Os aspectos morfológicos dos neurônios e estruturas neurais, embora potencialmente importantes, têm recebido relativamente pouca atenção na literatura em neurociência. Este trabalho consiste numa substancial parte de um projeto em desenvolvimento no Grupo de Pesquisa em Visão Cibernética voltado para o estudo da relação formal/função neural. Mais especificamente, o presente trabalho dedica particular atenção para a síntese, modelagem e simulação de estruturas neurais morfologicamente realísticas. A tese se inicia com revisões bibliográficas sobre visão biológica e neurociência, direcionadas aos assuntos a serem aqui abordados. Começamos a descrição dos desenvolvimentos com um levantamento, avaliação e proposta de medidas neuromorfométricas adequadas para exprimir as propriedades mais representativas para nosso trabalho, tais como cobertura espacial, complexidade e decaimento eletrônico. Incluímos nessa parte a metodologia utilizada para a geração de neurônios artificiais bidimensionais estatisticamente semelhantes aos naturais. Apresenta-se também a extensão desta metodologia para o caso tridimensional, validada pela análise neuroinorfométrica dos neurônios gerados. Na seqüência, descrevemos o processo de geração de estruturas neurais compostas de neurônios. Considerando modelos com uma camada neural para a codificação de especificidade de orientação, mas sem levar em conta a forma neural, vários casos são simulados, utilizando gradientes na distribuição dos pesos sinápticos e distribuições regulares ou aleatórias (uniformes) dos neurônios na estrutura. A extensão dessas simulações utilizando estruturas que consideram mais detalhadamente a forma neural, usando agora neurônios artificiais gerados pelo método descrito nesta monografia, é apresentada na seqüência. Entre outros efeitos, mostramos que a extensão da arborização dendrítica é um fator determinante da taxa de convergência e seletividade nos modelos, e que gradientes na extensão das arborizações sinápticas são essenciais para a adequada codificação de orientações em módulos cêntricos contendo somatas aleatoriamente distribuídos. / The morphological aspects of neurons and neural structures, although potentially important, have received relatively little attention in the literature in neuroscience. This work consists in a substantial part of a project in development at the Cybernetic Vision Research Group, directed to the study of the form/function relationship. More specifically, the present work dedicates particular attention to the synthesis, modeling, and simulation of morphologically realistic neural structures. The thesis begins with a bibliographic review about biological vision and neuroscience, focusing on the subjects to be here considered. We start the description of the developments with the revision; evaluation and proposal of neuromorphometric measures adequate express the properties more representative to the work, such as spatial cover, complexity and electrotonic decay. We include in this part the methodology used for the generation of bidimensional artificial neurons statistically similar to natural ones. The extension of these developments to the tridimensional case, including their respective validation (performed in terms of neuromorphometric analysis of the generated neurons) is also presented. Next, we describe the generation process of neural structures composed of neurons. Using one-layer neural models for orientation specificity encoding, but without considering the neural shape, several cases are simulated, using gradients in the distribution of the synaptic weights and regular or random (uniform) distributions of the neurons in the structures. The extension of these simulations using structures that consider the neural form in more detail, composed of artificial neurons generated by the described method in this monograph is presented in the sequence. We show that the extension of the dendritic arborization is a determinant factor on the convergence rate and selectivity in the models, and that gradients in the extension of the synaptic arborizations are essentials for the adequate codification of orientations in centric models containing distributed random somata.

