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              <p>We propose a computational approach using a variational specification of the visual front-end, where ganglion cells with properties of retinal Konio cells (K-cells), are considered as a network, yielding a mesoscopic view of the retinal process. The variational framework is implemented as a simple mechanism of diffusion in a two-layered non-linear filtering mechanism with feedback, as observed in synaptic layers of the retina, while its biological plausibility, and capture functionalities as (i) stimulus adapted response; (ii) non-local noise reduction (i.e. segmentation); (iii) visual event detection, taking several visual cues into account: contrast and local texture, color or edge channels, and motion base in natural images. Those functionalities could be implemented in the biological tissues We use computer vision methods to propose an effective link between the observed functions and their possible implementation in the retinal network base on a two-layers network with non-separable local spatio-temporal convolution as input, and recurrent connections performing non-linear diffusion before prototype based visual event detection. The numerical robustness of the proposed model has been experimentally checked on real natural images. Finally, we discuss in base of experimental biological and computational results the generality of our description.</p>
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              <p>Nous proposons ici une approche fonctionnelle de la description des propriétés des cellules rétiniennes dites Konio, ceci au niveau du réseau, en utilisant une spécification variationnelle, ce qui donne une vue mésoscopique du processus de calcul de la rétine. Le cadre variationnel est implémenté comme un simple mécanisme de diffusion non-linéaire, mécanisme de filtrage avec rétroaction, suivi d'une couche d'unités ajustées à un élément statistique de la scène, comme on l'observe dans les couches synaptiques de la rétine pour ces cellules. On se propose de capturer les fonctionnalités suivantes: (i) adaptation de la réponse aux statistiques des stimuli naturels, (ii) réduction non-locale du bruit (en lien avec la segmentation de l'image), et (iii) détection d'événements visuels, en tenant compte de plusieurs indices visuels: contraste local, texture ou couleur, ces amers étant généralisables à des canaux de calcul de mouvement. Ces fonctionnalités peuvent être mises en œuvre dans les tissus biologiques, comme on le discute ici. Nous utilisons des méthodes de vision par ordinateur pour proposer une description fonctionnelle du calcul effectué au niveau de la rétine. La robustesse numérique du modèle proposé a été vérifié expérimentalement au niveau numérique sur de véritables images naturelles. Nous discutons, sur la base de résultats expérimentaux biologiques et informatiques, la généralité de notre description.</p>
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