Les robots vont peu à peu intégrer nos foyers sous la forme d’assistants et de compagnons,humanoïdes ou non. Afin de remplir leur rôle efficacement ils devront s’adapter àl’utilisateur, notamment en apprenant de celui-ci le savoir ou les capacités qui leur fontdéfaut. Dans ce but, leur manière d’interagir doit être naturelle et évoquer les mêmesmécanismes coopératifs que ceux présent chez l’homme. Au centre de ces mécanisme setrouve le concept d’action : qu’est-ce qu’une action, comment les humains les reconnaissent,comment les produire ou les décrire ? La modélisation de toutes ces fonctionnalitésconstituera la fondation de cette thèse et permettra la mise en place de mécanismescoopératifs de plus haut niveau, en particulier les plan partagés qui permettent à plusieursindividus d’oeuvrer de concert afin d’atteindre un but commun. Finalement, je présenteraiune différence fondamentale entre la représentation de la connaissance chez l’homme etchez la machine, toujours dans le cadre de l’interaction coopérative : la dissociation possibleentre le corps d’un robot et sa cognition, ce qui n’est pas imaginable chez l’homme. Cettedissociation m’amènera notamment à explorer le « shared experience framework », unesituation dans laquelle une cognition artificielle centrale gère l’expérience partagée demultiples individus ayant chacun une identité propre. Cela m’amènera finalement àquestionner les différentes philosophies de l’esprit du point de vue de l’attribution d’unesprit à une machine et de ce que cela impliquerai quant à l’esprit humain. / Robots will gradually integrate our homes wielding the role of companions, humanoids ornot. In order to cope with this status they will have to adapt to the user, especially bylearning knowledge or skills from him that they may lack. In this context, their interactionshould be natural and evoke the same cooperative mechanisms that humans use. At thecore of those mechanisms is the concept of action: what is an action, how do humansrecognize them, how they produce or describe them? The modeling of aspects of thesefunctionalities will be the basis of this thesis and will allow the implementation of higherlevel cooperative mechanisms. One of these is the ability to handle “shared plans” whichallow two (or more) individuals to cooperate in order to reach a goal shared by all.Throughout the thesis I will attempt to make links between the human development ofthese capabilities, their neurophysiology, and their robotic implementation. As a result ofthis work, I will present a fundamental difference between the representation of knowledgein humans and machines, still in the framework of cooperative interaction: the possibledissociation of a robot body and its cognition, which is not easily imaginable for humans.This dissociation will lead me to explore the “shared experience framework, a situationwhere a central artificial cognition manages the shared knowledge of multiple beings, eachof them owning some kind of individuality. In the end this phenomenon will interrogate thevarious philosophies of mind by asking the question of the attribution of a mind to amachine and the consequences of such a possibility regarding the human mind.

O sistema olfatório é notável por sua capacidade de discriminar odores muito similares, mesmo que estejam misturados. Essa capacidade de discriminação é, em parte, devida a padrões de atividade espaço-temporais gerados nas células mitrais, as células principais do bulbo olfatório, durante a apresentação de um odor. Tais padrões dinâmicos decorrem de interações sinápticas recíprocas entre as células mitrais e interneurônios inibitórios do bulbo olfatório, por exemplo, as células granulares. Nesta tese, apresenta-se um modelo do bulbo olfatório baseado em modelos pulsados das células mitrais e granulares e avalia-se o seu desempenho como sistema reconhecedor de padrões usando-se bases de dados de padrões artificiais e reais. Os resultados dos testes mostram que o modelo possui a capacidade de separar padrões em diferentes classes. Essa capacidade pode ser explorada na construção de sistemas reconhecedores de padrões. Apresenta-se também a ferramenta denominada Nemos, desenvolvida para a implementação do modelo, que é uma plataforma para simulação de neurônios e redes de neurônios pulsados com interface gráfica amigável com o usuário. / The olfactory system is a remarkable system capable of discriminating very similar odorant mixtures. This is in part achieved via spatio-temporal activity patterns generated in mitral cells, the principal cells of the olfactory bulb, during odor presentation. Here, we present a spiking neural network model of the olfactory bulb and evaluate its performance as a pattern recognition system with datasets taken from both artificial and real pattern databases. Our results show that the dynamic activity patterns produced in the mitral cells of the olfactory bulb model by pattern attributes presented to it have a pattern separation capability. This capability can be explored in the construction of high-performance pattern recognition systems. Besides, we proposed Nemos a framework for simulation spiking neural networks through graphical user interface and has extensible models for neurons, synapses and networks.