Investigamos o papel de condutâncias iônicas lentas na transmissão/codificação de informação entre neurônios que disparam em rajadas ou bursts. Para isso, desenvolvemos um protocolo experimental no qual a interação em tempo real entre computador e neurônio biológico permite isolar o efeito da dinâmica de um determinado tipo de canal iônico e estudar sua inuência nos mecanismos de codificação de informação. Os experimentos foram realizados com neurônios do gânglio estomatogástrico do siri azul, Callinectes sapidus, que não possuem condutâncias lentas capazes de fazê-los apresentar rajadas de disparos quando in vitro, condição na qual apresentam comportamento quiescente ou disparam tonicamente. Durante os experimentos, alteramos artificialmente o comportamento de um destes neurônios, conectando-o a um computador que introduz uma corrente capaz de fazê-lo apresentar rajadas. Essa corrente possui uma componente senoidal (vinda de um gerador de funções) e uma componente devido a uma condutância iônica lenta modelada matematicamente. A condutância lenta pode ser escolhida entre duas versões: uma em que a condutância é calculada em tempo real, a partir do valor instantâneo do potencial de membrana do neurônio biológico, e outra em que o valor da condutância é oriundo de uma série temporal previamente gravada. A fonte de informação utilizada nos experimentos é um neurônio artificial pré-sináptico, que possui uma distribuição de potenciais de ação (spikes) escolhida pelo experimentador, e é conectado ao neurônio biológico modificado através de um modelo de sinapse química inibidora. A quantidade de informação do neurônio artificial (variabilidade dos padrões de disparo) codificada pelo neurônio biológico é inferida calculando-se a informação mútua média entre eles para as duas versões da condutância lenta (dinâmica ou previamente gravada). Nossos experimentos reproduziram qualitativamente as observações feitas por nosso grupo no circuito pilórico intacto do siri, que possui neurônios conectados por mutua inibição que naturalmente apresentam bursts. Além disso, observamos que vários picos de informação mútua média, presentes quando a condutância é dinâmica, desaparecem quando esta é substituída pela série temporal previamente gravada da condutância. Assim, pudemos confirmar os resultados previamente obtidos com simulações computacionais em que foram utilizados apenas modelos matemáticos e na ausência de ruído e demonstramos que as condutâncias iônicas lentas constituem um mecanismo biofísico que permite a codificação de estímulos sinápticos em neurônios que apresentam rajadas. / We investigated the role of slow ionic conductances on information processing by bursting neurons. A real time experimental protocol was developed to allow interacting computer models and biological neurons to address the effect of dynamical details of a single type of ion channel in information coding mechanisms. We experimented on Callinectes sapidus (blue crab) stomatogastric ganglion neurons. Such neurons were chosen because they do not present the slow conductances that can led to bursting activity in vitro (in such conditions they can be found either in a quiescent or in a tonic firing state). The experiments consisted in artificially changing the behavior of one of these neurons by injecting a computer generated current to achieve bursting. Such current has a sinusoidal component (from a function generator) and a component due to mathematical model of a slow ionic conductance. The slow conductance was implemented in two versions: in one of them the instantaneous value of the conductance is computed in real time and according to the membrane potential of the biological neuron, in another version the value of the conductance simply comes from a time series previously stored in the computer. A pre-synaptic artificial neuron, with a spike distribution chosen by the experimenter, provided input for the biological neuron through an artificial chemical inhibitory synapse. The amount of information (variability of spike patterns from the artificial neuron) coded by the biological neuron was inferred by calculating the average mutual information along stimulus and response bursts for the two conditions of the slow conductance (dynamically calculated or from file previously stored). Our experiments reproduced the results found in intact pyloric central pattern generator bursting neurons connected by mutual inhibition. Moreover, we show that the average mutual information peaks, found when the conductance is dynamically calculated, disappear when we use the previously recorded time series of the conductance. Such results validate those only found previously in numerical simulations in the absence of noise and point the role of the slow ionic conductances in a biophysical mechanism that allow bursting motor neurons to encode in a nontrivial fashion the information they receive through a single synapse.

