as the mathematical extraction of the model coe cients from measurement data. As each of these models contain have extended versions that enhance the predictive capability of the model under stronger nonlinear modes of operation, a comparison is used to represent the cost of increasing model accuracy as a function of the increasing model complexity for each model. The order of complexity of each model can manifest itself in terms of the mathematical formulation, the number of parameters required and the measurement time that is required to extract each model for a given DUT. This comparison will fairly assess the relative strengths and weaknesses of each model.

Ce travail de thèse a pour contexte la surveillance des systèmes homme-machine, où l'opérateur est le conducteur d'un système de transport ferroviaire. Notre objectif est d'améliorer la sécurité du système en prévenant et en évitant les facteurs pouvant augmenter le risque d'une erreur humaine. Deux verrous majeurs sont identifiés : l'aspect caractérisation, ou comment déterminer les phases indicatives et discernables de l'activité de conduite et l'aspect représentation, ou comment décrire et codifier les actions de conduite de l'opérateur et leurs répercussions sur le système ferroviaire dans un formalisme mathématique permettant une analyse sans équivoque. Pour solutionner ces verrous, nous proposons en premier lieu un modèle comportemental de l'opérateur humain permettant de représenter son comportement de contrôle en temps continu. Afin de tenir compte des différences inter- et intra-individuelles des opérateurs humains, ainsi des changements de situations, nous proposons une transformation du modèle comportemental initialement présenté, dans un nouveau espace de représentation. Cette transformation est basée sur la théorie des chaines cachées de Markov, et sur l'adaptation d'une technique particulière de reconnaissance de formes. Par la suite, nous définissons une modélisation comportementale en temps discret de l'opérateur humain, permettant en même temps de représenter ses actions et de tenir compte des erreurs et des évènements inattendus dans l'environnement de travail. Cette modélisation est inspirée des modèles cognitifs d’opérateur. Les deux aspects permettent d'interpréter les observables par rapport à des situations de référence. Afin de caractériser l'état global de l'opérateur humain, différentes informations sont prises en considération ; ces informations sont hétérogènes et entachées d’incertitudes de mesure, nécessitant une procédure de fusion de données robuste qui est effectuée à l'aide d'un réseau Bayésien. Au final, les méthodologies de modélisation et de fusion proposées sont exploitées pour la conception d'un système de vigilance fiable et non-intrusif. Ce système permet d'interpréter les comportements de conduite et de détecter les états à risque du conducteur (ex. l'hypovigilance). L'étude théorique a été testée en simulation pour vérifier sa validité. Puis, une étude de faisabilité a été menée sur des données expérimentales obtenues lors des expériences sur la plate-forme de conduite ferroviaire COR&GEST du laboratoire LAMIH. Ces résultats ont permis de planifier et de mettre en place les expérimentations à mener sur le futur simulateur de conduite multimodal "PSCHITT-PMR". / The scope of the thesis is the monitoring of human-machine systems, where the operator is the driver of rail-based transportation system. Our objective is to improve the security of the system preventing and avoiding factors that increase the risk of a human error. Two major problems are identified: characterization, or how to determine indicative and discernible phases of driver's activity and representation, or how to describe and codify driver's actions and its repercussions on the rail system in a mathematical formalism that will allow unequivocal analysis. In order to bring a solution to those problems, we propose, first-of-all, a behavioral model of the human operator representing his control behavior in continuous-time. To consider inter- and intra-individual differences of human operators and situation changes, we propose a transformation of the latter behavioral model in a new space of representation. This transformation is based on the theory of Hidden Markov Models, and on an adaptation of a special pattern recognition technique. Then, we propose a discrete-time behavioral modeling of the human operator, which represents his actions and takes account of errors and unexpected events in work environment. This model is inspired by cognitive models of human operators. These two aspects allow us to interpret observables with respect to reference situations in order to characterize the overall human operator state. Different information sources are considered; as a result the data are heterogeneous and subject to measuring uncertainties, needing a robust data fusion approach that is performed using a Bayesian Network. Finally, the proposed modeling and fusion methodologies are used to design a reliable and unintrusive vigilance system. This system can interpret driving behaviors and to detect driver’s risky states in order to prevent drowsiness. The theoretical study was tested in simulation to check the validity. Then, a feasibility study was conducted using data obtained during experiments on the LAMIH laboratory railroad platform “COR&GEST”. These results allowed us to plan and implement experiments to be conducted on the future multimodal driving simulator “PSCHITT-PMR”.