Humidity measurement has been increasingly important in many industries and process control applications. This thesis research focus mainly on humidity sensor calibration and characterization. The humidity sensor instrumentation is briefly described. The testing infrastructure was designed for sensor data acquisition, in order to compensate the humidity sensor’s temperature coefficient, temperature chambers using Peltier elements are used to achieve easy-controllable stable temperatures. The sensor characterization falls into a multivariate interpolation problem. Neuron networks is tried for non-linear data fitting, but in the circumstance of limited training data, an innovative algorithm was developed to utilize shape preserving polynomials in multiple planes in this kind of multivariate interpolation problems.

O sistema olfatório é notável por sua capacidade de discriminar odores muito similares, mesmo que estejam misturados. Essa capacidade de discriminação é, em parte, devida a padrões de atividade espaço-temporais gerados nas células mitrais, as células principais do bulbo olfatório, durante a apresentação de um odor. Tais padrões dinâmicos decorrem de interações sinápticas recíprocas entre as células mitrais e interneurônios inibitórios do bulbo olfatório, por exemplo, as células granulares. Nesta tese, apresenta-se um modelo do bulbo olfatório baseado em modelos pulsados das células mitrais e granulares e avalia-se o seu desempenho como sistema reconhecedor de padrões usando-se bases de dados de padrões artificiais e reais. Os resultados dos testes mostram que o modelo possui a capacidade de separar padrões em diferentes classes. Essa capacidade pode ser explorada na construção de sistemas reconhecedores de padrões. Apresenta-se também a ferramenta denominada Nemos, desenvolvida para a implementação do modelo, que é uma plataforma para simulação de neurônios e redes de neurônios pulsados com interface gráfica amigável com o usuário. / The olfactory system is a remarkable system capable of discriminating very similar odorant mixtures. This is in part achieved via spatio-temporal activity patterns generated in mitral cells, the principal cells of the olfactory bulb, during odor presentation. Here, we present a spiking neural network model of the olfactory bulb and evaluate its performance as a pattern recognition system with datasets taken from both artificial and real pattern databases. Our results show that the dynamic activity patterns produced in the mitral cells of the olfactory bulb model by pattern attributes presented to it have a pattern separation capability. This capability can be explored in the construction of high-performance pattern recognition systems. Besides, we proposed Nemos a framework for simulation spiking neural networks through graphical user interface and has extensible models for neurons, synapses and networks.

Cette thèse s'intéresse au développement neuronal in vitro dans le but ultime d'enregistrer l'activité de réseaux de neurones à géométrie et connectivité contrôlées. Le développement neuronal est régi par un ensemble de régulations, intrinsèques mais également sous contrôle de facteurs extérieurs, qui permettent à la cellule d'adhérer à un substrat, de croître, et de se polariser. Une partie de ce travail de thèse explore deux types de contraintes physiques de l'environnement que sont la géométrie d'adhésion et la rugosité de surface. La première révèle l'implication des forces dans les stades précoces de développement neuronal régis par un phénomène de compétition neuritique, et permet in fine de contrôler la direction d'émission de l'axone, notamment par une inhibition de sa différenciation sur lignes ondulées. La seconde montre que la distribution des points d'adhésion peut accélérer la croissance jusqu'à favoriser la polarisation axonale. L'autre partie de ce travail s'attache à résoudre le problème technologique majeur qu'est le remplissage des sites d'adhésion par le biais d'une attraction magnétique, et démontre la possibilité de faire croître des réseaux neuronaux modèles sur nanotransistors. / This thesis focuses on in vitro neuronal development, with the long-term goal of building controlled neuron networks that would allow the recording of their electric activity. A collection of intrinsic regulations are involved in neuronal adhesion, growth and polarization, in such a way that the cell can adapt to changes in its environment. Nevertheless, this environment can affect the behavior of the cell through mechanisms that rely on biophysical signals or even physical properties of this environment. The work presented in this thesis is based on the modification of two main aspects of the physical environment: geometry of adhesion and surface roughness. On the one hand, the geometry is controlled by patterns of adhesions, giving the ability to design bipolar motifs that highligt the importance of mechanical forces in neuronal growth, and also more complex motifs that allow the control of neuronal polarization, in particular by an inhibition of axonal differenciation on curved lines. On the other hand, a roughness below the microscale creates a distribution of adhesion points that results in an increase in neuronal growth rate and even influences axonal polarization. The final part of this thesis focuses on the development of an innovative method for placing cells at precise locations on a substrate, by the help of magnetic traps. This method is the final step required for growing model neuron networks on our nanotransistors.