Comprendre le comportement du système visuel rétino-thalamo-cortico-colliculaire (i.e. précoce) dans une situation d'images naturelles est d'une importance capitale pour comprendre ce qui se passe ensuite dans le cerveau. Pour comprendre ces comportements, les neurobiologistes ont étudié les voies standard, Parvocellulaires et Magnocellulaires, depuis des décennies. Cependant, il y a aussi la voie non-standard, ou Koniocellulaire, qui joue un rôle modulateur important dans les traitements local, global, et entremêlé, pour atteindre de tels comportements. Particulièrement, l'analyse standard du mouvement réalisée par la voie Magno est alternée avec des réactions rapides, comme la fuite ou l'approche à des mouvements spécifiques, qui sont pré-câblés dans la voie Konio. De plus, l'étude d'une tâche de fixation dans une situation réelle, par exemple quand un prédateur s'approche lentement de sa proie, implique non seulement un mécanisme de mouvement, mais nécessite également l'utilisation de la voie Parvo, qui analyse, au moins, le contraste de l'image. Ici, nous étudions dans un modèle neuronal de calcul bio-inspiré comment ces voies peuvent être modélisées avec un ensemble minimal de paramètres, afin de fournir des résultats numériques robustes lors d'une tâche réelle. Ce modèle repose sur une étude approfondie pour intégrer des éléments biologiques dans l'architecture des circuits, les constantes de temps et les caractéristiques de fonctionnement des neurones. Nos résultats montrent que notre modèle, bien que fonctionnant via des calculs locaux, montre globalement un bon comportement de réseau en termes d'espace et de temps, et permet d'analyser et de proposer des interprétations de l'interaction entre le thalamus et le cortex. À une échelle plus macroscopique, les comportements du modèle sont reproductibles et peuvent être qualitativement comparés à des mesures de fixation oculaire chez l'homme. Cela est également vrai lorsque l'on utilise des images naturelles, où quelques paramètres sont légèrement modifiés, en gardant des résultats qualitativement humains. Les résultats de robustesse montrent que les valeurs précises des paramètres ne sont pas critiques, mais leur ordre de grandeur l'est. Une instabilité numérique ne se produit qu'après une variation de 100% d'un paramètre. Nous pouvons donc conclure que cette approche systémique est capable de représenter les changements de l'attention en utilisant des images naturelles, tout en étant algorithmiquement robuste. Cette étude nous donne ainsi une interprétation possible sur le rôle de la voie Konio, tandis qu'en même temps elle nous permet de participer au débat sur les low et high-roads des flux attentionnel et émotionnel. Néanmoins, d'autres informations, comme la couleur, sont également présentes dans le système visuel précoce, et pourraient être prises en considération, ainsi que des mécanismes corticaux plus complexes, dans les perspectives de ce travail / Understanding the behavior of the retino-thalamo-cortico-collicular (i.e. early) visual system in a natural images situation is of utmost importance to understand what further happens in the brain. To understand these behaviors, neuroscientists have looked at the standard Parvocellular and Magnocellular pathways for decades. However, there is also the non-standard Koniocellular pathway, which plays an important modulating role in the local, global, and intermingled processing carried out to achieve such behaviors. Particularly, the standard motion analysis carried out by the Magno pathway is alternated with rapid reactions, like fleeing or approaching to specific motions, which are hard-wired in the Konio pathway. In addition, studying a fixation task in a real situation, e.g., when a predator slowly approaches its prey, not only involves a motion mechanism, but also requires the use of the Parvo pathway, analyzing, at least, the image contrast. Here, we study in a bio-inspired computational neural model how these pathways can be modeled with a minimal set of parameters, in order to provide robust numerical results when doing a real task. This model is based upon an important study to integrate biological elements about the architecture of the circuits, the time constants and the operating characteristics of the different neurons. Our results show that our model, despite operating via local computations, globally shows a good network behavior in terms of space and time, and allows to analyze and propose interpretations to the interplay between thalamus and cortex. At a more macroscopic scale, the behaviors emerging from the model are reproducible and can be qualitatively compared to human-made fixation measurements. This is also true when using natural images, where just a few parameters are slightly modified, keeping the qualitatively human-like results. Robustness results show that the precise values of the parameters are not critical, but their order of magnitude matters. Numerical instability occurs only after a 100% variation of a parameter. We thus can conclude that such a reduced systemic approach is able to represent attentional shifts using natural images, while also being algorithmically robust. This study gives us as well a possible interpretation about the role of the Konio pathway, while at the same time allowing us to participate in the debate between low and high-roads in the attentional and emotional streams. Nevertheless, other information, such as color, is also present in the early visual system, and should be addressed together with more complex cortical mechanisms in a sequel of this work

