Un outil de développement parallèle des réseaux de neurone

Colloque avec actes et comité de lecture. nationale.National audienceNotre recherche a pour but l'étude du parallélisme intrinsèque des réseaux de neurones à inspiration biologique, qui sont des modèles à calculs totalement distribués. Nos objectifs sont doubles. Tout d'abord, nous souhaitons pouvoir utiliser cette propriété dans l'étude et le développement de nouveaux modèles neuromimétiques. Ensuite nous voulons exploiter ce parallélisme neuronal pour implanter nos modèles sur machines parallèles MIMD à mémoire partagée. l'un des problèmes liés à l'utilisation des réseaux de neurones est en effet le fort coût de ceux-ci en temps de calcul, et ce essentiellement en phase d'apprentissage. Il est donc intéressant de pouvoir utiliser la puissance de calcul des machines parallèles modernes pour accélérer l'exécution de nos réseaux, pour les configurer comme pour les exécuter. l'utilisation de la machine parallèle peut aussi nous permettre de construire des réseaux plus complexes et de travailler sur de plus vastes bases de données

Quelques considérations interactionnelles autour d'une expérience robotique

International audienceThe Psyphine group adresses the human/robot interactions and more particularly on the attribution or not of intentions, intelligence or even consciousness to a non-humanoid robotized object. This article presents an interactive analysis of DECIDE, the latest experiment of this group. This experiment relates humans to a lamp-shaped robotic object. The analysis presented shows the demystification of robotic objects and their integration into everyday life.Le groupe Psyphine s'interroge sur les interactions homme/robot et plus particulièrement sur l'attribution ou non d'intentions, d'intelligence voire de conscience à un objet robotisé non humanoïde. Cet article propose une analyse interactionnelle de DECIDE, la dernière expérience de ce groupe. Cette expérience met en relation des humains et un objet robotisé en forme de lampe. L'analyse présentée montre la démystification des objets robotiques et leur intégration dans le quotidien

Dynamic Self-Organising Map

International audienceWe present in this paper a variation of the self-organising map algorithm where the original time-dependent (learning rate and neighbourhood) learning function is replaced by a time-invariant one. This allows for on-line and continuous learning on both static and dynamic data distributions. One of the property of the newly proposed algorithm is that it does not fit the magnification law and the achieved vector density is not directly proportional to the density of the distribution as found in most vector quantisation algorithms. From a biological point of view, this algorithm sheds light on cortical plasticity seen as a dynamic and tight coupling between the environment and the model

Détermination de l'architecture de modèles neuromimétiques par implantation parallèle

Colloque avec actes sans comité de lecture. nationale.National audienceLa méthode présentée dans cet article permet de déterminer de manière systématique l'architecture idéale pour modéliser une fonction à l'aide de réseaux neuromimétiques. Partant d'une structure volontairement surdimensionnée, ce qui permet de modéliser des fonctions complexes, l'architecture peut ensuite être simplifiée en conservant, voire en améliorant, les capacités de modélisation. l'apprentissage se fait par itérations successives. La méthode nécessite un nombre important d'unités de calcul, de connexions et d'itérations, par conséquent son implantation parallèle est intéressante en vue de réduire le temps de calcul nécessaire à l'élaboration de l'architecture optimale

Some experiments around a neural network for multimodal associations

This paper presents a study of the model of triple BAM by E.Reynaud which is an improved variation of the original BAM model by Kosko. This class of model aims at integrating different sensory inputs in order to memorize a unified and distributed representation. An experimental evaluation of the model is presented that underlines its limitations in terms of noise robustness and learning capacities. A new model is presented in order to overcome those initial limitations by introducing a new online learning algorithm adapted from the PRLAB initial algorithm that improve both noise robustness and learning capacities. Finally, model properties and limitations are considered and discussed within the context of multi-modal integration and brain modeling