La représentation de l’information mnésique est toujours une question d’intérêt majeur en neurobiologie, mais également, du point de vue informatique, en apprentissage artificiel. Dans certains modèles de réseaux de neurones artificiels, nous sommes confrontés au dilemme de la récupération de l’information sachant, sur la base de la performance du modèle, que cette information est effectivement stockée mais sous une forme inconnue ou trop complexe pour être facilement accessible. C’est le dilemme qui se pose pour les grands réseaux de neurones et auquel tente de répondre le paradigme du « reservoir computing ».Le « reservoir computing » est un courant de modèles qui a émergé en même temps que le modèle que nous présentons ici. Il s’agit de décomposer un réseau de neurones en (1) une couche d’entrée qui permet d’injecter les exemples d’apprentissage, (2) un « réservoir » composé de neurones connectés avec ou sans organisation particulière définie, et dans lequel il peut y avoir des mécanismes d’adaptation, (3) une couche de sortie, les « readout », sur laquelle un apprentissage supervisé est opéré. Nous apportons toutefois une particularité, qui est celle d’utiliser les délais axonaux, temps de propagation d’une information d’un neurone à un autre. Leur mise en oeuvre est un apport computationnel en même temps qu’un argument biologique pour la représentation de l’information. Nous montrons que notre modèle est capable d’un apprentissage artificiel efficace et prometteur même si encore perfectible. Sur la base de ce constat et dans le but d’améliorer les performances nous cherchons à comprendre les dynamiques internes du modèle. Plus précisément nous étudions comment la topologie du réservoir peut influencer sa dynamique. Nous nous aidons pour cela de la théorie des groupes polychrones. Nous avons développé, pour l’occasion, des algorithmes permettant de détecter ces structures topologico-dynamiques dans un réseau, et dans l’activité d’un réseau de topologie donnée.Si nous comprenons les liens entre topologie et dynamique, nous pourrons en tirer parti pour créer des réservoirs adaptés aux besoins de l’apprentissage. Finalement, nous avons mené une étude exhaustive de l’expressivité d’un réseau en termes de groupes polychrones, en fonction de différents types de topologies (aléatoire, régulière, petit-monde) et de nombreux paramètres (nombre de neurones, connectivité, etc.). Nous pouvons enfin formuler un certain nombre de recommandations pour créer un réseau dont la topologie peut être un support riche en représentations possibles. Nous tentons également de faire le lien avec la théorie cognitive de la mémoire à traces multiples qui peut, en principe, être implémentée et étudiée par le prisme des groupes polychrones. / How memory information is represented is still an open question in neurobiology, but also, from the computer science point of view, in machine learning. Some artificial neuron networks models have to face the problem of retrieving information, knowing that, in regard to the model performance, this information is actually stored but in an unknown form or too complex to be easily accessible. This is one of the problems met in large neuron networks and which « reservoir computing » intends to answer.« Reservoir computing » is a category of models that has emerged at the same period as, and has propoerties similar to the model we present here. It is composed of three parts that are (1) an input layer that allows to inject learning examples, (2) a « reservoir » composed of neurons connected with or without a particular predefined, and where there can be adaptation mecanisms, (3) an output layer, called « readout », on which a supervised learning if performed. We bring a particularity that consists in using axonal delays, the propagation time of information from one neuron to another through an axonal connexion. Using delays is a computational improvement in the light of machin learning but also a biological argument for information representation.We show that our model is capable of a improvable but efficient and promising artificial learning. Based on this observation and in the aim of improving performance we seek to understand the internal dynamics of the model. More precisely we study how the topology of the reservoir can influence the dynamics. To do so, we make use of the theory of polychronous groups. We have developped complexe algorithms allowing us to detect those topologicodynamic structures in a network, and in a network activity having a given topology.If we succeed in understanding the links between topology and dynamics, we may take advantage of it to be able to create reservoir with specific properties, suited for learning. Finally, we have conducted an exhaustive study of network expressivness in terms of polychronous groups, based on various types of topologies (random, regular, small-world) and different parameters (number of neurones, conectivity, etc.). We are able to formulate some recommandations to create a network whose topology can be rich in terms of possible representations. We propose to link with the cognitive theory of multiple trace memory that can, in principle, be implemented and studied in the light of polychronous groups.