Made available in DSpace on 2014-12-17T14:54:59Z (GMT). No. of bitstreams: 1 ElderECO_TESE_Capa_ate_pag86.pdf: 3877192 bytes, checksum: d9b068e5eaa76a69d5a1fa1f245dbc5d (MD5) Previous issue date: 2011-09-02 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / The search for ever smaller device and without loss of performance has been increasingly investigated by researchers involving applied electromagnetics. Antennas using ceramics materials with a high dielectric constant, whether acting as a substract element of patch radiating or as the radiant element are in evidence in current research, that due to the numerous advantages offered, such as: low profile, ability to reduce the its dimensions when compared to other devices, high efficiency of ratiation, suitability the microwave range and/or millimeter wave, low temperature coefficient and low cost. The reason for this high efficiency is that the dielectric losses of ceramics are very low when compared to commercially materials sold used in printed circuit boards, such as fiberglass and phenolite. These characteristics make ceramic devices suitable for operation in the microwave band. Combining the design of patch antennas and/or dielectric resonator antenna (DRA) to certain materials and the method of synthesis of these powders in the manufacture of devices, it s possible choose a material with a dielectric constant appropriate for the design of an antenna with the desired size. The main aim of this work is the design of patch antennas and DRA antennas on synthesis of ceramic powders (synthesis by combustion and polymeric precursors - Pe- chini method) nanostructured with applications in the microwave band. The conventional method of mix oxides was also used to obtain nanometric powders for the preparation of tablets and dielectric resonators. The devices manufactured and studied on high dielectric constant materials make them good candidates to have their small size compared to other devices operating at the same frequency band. The structures analyzed are excited by three different techniques: i) microstrip line, ii) aperture coupling and iii) inductive coupling. The efficiency of these techniques have been investigated experimentally and compared with simulations by Ansoft HFSS, used in the accurate analysis of the electromagnetic behavior of antennas over the finite element method (FEM). In this thesis a literature study on the theory of microstrip antennas and DRA antenna is performed. The same study is performed about the materials and methods of synthesis of ceramic powders, which are used in the manufacture of tablets and dielectric cylinders that make up the devices investigated. The dielectric media which were used to support the analysis of the DRA and/or patch antennas are analyzed using accurate simulations using the finite difference time domain (FDTD) based on the relative electrical permittivity (er) and loss tangent of these means (tand). This work also presents a study on artificial neural networks, showing the network architecture used and their characteristics, as well as the training algorithms that were used in training and modeling some parameters associated with the devices investigated / A busca por dispositivos cada vez menores e sem perda de desempenho vem sendo cada dia mais investigada pelos pesquisadores da ?rea envolvendo eletromagnetismo apli- cado. Antenas utilizando materiais cer?micos com uma alta constante diel?trica, sejam elas atuando como substrato do elemento patch radiante ou como sendo o pr?prio ele- mento radiante est?o em evid?ncia nas pesquisas atuais, isso devido ?s in?meras vantagens que apresentam, tais como: baixo perfil, capacidade de redu??o de suas dimens?es (quando comparado a outros dispositivos), alta efici?ncia de radia??o, adequabilidade a faixa de micro-ondas e/ou ondas milim?tricas, baixo coeficiente de temperatura e baixo custo. A raz?o para essa alta efici?ncia ? que as perdas diel?tricas das cer?micas s?o muito baixas, quando comparadas ?s dos materiais comercialmente usados em placas de circuito impresso, tais como: fibra de vidro e fenolite. Essas caracter?sticas tornam os dispositivos cer?micos adequados para operar na faixa de micro-ondas. Aliando o projeto de antenas patch e/ou antenas ressoadoras diel?tricas (DRA) ao uso de certos materiais e ao m?todo de s?ntese desses p?s na fabrica??o