Interagir sans interpréter Apport d'une IA pour autonomiser un objet robotique

En raison de la situation sanitaire actuelle (Covid-19), WACAI est reporté du 02 au 04/06/2021International audienceNous proposons de présenter dans cet article l’utilisation d’une carte auto-organisatrice dans le processus de décision de l’action d’un objet robotisé muni d’un capteur, ici une caméra, interagissant avec un ou plusieurs humains. Cette carte utilise le capteur pour s’adapter à ses interlocuteur, ici les traits de leur visage, et ainsi ajuster ses mouvements au comportement des humains, sans toutefois les interpréte

Off-Policy Neural Fitted Actor-Critic

International audienceA new off-policy, offline, model-free, actor-critic reinforcement learning algorithm dealing with continuous environments in both states and actions is presented. It addresses discrete time problems where the goal is to maximize the discounted sum of rewards using stationary policies. Our algorithm allows to trade-off between data-efficiency and scalability. The amount of a priori knowledge is kept low by: (1) using neural networks to learn both the critic and the actor, (2) not relying on initial trajectories provided by an expert, and (3) not depending on known goal states. Experimental results compare data-efficiency to 4 state-of-the-art algorithms on three benchmark environments. This article largely reproduces a previous work [34] by adding a higher dimensional environment, improving control architectures and provides batch normalization for others state-of-the-art algorithms

SOMMA: Cortically Inspired Paradigms for Multimodal Processing

International audienceSOMMA (Self Organizing Maps for Multimodal Association) consists on cortically inspired paradigms for multi-modal data processing. SOMMA defines generic cortical maps - one for each modality - composed of 3-layers cortical columns. Each column learns a discrimination to a stimulus of the input flow with the BCMu learning rule [26]. These discriminations are self-organized in each map thanks to the coupling with neural fields used as a neighborhood function. Learning and computation in each map is influenced by other modalities thanks to bidirectional topographic connections between all maps. This multimodal influence drives a joint self-organization of maps and multimodal perceptions of stimuli. This work takes place after the design of a self-organizing map and of a modulation mechanism for influencing its self-organization oriented towards a multimodal purpose. In this paper, we introduce a way to connect these self-organizing maps to obtain a multimap multimodal processing, completing our previous work. We also give an overview of the architectural and functional properties of the resulting paradigm SOMMA

Self-organization of neural maps using a modulated BCM rule within a multimodal architecture

International audienceHuman beings interact with the environment through different modalities, i.e. perceptions and actions. Different perceptions as view, audition or proprioception for example, are picked up by different spatially separated sensors. They are processed in the cortex by dedicated brain areas, which are self-organized, so that spatially close neurons are sensitive to close stimuli. However, the processings of these perceptive flows are not isolated. On the contrary, they are constantly interacting, as illustrated by the McGurk effect. When the phonetic stimulus /ba/ is presented simultaneously with a lip movement corresponding to a /ga/, people perceive a /da/, which does not correspond to any of the stimuli. Merging several stimuli into one multimodal perception reduces the ambiguities and the noise of each perception. This is an essential mechanism of the cortex to interact with the environment. The aim of this article is to propose a model for the assembling of modalities, inspired by the biological properties of the cortex. We have modified the Bienenstock Cooper Munro (BCM) rule to include it in a model that consists of interacting maps of multilayer cortical columns. Each map is able to self-organize thanks to a continuous decentralized and local learning modulated by a high level signal. By assembling different maps corresponding to different modalities, our model creates a multimodal context which is used as a modulating signal and thus it influences the self-organization of each map

Toward a data efficient neural actor-critic

International audienceA new off-policy, offline, model-free, actor-critic reinforcement learning algorithm dealing with continuous environments in both states and actions is presented. It addresses discrete time problems where the goal is to maximize the discounted sum of rewards using stationary policies. Our algorithm allows to trade-off between data-efficiency and scalability. The amount of a priori knowledge is kept low by: (1) using neural networks to learn both the critic and the actor, (2) not relying on initial trajectories provided by an expert, and (3) not depending on known goal states. Experimental results show better data-efficiency than 4 state-of-the-art algorithms on two benchmark environments