There have been many different studies performed in order to examine various properties of neurons. One of the most important properties of neurons is an ability to originate and propagate action potential. The action potential is a source of communication between different neural structures located in different anatomical regions. Many different studies use modeling to describe the action potential and its properties. These models mathematically describe physical properties of neurons and analyze and explain biological and electrochemical processes such as action potential initiation and propagation. Therefore, one of the most important functions of neurons is an ability to provide communication between different neural structures located in different anatomical regions. This is achieved by transmitting electrical signals from one part of the body to another. For example, neurons transmit signals from the brain to the motor neurons (efferent neurons) and from body tissues back to the brain (afferent neurons). This communication process is extremely important for a being to function properly. One of the most valuable studies in neuroscience was conducted by Alan Hodgkin and Andrew Huxley. In their work, Alan Hodgkin and Andrew Huxley used a giant squid axon to create a mathematical model which analyzes and explains the ionic mechanisms underlying the initiation and propagation of action potentials. They received the 1963 Nobel Prize in Physiology/Medicine for their valuable contribution to medical science. The Hodgkin and Huxley model is a mathematical model that describes how the action potential is initiated and how it propagates in a neuron. It is a set of nonlinear ordinary differential equations that approximates the electrical characteristics of excitable cells such as neurons and cardiomyocytes. This work focuses on modeling the Hodgkin and Huxley model using MATLAB extension - Simulink. This tool provides a graphical editor, customizable block libraries, and solvers for modeling and simulating dynamic systems. Simulink model is used to describe the mechanisms and underlying processes involved in action potential initiation and propagation.

Dans ces travaux de thèse, nous nous sommes intéressés à l'influence du bruit synaptique sur la plasticité synaptique dans un réseau de neurones biophysiquement réalistes. Le simulateur utilisé est un système électronique neuromorphique. Nous avons implanté un modèle de neurones à conductances basé sur le formalisme de Hodgkin et Huxley, et un modèle biophysique de plasticité. Ces travaux ont inclus la configuration du système, le développement d'outils pour l'exploiter, son utilisation ainsi que la mise en place d'une plateforme le rendant accessible à la communauté scientifique via Internet et l'utilisation de scripts PyNN (langage de description de simulations en neurosciences computationnelles). / In this work, we have investigated the effect of input noise patterns on synaptic plasticity applied to a neural network. The study was realised using a neuromorphic hardware simulation system. We have implemented a neural conductance model based on Hodgkin and Huxley formalism, and a biophysical model for plasticity. The tasks performed during this thesis project included the configuration of the system, the development of software tools, the analysis tools to explore experimental results, and the development of the software modules for the remote access to the system via Internet using PyNN scripts (PyNN is a neural network description language commonly used in computational neurosciences).

Dans ces travaux de thèse, nous nous sommes intéressés à l'infuence du bruit synaptique sur la plasticité synaptique dans un réseau de neurones biophysiquement réalistes. Le simulateur utilisé est un système électronique neuromorphique. Nous avons implanté un modèle de neurones à conductances basé sur le formalisme de Hodgkin et Huxley, et un modèle biophysique de plasticité. Ces travaux ont inclus la configuration du système, le développement d'outils pour l'exploiter, son utilisation ainsi que la mise en place d'une plateforme le rendant accessible à la communauté scientique via Internet et l'utilisation de scripts PyNN (langage de description de simulations en neurosciences computationnelles).

Made available in DSpace on 2017-07-21T19:25:53Z (GMT). No. of bitstreams: 1 Rafael R Borges.pdf: 2438169 bytes, checksum: 7ddddb02731c3c29cfcf61b843e0f82d (MD5) Previous issue date: 2016-05-11 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / In this thesis, it was investigated the influence of synaptic plasticity in the dynamics of neuronal networks. Specifically, we analyzed the effect on creation and suppression of synchronization spikes in networks composed of neurons with excitatory and inhibitory synapses. The spike timing-dependent plasticity (STDP) changes the strength of existing synapses in the neuronal network. To simulate the dynamics of each neuron, we considered the Hodgkin and Huxley model (HH), that is able to provide the main features of the temporal evolution of the membrane potential of each cell. The Kuramoto order parameter was utilized as synchronization diagnostic. First of all, we have studied the dynamics spikes in neuronal networks on a global and random topology with excitatory synapses with plasticity (STDP). It was observed that the STDP improves synchronization in a sufficiently dense neuronal networks. However, this effect is maximized by the insertion of an external perturbation of moderate intensity. Further, the system behavior was analyzed using the combination of inhibitory and excitatory synapses, both with spike timing-dependent plasticity. Our results indicated that the network becomes desynchronized when the intensity of inhibitory synapses is increased. Nevertheless, for small intensities of these synapses there was an increase in the values of the order parameter when the system with STDP was perturbed. / Nesta tese foi investigada a influência dos modelos de plasticidade sináptica na dinâmica de redes neuronais. Especificamente, foi analisado o efeito da plasticidade na criação e supressão da sincronização ao de disparos em redes compostas por neurônios com sinapses excitatórias e inibitórias. O modelo de plasticidade sináptica dependente do tempo entre disparos (do inglês: Spike-timing-dependent plasticity: STDP), modifica a intensidade das sinapses existentes na rede neuronal. Para simular a dinâmica de cada neurônio foi utilizado o modelo de Hodgkin e Huxley (HH), que ´e capaz de fornecer as principais características da evolução ao temporal do potencial de membrana de cada célula. Como diagnóstico de sincronização foi utilizado o parâmetro de ordem de Kuramoto. Primeiramente foi investigada a dinâmica de disparos em redes neuronais com topologia global e aleatória com sinapses excitatórias com plasticidade (STDP). Observou-se que a STDP contribui para a sincronização ao do sistema em redes neuronais suficientemente densas. No entanto, este efeito é maximizado com a inserção de uma perturbação o externa de intensidade moderada. Na sequência, foi analisado o comportamento do sistema com a combinação de sinapses excitatórias e inibitórias, ambas com STDP. Os resultados indicaram que a rede torna-se não sincronizada com o aumento da intensidade das sinapses inibitórias. Entretanto, para pequenas intensidades destas sinapses, observou-se um acréscimo nos valores do parâmetro de ordem quando o sistema com STDP foi perturbado.

