For almost a century, the electrical properties of the brain and the nervous system have been investigated to gain a better understanding of their mechanisms and to find cures for pathological conditions. Despite the fact that today's advancements in surgical techniques, research, and medical imaging have improved our ability to treat brain disorders, our knowledge of the brain and its functions is still limited. Culturing dissociated cortical neurons on Micro-Electrode Array dishes is a powerful experimental tool for investigating functional and structural characteristics of in-vitro neuronal networks, such as the cellular basis of brain learning, memory and synaptic developmental plasticity. This dissertation focuses on combining MEAs with novel electrophysiology experimental paradigms and statistical data analysis to investigate the mechanisms that regulate brain development at the level of synaptic formation and growth cones. The goal is to use a mathematical approach and specifically designed experiments to investigate whether dissociated neuronal networks can dependably display long and short-term plasticity, which are thought to be the building blocks of memory formation in the brain. Quantifying the functional evolution of dissociated neuronal networks during in- vitro development, using a statistical analysis tool was the first aim of this work. The results of the False Discovery Rate analysis show an evolution in network activity with changes in both the number of statistically significant stimulus/recording pairs as well as the average length of connections and the number of connections per active node. It is therefore proposed that the FDR analysis combined with two metrics, the average connection length and the number of highly connected "supernodes" is a valuable technique for describing neuronal connectivity in MEA dishes. Furthermore, the statistical analysis indicates that cultures dissociated from the same brain tissue display trends in their temporal evolution that are more similar than those obtained with respect to different batches. The second aim of this dissertation was to investigate long and short-term plasticity responsible for memory formation in dissociated neuronal networks. In order to address this issue, a set of experiments was designed and implemented in which the MEA electrode grid was divided into four quadrants, two of which were chronically stimulated, every two days for one hour with a stimulation paradigm that varied over time. Overall network and quadrant responses were then analyzed to quantify what level of plasticity took place in the network and how this was due to the stimulation interruption. The results demonstrate that here were no spatial differences in the stimulus-evoked activity within quadrants. Furthermore, the implemented stimulation protocol induced depression effects in the neuronal networks as demonstrated by the consistently lower network activity following stimulation sessions. Finally, the analysis demonstrated that the inhibitory effects of the stimulation decreased over time, thus suggesting a habituation phenomenon. These findings are sufficient to conclude that electrical stimulation is an important tool to interact with dissociated neuronal cultures, but localized stimuli are not enough to drive spatial synaptic potentiation or depression. On the contrary, the ability to modulate synaptic temporal plasticity was a feasible task to achieve by chronic network stimulation. / Electrical and Computer Engineering

Cette thèse s’intègre dans le cadre du projet Européen FACETS. Pour ce projet, des systèmes matériels mixtes analogique-numérique effectuant des simulations en temps réel des réseaux de neurones doivent être développés. Le but est d’aider à la compréhension des phénomènes d’apprentissage dans le néocortex. Des circuits intégrés spécifiques analogiques ont préalablement été conçus par l’équipe pour simuler le comportement de plusieurs types de neurones selon le formalisme de Hodgkin-Huxley. La contribution de cette thèse consiste à la conception et la réalisation des circuits numériques permettant de gérer la connectivité entre les cellules au sein du réseau de neurones, suivant les règles de plasticité configurées par l’utilisateur. L’implantation de ces règles est réalisée sur des circuits numériques programmables (FPGA) et est optimisée pour assurer un fonctionnement temps réel pour des réseaux de grande taille. Des nouvelles méthodes de calculs et de communication ont été développées pour satisfaire les contraintes temporelles et spatiales imposées par le degré de réalisme souhaité. Entre autres, un protocole de communication basé sur la technique anneau à jeton a été conçu pour assurer le dialogue entre plusieurs FPGAs situés dans un système multicarte tout en garantissant l’aspect temps-réel des simulations. Les systèmes ainsi développés seront exploités par les laboratoires partenaires, neurobiologistes ou informaticiens. / This work has been supported by the European FACETS project. Within this project, we contribute in developing hardware mixed-signal devices for real-time spiking neural network simulation. These devices may potentially contribute to an improved understanding of learning phenomena in the neo-cortex. Neuron behaviours are reproduced using analog integrated circuits which implement Hodgkin-Huxley based models. In this work, we propose a digital architecture aiming to connect many neuron circuits together, forming a network. The inter-neuron connections are reconfigurable and can be ruled by a plasticity model. The architecture is mapped onto a commercial programmable circuit (FPGA). Many methods are developed to optimize the utilisation of hardware resources as well as to meet real-time constraints. In particular, a token-passing communication protocol has been designed and developed to guarantee real-time aspects of the dialogue between several FPGAs in a multiboard system allowing the integration of a large number of neurons. The global system is able to run neural simulations in biological real-time with high degree of realism, and then can be used by neurobiologists and computer scientists to carry on neural experiments.