A computational approach to the covert and overt deployment of spatial attention

Popular computational models of visual attention tend to neglect the influence of saccadic eye movements whereas it has been shown that the primates perform on average three of them per seconds and that the neural substrate for the deployment of attention and the execution of an eye movement might considerably overlap. Here we propose a computational model in which the deployment of attention with or without a subsequent eye movement emerges from local, distributed and numerical computations

Optimisation de contrôleurs par essaim particulaire

http://cap2012.loria.fr/pub/Papers/10.pdfNational audienceTrouver des contrôleurs optimaux pour des systèmes stochastiques est un problème particulièrement difficile abordé dans les communautés d'apprentissage par renforcement et de contrôle optimal. Le paradigme classique employé pour résoudre ces problèmes est celui des processus décisionnel de Markov. Néanmoins, le problème d'optimisation qui en découle peut être difficile à résoudre. Dans ce papier, nous explorons l'utilisation de l'optimisation par essaim particulaire pour apprendre des contrôleurs optimaux. Nous l'appliquons en particulier à trois problèmes classiques : le pendule inversé, le mountain car et le double pendule

YARBUS : Yet Another Rule Based belief Update System Jérémy Fix Hervé Frezza-Buet

We introduce a new rule based system for belief tracking in dialog systems. Despite the simplicity of the rules being considered, the proposed belief tracker ranks favourably compared to the previous submissions on the second and third Dialog State Tracking challenges. The results of this simple tracker allows to reconsider the performances of previous submissions using more elaborate techniques

A computational approach to the control of voluntary saccadic eye movements.

Chapitre 85 ; pp 491-495 ; ISBN: 978-1-4020-8386-0International audienceWe present a computational model of how the several areas involved in the control of voluntary saccadic eye movements might cooperate. This model is based on anatomical considerations and lays the emphasis on the temporal evolution of the activities in each of these areas, and their potential functional role in the control of saccades

Principes de transformations sensorimotrices à l'aide de neurones Sigma-Pi

Le modèle que nous proposons se place dans le cadre du paradigme des champs neuronaux discrets à deux dimensions qui repose sur une équation différentielle régissant la dynamique des neurones. Les informations visuelles et proprioceptives sont representées sous forme de paquets d'activités possédant un profil gaussien. Une représentation centrée tête est alors obtenue par un calcul distribué du produit de convolution des entrées

A dynamic neural field approach to the covert and overt deployment of spatial attention

International audienceAbstract The visual exploration of a scene involves the in- terplay of several competing processes (for example to se- lect the next saccade or to keep fixation) and the integration of bottom-up (e.g. contrast) and top-down information (the target of a visual search task). Identifying the neural mech- anisms involved in these processes and in the integration of these information remains a challenging question. Visual attention refers to all these processes, both when the eyes remain fixed (covert attention) and when they are moving (overt attention). Popular computational models of visual attention consider that the visual information remains fixed when attention is deployed while the primates are executing around three saccadic eye movements per second, changing abruptly this information. We present in this paper a model relying on neural fields, a paradigm for distributed, asyn- chronous and numerical computations and show that covert and overt attention can emerge from such a substratum. We identify and propose a possible interaction of four elemen- tary mechanisms for selecting the next locus of attention, memorizing the previously attended locations, anticipating the consequences of eye movements and integrating bottom- up and top-down information in order to perform a visual search task with saccadic eye movements

DANA: Distributed (asynchronous) Numerical and Adaptive modelling framework

International audienceDANA is a python framework (http://dana.loria.fr) whose computational paradigm is grounded on the notion of a unit that is essentially a set of time dependent values varying under the influence of other units via adaptive weighted connections. The evolution of a unit's value are defined by a set of differential equations expressed in standard mathematical notation which greatly ease their definition. The units are organized into groups that form a model. Each unit can be connected to any other unit (including itself) using a weighted connection. The DANA framework offers a set of core objects needed to design and run such models. The modeler only has to define the equations of a unit as well as the equations governing the training of the connections. The simulation is completely transparent to the modeler and is handled by DANA. This allows DANA to be used for a wide range of numerical and distributed models as long as they fit the proposed framework (e.g. cellular automata, reaction-diffusion system, decentralized neural networks, recurrent neural networks, kernel-based image processing, etc.)

A distributed computational model of spatial memory anticipation during a visual search task

Some visual search tasks require the memorization of the location of stimuli that have been previously focused. Considerations about the eye movements raise the question of how we are able to maintain a coherent memory, despite the frequent drastic changes in the perception. In this article, we present a computational model that is able to anticipate the consequences of eye movements on visual perception in order to update a spatial working memory

A Top-down attentional system scanning multiple targets with saccades

International audienceWe would like to introduce recent developments in the computational cognitive neuroscience domain, applied to computer vision. Our objective in this paper is to propose a biologicially inspired algorithm to solve a computer vision problem that is to focus (by the mean of eye movements or camera movements) on several targets that share given properties. This algorithm relies on the paradigm of distributed, asynchronous and numerical computations that we think could lead to efficient algorithms in the long run

Non-Associative Learning and the Iterant Deformable Sensorimotor Medium

What drives the behavior of an organism ? That fundamental question has been addressed in the literature for a variety of organisms and through different angles. For some species, part of the answer lies in the associative learning mechanisms but there also exists more widespread non-associative learning mechanisms. Habituation and Sensitization, the first translating behaviorally in a response decrease while the second in a response increase, are two such mechanisms that are so widely observed in the phylogeny that it suggests their fundamental role in behavioral learning. In this context, we propose new computational mechanisms of learning inspired by habituation and sensitization as described by biologists. These models are seen as extensions to the Iterant Deformable Sensorimotor Medium (IDSM), a conceptual model of habits formation from an artificial agent perspective as self-reinforcing sensorimotor behavioral patterns. It is an intermediate model between neuronal models and macroscopic behavior models.Qu’est-ce qui motive le comportement d’un organisme ? Cette question fondamentale a été abordée dans la littérature pour une variété d’organismes et sous différents angles. Pour certaines espèces, une partie de la réponse réside dans les mécanismes d’apprentissage associatif, mais il existe également des mécanismes d’apprentissage non associatif plus répandus. Habituation et Sensibilisation, le premier se traduisant comportementalement par une diminution de la réponse et le second par une augmentation de la réponse, sont deux de ces mécanismes qui sont si largement observés dans la phylogénie que cela suggère leur rôle fondamental dans l’apprentissage comportemental. Dans ce contexte, nous proposons de nouveaux mécanismes computationnels d’apprentissage inspirés de l’habituation et de la sensibilisation telles que décrits par les biologistes. Ces modèles sont considérés comme des extensions du “Iterant Deformable Sensorimotor Medium” (IDSM), un modèle conceptuel de la formation d’habitudes pour un agent artificiel en tant que schémas comportementaux sensorimoteurs auto-renforcés. Il s’agit d’un modèle intermédiaire entre les modèles neuronaux et les modèles de comportement macroscopiques