dos dispositivos, ? poss?vel escolher um material com uma determinada constante diel?trica para o projeto de uma antena com o tamanho desejado. O objetivo principal deste trabalho consiste no projeto de antenas patches e antenas DRA sob s?ntese de p?s cer?micos (s?ntese por combust?o e por precursores polim?ricos - m?todo Pechini) nanoestruturados para aplica??es na faixa de micro-ondas. O m?todo convencional de mistura de ?xidos tamb?m foi utilizado na obten??o de p?s nanom?tricos para a confec??o das pastilhas e ressoadores diel?tricos. Os dispositivos fabricados e estudados sobre materiais de alta constante diel?trica os tornam bons candidatos ? fabrica??o de dispositivos e circuitos de dimens?es reduzidas quando comparado aos outros dispositivos tradicionais operando na mesma faixa de frequ?ncia. As estruturas analisadas s?o excitadas por tr?s diferentes t?cnicas: i) linha de microfita, ii) acoplamento por abertura e iii) acoplamento indutivo. A efici?ncia dessas t?cnicas de alimenta??o s?o investigadas experimentalmente e comparada com simula??es realizadas pelo Ansoft HFSS, utilizado na an?lise precisa do comportamento eletromagn?tico das antenas atrav?s do m?todo dos elementos finitos (FEM). Nesta tese um estudo bibliogr?fico sobre teoria de antenas de microfita e antenas DRA ? realizado. O mesmo estudo ? realizado a respeito dos materiais e dos m?todos de s?ntese dos p?s cer?micos que s?o utilizados na fabri- ca??o das pastilhas e dos cil?ndros diel?tricos que compor?o os dispositivos investigados. Os meios diel?tricos os quais serviram de suporte na an?lise das antenas patch e/ou DRA s?o analisados atrav?s de simula??es precisas utilizando o m?todo das diferen?as finitas no dom?nio do tempo (FDTD) com base na permissividade el?trica relativa (er) e tangente de perda desses meios (tand). Este trabalho ainda apresenta um estudo em redes neurais artificiais, evidenciando a arquitetura de rede utilizada e suas caracter?sticas, bem como os algoritmos de treinamento que foram usados no treinamento e na modelagem de alguns par?metros associados aos dispositivos investigados

Investigamos o papel de condutâncias iônicas lentas na transmissão/codificação de informação entre neurônios que disparam em rajadas ou bursts. Para isso, desenvolvemos um protocolo experimental no qual a interação em tempo real entre computador e neurônio biológico permite isolar o efeito da dinâmica de um determinado tipo de canal iônico e estudar sua inuência nos mecanismos de codificação de informação. Os experimentos foram realizados com neurônios do gânglio estomatogástrico do siri azul, Callinectes sapidus, que não possuem condutâncias lentas capazes de fazê-los apresentar rajadas de disparos quando in vitro, condição na qual apresentam comportamento quiescente ou disparam tonicamente. Durante os experimentos, alteramos artificialmente o comportamento de um destes neurônios, conectando-o a um computador que introduz uma corrente capaz de fazê-lo apresentar rajadas. Essa corrente possui uma componente senoidal (vinda de um gerador de funções) e uma componente devido a uma condutância iônica lenta modelada matematicamente. A condutância lenta pode ser escolhida entre duas versões: uma em que a condutância é calculada em tempo real, a partir do valor instantâneo do potencial de membrana do neurônio biológico, e outra em que o valor da condutância é oriundo de uma série temporal previamente gravada. A fonte de informação utilizada nos experimentos é um neurônio artificial pré-sináptico, que possui uma distribuição de potenciais de ação (spikes) escolhida pelo experimentador, e é conectado ao neurônio biológico modificado através de um modelo de sinapse química inibidora. A quantidade de informação do neurônio artificial (variabilidade dos padrões de disparo) codificada pelo neurônio biológico é inferida calculando-se a informação mútua média entre eles para as duas versões da condutância lenta (dinâmica ou previamente gravada). Nossos experimentos reproduziram qualitativamente as observações feitas por nosso grupo no circuito pilórico intacto do siri, que possui neurônios conectados por mutua inibição que naturalmente apresentam bursts. Além disso, observamos que vários picos de informação mútua média, presentes quando a condutância é dinâmica, desaparecem quando esta é substituída pela série temporal previamente gravada da condutância. Assim, pudemos confirmar os resultados previamente obtidos com simulações computacionais em que foram utilizados apenas modelos matemáticos e na ausência de ruído e demonstramos que as condutâncias iônicas lentas constituem um mecanismo biofísico que permite a codificação de estímulos sinápticos em neurônios que apresentam rajadas. / We investigated the role of slow ionic conductances on information processing by bursting neurons. A real time experimental protocol was developed to allow interacting computer models and biological neurons to address the effect of dynamical details of a single type of ion channel