A Computational Model of Spatial Memory Anticipation during Visual Search

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

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

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

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 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

Mécanisme connexionniste pour l'anticipation visuelle

On présente un modèle d'anticipation visuelle utilisant un modèle de neurone Sigma-Pi. Ce modèle permet de prédire les modifications de la perception visuelle à la suitede saccades oculaires, avant qu'elles ne soient réalisées ; ce mécanisme a été mis en évidence par des données biologiques. On souhaite appliquer ce mécanisme pour construire des représentations motrices centrées surl'oeil et sur la tête des objets visuellement saillants, mises à jour par la perception visuelle et le mouvement oculaire.On peut ainsi déterminer la consigne motrice permettant de recentrer un stimulus visuel sorti du champ visuel à la suite d'un mouvement des yeux

Algorithmic adjustment of neural field parameters

Revisiting neural-field calculation maps in the discrete case, we propose algorithmic mechanisms allowing to choose a right set of parameters in order to both (i) guaranty the stability of the calculation and (ii) tune the shape of the output map. These results do not ``prove'' the existence of stable bump solutions, this being already known and extensively verified numerically, but allow to calculate algorithmically the related parameters. The results apply to scalar and vectorial neural-fields thus allowing to bypass the inherent limitations brought by mean frequency models and also take the laminar structure of the cortex or high-level representation of cortical computations into account. We obtain an easy to implement procedure that guaranty the convergence of the map onto a fixed point, even with large sampling steps. Furthermore, we report how rectification is the minimal required non-linearity to obtain usual neural-field behaviors. We also propose a way to control and tune these behaviors (filtering, selection, tracking, remanence) and optimize the convergence rate. This applies to both non parametric profiles, i.e. adjusting the weight values directly, or to parametric profiles and thus adjusting their parameters (e.g. Mexican-hat profiles). Beyond these algorithmic results, the idea of studying neural computations as discrete dynamical systems and not only the discretization of a continuous system is emphasized here. The outcome is shared as an open-source plug-in module, called EnaS (http://enas.gforge.inria.fr), to be used in existing simulation software