Dans ces travaux de thèse, nous nous sommes intéressés à l'in fluence du bruit synaptique sur la plasticité synaptique dans un réseau de neurones biophysiquement réalistes. Le simulateur utilisé est un système électronique neuromorphique. Nous avons implanté un modèle de neurones à conductances basé sur le formalisme de Hodgkin et Huxley, et un modèle biophysique de plasticité. Ces travaux ont inclus la con figuration du système, le développement d'outils pour l'exploiter, son utilisation ainsi que la mise en place d'une plateforme le rendant accessible à la communauté scientifique via Internet et l'utilisation de scripts PyNN (langage de description de simulations en neurosciences computationnelles).

Ces travaux ont été menés dans le cadre du projet européen FACETS-ITN. Nous avons contribué à la simulation de cellules corticales grâce à des données expérimentales d'électrophysiologie comme référence et d'un circuit intégré neuromorphique comme simulateur. Les propriétés intrinsèques temps réel de nos circuits neuromorphiques à base de modèles à conductance, autorisent une exploration détaillée des différents types de neurones. L'aspect analogique des circuits intégrés permet le développement d'un simulateur matériel temps réel à l'échelle du réseau. Le deuxième objectif de cette thèse est donc de contribuer au développement d'une plate-forme mixte - matérielle et logicielle - dédiée à la simulation de réseaux de neurones impulsionnels. / This work has been supported by the European FACETS-ITN project. Within the frameworkof this project, we contribute to the simulation of cortical cell types (employingexperimental electrophysiological data of these cells as references), using a specific VLSIneural circuit to simulate, at the single cell level, the models studied as references in theFACETS project. The real-time intrinsic properties of the neuromorphic circuits, whichprecisely compute neuron conductance-based models, will allow a systematic and detailedexploration of the models, while the physical and analog aspect of the simulations, as opposedthe software simulation aspect, will provide inputs for the development of the neuralhardware at the network level. The second goal of this thesis is to contribute to the designof a mixed hardware-software platform (PAX), specifically designed to simulate spikingneural networks. The tasks performed during this thesis project included: 1) the methodsused to obtain the appropriate parameter sets of the cortical neuron models that can beimplemented in our analog neuromimetic chip (the parameter extraction steps was validatedusing a bifurcation analysis that shows that the simplified HH model implementedin our silicon neuron shares the dynamics of the HH model); 2) the fully customizablefitting method, in voltage-clamp mode, to tune our neuromimetic integrated circuits usinga metaheuristic algorithm; 3) the contribution to the development of the PAX systemin terms of software tools and a VHDL driver interface for neuron configuration in theplatform. Finally, it also addresses the issue of synaptic tuning for future SNN simulation.

This work has been supported by the European FACETS-ITN project. Within the frameworkof this project, we contribute to the simulation of cortical cell types (employingexperimental electrophysiological data of these cells as references), using a specific VLSIneural circuit to simulate, at the single cell level, the models studied as references in theFACETS project. The real-time intrinsic properties of the neuromorphic circuits, whichprecisely compute neuron conductance-based models, will allow a systematic and detailedexploration of the models, while the physical and analog aspect of the simulations, as opposedthe software simulation aspect, will provide inputs for the development of the neuralhardware at the network level. The second goal of this thesis is to contribute to the designof a mixed hardware-software platform (PAX), specifically designed to simulate spikingneural networks. The tasks performed during this thesis project included: 1) the methodsused to obtain the appropriate parameter sets of the cortical neuron models that can beimplemented in our analog neuromimetic chip (the parameter extraction steps was validatedusing a bifurcation analysis that shows that the simplified HH model implementedin our silicon neuron shares the dynamics of the HH model); 2) the fully customizablefitting method, in voltage-clamp mode, to tune our neuromimetic integrated circuits usinga metaheuristic algorithm; 3) the contribution to the development of the PAX systemin terms of software tools and a VHDL driver interface for neuron configuration in theplatform. Finally, it also addresses the issue of synaptic tuning for future SNN simulation.