Le récepteur cannabinoique de type 1 (CB1R) est l’un des récepteurs couplés aux protéines G les plus abondants du cerveau mammifère. CB1R a longtemps été décrit comme un récepteur présynaptique régulant de manière rétrograde la transmission synaptique. Cependant, depuis les vingt dernières années, de nouveaux rôles ont été découverts et il est maintenant clairement admis que l’action des endocannabinoides (eCBs) ne se limite pas à la régulationde la neurotransmission au niveau de synapses adultes déjà établies. En effet, les eCBs et le CB1R sont des acteurs majeurs de l’ensemble des phases du développement cérébral. Cependant, les mécanismes moléculaires impliqués n’ont toujours pas été identifiés. Les mécanismes cellulaires auxquels nous nous intéressons ne dépendant pas de l’environnement cellulaire, nous proposons donc de combiner la puissance génétique du modèle drosophile à l’accessibilité et la haute résolution offerte par la culture primaire de neurones. De plus, le récepteur CB1 ne possédant pas d’orthologue parmi les invertébrés, ce système offre la possibilité d’étudier la biologie du récepteur en s’affranchissant de la machinerie endocannabinoide. Cependant, actuellement, aucun protocole de culture primaire de neurones de drosophile ne permet d’obtenir des cellules hautement différenciées et polarisées à basse densité. Ainsi, nous avons tout d’abord développé, optimisé et validé un nouveau protocole permettant de d’obtenir des neurones fonctionnels, hautement différenciés et polarisés en culture de basse densité. Dans un second temps, nous avons démontré que l’activation durécepteur CB1, exprimé ectopiquement dans les neurones de drosophile, entrainait son internalisation, de manière identique à ce qui avait déjà été observé chez les mammifères. Puis, nous avons étudié l’effet de l’expression et de l’activation ectopique de CB1R sur le développement neuronal chez la drosophile. Ainsi, nous avons démontré que l’activation du récepteur module directement la dendritogénèse. Afin de compléter la caractérisation de notremodèle, nous avons démontré que l’activation transitoire du récepteur dans les corps pédonculés (le centre de la mémoire olfactive chez la drosophile) altérait spécifiquement la formation d’une forme consolidée de mémoire après un conditionnement aversif. En conclusion, la validation du modèle drosophile dans l’étude de la biologie cellulaire durécepteur CB1 ouvre de nouvelles perspectives quant à la détermination des mécanismes moléculaires régissant l’action du récepteur sur le fonctionnement neuronal. / The type-1 cannabinoid receptor (CB1R), the neuronal receptor for the major psychoactive substance of marijuana, is one, of the most abundant G-protein coupled receptors in the mammalian central nervous system. CB1R is traditionally described as a presynaptic receptor that retrogradely regulates synaptic transmission. In addition to this now relatively wellcharacterized function, in the last two decades it has become widely recognized that endocannabinoid (eCB) actions in the brain are not limited to the regulation of neurotransmission at established adult synapses. Indeed, eCB and CB1R are now recognized to be involved in brain development at the synaptic, neuronal and network levels. However, precise mechanisms underlying these processes remain poorly described. Since cellular mechanisms that mediate CB1R-activition dependent neuronal remodeling and subneuronal targeting have been demonstrated to be cell-autonomous, we aimed to combine the power of Drosophila genetics with the experimental accessibility and single-cell resolution of lowdensity primary neuronal cultures, a tool currently lacking in Drosophila. Moreover, becauseDrosophila does not have a CB1R ortholog, CB1R cell biology may be observed independently from eCB machinery. Thus, we first developed and validated an in vitro culture protocol that yields mature and fully differentiated Drosophila neurons. Secondly, we showed that activation-dependent endocytosis of ectopically expressed CB1R is conserved in Drosophila neurons. Next, we investigated whether ectopic expression and activation of CB1R in Drosophila modulate neuronal development. As observed in mammals, we observed that activation of CB1R impairs dendritogenesis in a cell-autonomous manner. For further characterization of our model, we showed that, as with mammals, transient ectopic CB1R expression and activation in mushroom body neurons (the center of olfactory memory in Drosophila) modulate the formation of a consolidated form of aversive memory. In conclusion, the validation of this new animal model opens new perspectives to better characterize mechanisms underlying modulation of neuronal functions induced by CB1Ractivity

Complex networks of cells called neurons in the brain enable human learning and memory. The topology and electrophysiological function of these networks are affected by nano and microscale geometries of neurons. Understanding of these structure-function relationships in neurons is an important component of neuroscience in which simulation plays a fundamental role. This thesis addresses four specific geometric problems raised by modeling and simulation of intricate neuronal structure and behavior at the nanoscale. The first two problems deal with 3D surface reconstruction: neurons are geometrically complex structures that are tightly intertwined in the brain, presenting great challenges in reconstruction. We present the first algorithm that reconstructs surface meshes from polygonal contours that provably guarantees watertight, manifold, and intersection-free forests of densely packed structures. Many algorithms exist that produce surfaces from cross-sectional contours, but all either use heuristics in fitting the surface or they fail when presented with tortuous objects in close proximity. Our algorithm reconstructs surfaces that are not only internally correct, but are also free of intersections with other reconstructed objects in the same region. We also present a novel surface remeshing algorithm suitable for models of neuronal dual space. The last two problems treated by this thesis deal with producing derivative models from surface meshes. A range of neuronal simulation methodologies exist and we offer a framework to derive appropriate models for each from surface meshes. We present two specific algorithms that yield analysis-ready 1D cable models in one case, and proposed "aligned cell" models in the other. In the creation of aligned cells we also present a novel adaptive distance transform. Finally, we present a software package called VolRoverN in which we have implemented many of our algorithms and which we expect will serve as a repository of important tools for the neuronal modeling community. Our algorithms are designed to meet the immediate needs of the neuroscience community, but as we show in this thesis, they are general and suitable for a variety of applications. / text

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