in information coding mechanisms. We experimented on Callinectes sapidus (blue crab) stomatogastric ganglion neurons. Such neurons were chosen because they do not present the slow conductances that can led to bursting activity in vitro (in such conditions they can be found either in a quiescent or in a tonic firing state). The experiments consisted in artificially changing the behavior of one of these neurons by injecting a computer generated current to achieve bursting. Such current has a sinusoidal component (from a function generator) and a component due to mathematical model of a slow ionic conductance. The slow conductance was implemented in two versions: in one of them the instantaneous value of the conductance is computed in real time and according to the membrane potential of the biological neuron, in another version the value of the conductance simply comes from a time series previously stored in the computer. A pre-synaptic artificial neuron, with a spike distribution chosen by the experimenter, provided input for the biological neuron through an artificial chemical inhibitory synapse. The amount of information (variability of spike patterns from the artificial neuron) coded by the biological neuron was inferred by calculating the average mutual information along stimulus and response bursts for the two conditions of the slow conductance (dynamically calculated or from file previously stored). Our experiments reproduced the results found in intact pyloric central pattern generator bursting neurons connected by mutual inhibition. Moreover, we show that the average mutual information peaks, found when the conductance is dynamically calculated, disappear when we use the previously recorded time series of the conductance. Such results validate those only found previously in numerical simulations in the absence of noise and point the role of the slow ionic conductances in a biophysical mechanism that allow bursting motor neurons to encode in a nontrivial fashion the information they receive through a single synapse.

One of the most powerful and important applications that the brain accomplishes is solving the sensory "cocktail party problem:" to adaptively suppress extraneous signals in an environment. Theoretical studies suggest that the solution to the problem involves an adaptive filter, which learns to remove the redundant noise. However, neural learning is also in its infancy and there are still many questions about the stability and application of synaptic learning rules for neural computation. In this thesis, the implementation of an adaptive filter in the brain of a weakly electric fish, A. Leptorhynchus, was studied. It was found to require a cerebellar architecture that could supply independent frequency channels of delayed feedback and multiple burst learning rules that could shape this feedback. This unifies two ideas about the function of the cerebellum that were previously separate: the cerebellum as an adaptive filter and as a generator of precise temporal inputs.

One of the most powerful and important applications that the brain accomplishes is solving the sensory "cocktail party problem:" to adaptively suppress extraneous signals in an environment. Theoretical studies suggest that the solution to the problem involves an adaptive filter, which learns to remove the redundant noise. However, neural learning is also in its infancy and there are still many questions about the stability and application of synaptic learning rules for neural computation. In this thesis, the implementation of an adaptive filter in the brain of a weakly electric fish, A. Leptorhynchus, was studied. It was found to require a cerebellar architecture that could supply independent frequency channels of delayed feedback and multiple burst learning rules that could shape this feedback. This unifies two ideas about the function of the cerebellum that were previously separate: the cerebellum as an adaptive filter and as a generator of precise temporal inputs.

One of the most powerful and important applications that the brain accomplishes is solving the sensory "cocktail party problem:" to adaptively suppress extraneous signals in an environment. Theoretical studies suggest that the solution to the problem involves an adaptive filter, which learns to remove the redundant noise. However, neural learning is also in its infancy and there are still many questions about the stability and application of synaptic learning rules for neural computation. In this thesis, the implementation of an adaptive filter in the brain of a weakly electric fish, A. Leptorhynchus, was studied. It was found to require a cerebellar architecture that could supply independent frequency channels of delayed feedback and multiple burst learning rules that could shape this feedback. This unifies two ideas about the function of the cerebellum that were previously separate: the cerebellum as an adaptive filter and as a generator of precise temporal inputs.

