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This thesis belongs to the computational neuroscience domain in which we aim at understanding complex cognitive functions with computer simulations built on the current knowledge of the brain. The proposed models and simulations are built on the paradigm of dynamic neural fields, that we use in order to study in which way complex congitive capabilities can be the emergent result of the interaction of elementary units.In this thesis, we are interested in the modelisation of visual attention, with and without eye movements. To guide the development of these models, we propose in the first part a review of the current psychological and physiological data on visual attention, before proposing a computational model of visual attention without saccadic eye movement. Then, we study in the second part the way we can integrate saccadic eye movements in our models based on the current anatomical and physiological data on the oculomotor control in the primate. The performances of the different proposed mechanisms are evaluated by simulating visual search tasks with saccadic eye movements.This work also makes us able to study a computational paradigm that relies on distributed, asynchronous, numerical and adaptive computation which permits to consider further developments of the proposed mechanisms on parallel architectures.; Cette thèse s'inscrit dans le domaine des neurosciences computationnelles dont le but est de modéliser des fonctions cognitives complexes par le biais de simulations informatiques et numériques en s'inspirant du fonctionnement cérébral. Les modèles et simulations proposées reposent sur le paradigme des champs neuronaux, que nous exploitons pour étudier dans quelle mesure des capacités cognitives complexes peuvent être le résultat émergeant de l'interaction de cellules élémentaires simples.Nous nous intéressons dans cette thèse à la modélisation de l'attention visuelle, avec ou sans mouvement oculaire. Pour guider le développement de nos modèles, nous proposons dans une première partie une revue de données psychologiques et physiologiques sur l'attention visuelle avant de proposer un modèle computationnel de l'attention visuelle sans saccade oculaire. Puis, nous nous intéressons dans une seconde partie à la manière dont on peut intégrer les saccades oculaires dans nos modèles en s'inspirant des données anatomiques et physiologiques sur le contrôle des saccades oculaires chez le primate. Les performances des différents mécanismes proposés sont évalués en simulation en les appliquant à des tâches de recherche visuelle.Nos travaux de thèse permettent également d'étudier un paradigme de calcul original qui repose sur des calculs distribués, asynchrones, numériques et adaptatifs qui permettent d'envisager le déploiement des mécanismes proposés dans ce cadre sur des supports de calculs parallèles.

International audience; The use of neural networks in finance began by the end of the 1980s and by the beginning of the 1990s, it developed specific applications related to forecasting on the failure of companies. In order to highlight the evolution of this research stream, we have retained and analysed 30 studies in which the authors use neural networks to solve companies' classification problems (healthy and failing firms). This review of all these works gives us the opportunity to stress upon future trends in bankruptcy forecasting research

International audience; The design and topology of a neural network is still an important and difficult task. To solve the problems of topology posed by the introduction of connexionism, new approaches are proposed, and especially a combination of induction rules with a statistical estimation of the neuron coefficients for each layer. This research aims to compare an algorithm of this SLN approach with traditional methods (regression and classical BP neural networks) using the gradient method. Methods are put into application to determine the price-quality relationship of a complex product, the automobile, according to the hedonic price model. This application of the price-quality relationship to the English automobile market leads to the conclusion that the claimed superiority of this approach is unsubstantiated since, compared to the BP neural networks and even linear regression, the performance of the GMDH method is inferior.