One of the most powerful and important applications that the brain accomplishes is solving the sensory "cocktail party problem:" to adaptively suppress extraneous signals in an environment. Theoretical studies suggest that the solution to the problem involves an adaptive filter, which learns to remove the redundant noise. However, neural learning is also in its infancy and there are still many questions about the stability and application of synaptic learning rules for neural computation. In this thesis, the implementation of an adaptive filter in the brain of a weakly electric fish, A. Leptorhynchus, was studied. It was found to require a cerebellar architecture that could supply independent frequency channels of delayed feedback and multiple burst learning rules that could shape this feedback. This unifies two ideas about the function of the cerebellum that were previously separate: the cerebellum as an adaptive filter and as a generator of precise temporal inputs.

Dans ce projet nous nous intéresserons à la création d’un nouveau modèle permettant de simuler des enregistrements extracellulaires de l’activité électrique neurale dans le système nerveux périphérique. Ce modèle fut développé pour permettre une meilleure compréhension de l’impact des différentes propriétés des interfaces sur la qualité des signaux recueillis. Ce projet fut en particulier conduit pour répondre au contexte actuel qui voit le développement de nombreuses applications dans le domaine des neuro-prothèses et autres interfaces neurales à but biomédical. Nos intentions étaient de fournir un nouvel outil permettant de mieux comprendre les particularités des interfaces existantes ou d’aider à leur amélioration et à la planification de futures innovations. Ce modèle est construit comme la synthèse de la compréhension actuelle des différents rouages biophysiques impactant les enregistrements. Sa structure peut être perçue comme l’assemblage de différents sous-systèmes interconnectés et représentant chacun une dimension du processus. Il s’avère particulièrement efficace pour l’analyse comparative des performances entre diffèrent types/géométries d’électrodes invasives. Dans ce document, nous nous efforcerons d’expliquer en détail la structure et les paramètres de notre modèle. Nous décrirons ensuite les différents tests que nous avons entrepris pour sa validation expérimentale, ainsi que les différentes voies d’applications que nous avons commencé à explorer. Nous finirons par décrire les améliorations qui nous sont apparues comme nécessaires ou possibles et par une discussion sur les ouvertures futures offerte à ce domaine de recherche. / Neural interfaces are becoming a newly dynamic and promising field especially thanks to the numerous applications they could have in the biomedical domain. A great deal of these applications requires a monitoring of targeted neural activity. Among the different technologies available for such recording practice, chronic electrodes implanted in the peripheral nervous system offer a good compromise on the resolution versus invasiveness technological constraint. A large array of electrodes has been developed in this intention but there is still only a limited comprehension of their recording principles and weakness. This makes difficult any targeted improvement of the electrodes and led this field to be mainly dominated by a trial and error empirical approach simultaneously costly in funds, animal lives and time. In particular, intrafascicular electrodes, while providing exiting results for stimulation, have often failed in recordings. These electrodes typically show interesting recording performance right after implantation but have rapid decline of their efficacy up to the points that they often become useless after a few weeks. Such performance proves problematic as they drastically limit the transfer of experimental results to human applications. The extent of our work has been the development of a theoretical framework for the study of implantable electrodes. Our goal here has been to construct a model that could be used as a platform to better understand implanted electrode and compare their performance and possible improvement. We focused our work on intrafascicular electrode for the peripheral nervous system. However, our procedure could easily be applied to other type of interface. During this project we first constructed a detailed model of the recording biophysical process happening at the peripheral nerve electrical interface. This model encompasses all the mechanism known to influence the quality and shape of neural activity recordings. We have then recreated within our model specific controlled experiments and by comparing the properties of the simulated recording with their experimental counterparts demonstrated the potency of our approach to produce bio-plausible signals. This validated our model as an in silico alternative to compare and test electrodes. We then further developed this model to also simulate some of the changes happening in the nerve post implantation. In particular, we found that the growth of the fibrotic scar could already explain a large part of the signal degradation happening in the first weeks. Then to demonstrate the adaptability of this model we used it to compare the performance of the main type of electrodes implanted nowadays peripherally. Finally, as the main weakness of our model relied in its relative complexity and the related long computing time, we started to analyze how this model could be simplified without losing the precision necessary for the intended applications. In conclusion, this project led to the creation of a model which in its current form can be used as an in silico platform to test and compare electrodes. This will facilitate the planning and development of future peripheral neural interface by proving both more economical and informative that current strategies. Conjointly, we opened the way to future improvement of our model, leading to more practicality.