Implementing Relational-Algebraic Operators for Improving Cognitive Abilities in Networks of Neural Cliques

International audienceAssociative memories are devices capable of retrieving previously stored messages from parts of their content. They are used in a variety of applications including CPU caches, routers, intrusion detection systems, etc. They are also considered a good model for human memory, motivating the use of neural-based techniques. When it comes to cognition, it is important to provide such devices with the ability to perform complex requests, such as union, intersection, difference, projection and selection. In this paper, we extend a recently introduced associative memory model to perform relational algebra operations. We introduce new algorithms and discuss their performance which provides an insight on how the brain performs some high-level information processing tasks

A model of bottom-up visual attention using cortical magnification

International audienceThe focus of visual attention has been argued to play a key role in object recognition. Many computational models of visual attention were proposed to estimate locations of eye fixations driven by bottom-up stimuli. Most of these models rely on pyramids consisting of multiple scaled versions of the visual scene. This design aims at capturing the fact that neural cells in higher visual areas tend to have larger receptive fields (RFs). On the other hand, very few models represent multi-scaling resulting from the eccentricity-dependent RF sizes within each visual layer, also known as the cortical magnification effect. In this paper, we demonstrate that using a cortical-magnification-like mechanism can lead to performant alternatives to pyramidal approaches in the context of attentional modeling. Moreover, we argue that introducing such a mechanism equips the proposed model with additional properties related to overt attention and distance-dependent saliency that are worth exploring

Architectures neuro-inspirées pour l'acquisition et le traitement de l'information visuelle

Computer vision and machine learning are two hot research topics that have witnessed major breakthroughs in recent years. Much of the advances in these domains have been the fruits of many years of research on the visual cortex and brain function. In this thesis, we focus on designing neuro-inspired architectures for processing information along three different stages of the visual cortex. At the lowest stage, we propose a neural model for the acquisition of visual signals. This model is adapted to emulating eye movements and is closely inspired by the function and the architecture of the retina and early layers of the ventral stream. On the highest stage, we address the memory problem. We focus on an existing neuro-inspired associative memory model called the Sparse Clustered Network. We propose a new information retrieval algorithm that offers more flexibility and a better performance over existing ones. Furthermore, we suggest a generic formulation within which all existing retrieval algorithms can fit. It can also be used to guide the design of new retrieval approaches in a modular fashion. On the intermediate stage, we propose a new way for dealing with the image feature correspondence problem using a neural network model. This model deploys the structure of Sparse Clustered Networks, and offers a gain in matching performance over state-of-the-art, and provides a useful insight on how neuro-inspired architectures can serve as a substrate for implementing various vision tasks.L'apprentissage automatique et la vision par ordinateur sont deux sujets de recherche d'actualité. Des contributions clés à ces domaines ont été les fruits de longues années d'études du cortex visuel et de la fonction des réseaux cérébraux. Dans cette thèse, nous nous intéressons à la conception des architectures neuro-inspirées pour le traitement de l'information sur trois niveaux différents du cortex visuel. Au niveau le plus bas, nous proposons un réseau de neurones pour l'acquisition des signaux visuels. Ce modèle est étroitement inspiré par le fonctionnement et l'architecture de la retine et les premières couches du cortex visuel chez l'humain. Il est également adapté à l'émulation des mouvements oculaires qui jouent un rôle important dans notre vision. Au niveau le plus haut, nous nous intéressons à la mémoire. Nous traitons un modèle de mémoire associative basée sur une architecture neuro-inspirée dite `Sparse Clustered Network (SCN)'. Notre contribution principale à ce niveau est de proposer une amélioration d'un algorithme utilisé pour la récupération des messages partiellement effacés du SCN. Nous suggérons également une formulation générique pour faciliter l'évaluation des algorithmes de récupération, et pour aider au développement des nouveaux algorithmes. Au niveau intermédiaire, nous étendons l'architecture du SCN pour l'adapter au problème de la mise en correspondance des caractéristiques d'images, un problème fondamental en vision par ordinateur. Nous démontrons que la performance de notre réseau atteint l'état de l'art, et offre de nombreuses perspectives sur la façon dont les architectures neuro-inspirées peuvent servir de substrat pour la mise en oeuvre de diverses tâches de vision

A study of retrieval algorithms of sparse messages in networks of neural cliques

International audienceAssociative memories are data structures addressed using part of the content rather than an index. They offer good fault reliability and biological plausibility. Among different families of associative memories, sparse ones are known to offer the best efficiency (ratio of the amount of bits stored to that of bits used by the network itself). Their retrieval process performance has been shown to benefit from the use of iterations. We introduce several families of algorithms to enhance the performance of the retrieval process inrecently proposed sparse associative memories based on binary neural networks. We show that these algorithms provide better performance than existing techniques and discuss their biological plausibility. We also analyze the required number of iterations and derive corresponding curves

A study of retrieval algorithms of sparse messages in networks of neural cliques

International audienceAssociative memories are data structures addressed using part of the content rather than an index. They offer good fault reliability and biological plausibility. Among different families of associative memories, sparse ones are known to offer the best efficiency (ratio of the amount of bits stored to that of bits used by the network itself). Their retrieval process performance has been shown to benefit from the use of iterations. We introduce several families of algorithms to enhance the performance of the retrieval process inrecently proposed sparse associative memories based on binary neural networks. We show that these algorithms provide better performance than existing techniques and discuss their biological plausibility. We also analyze the required number of iterations and derive corresponding curves

A Turbo-Inspired Iterative Approach for Correspondence Problems of Image Features

International audienceEstablishing correspondences between image fea- tures is a fundamental problem in many computer vision tasks. It is traditionally viewed as a graph matching problem, and solved using an optimization procedure. In this paper, we propose a new approach to solving the correspondence problem from a coding/decoding perspective. We then present an iterative matching algorithm inspired from the turbo-decoding concept. We provide an experimental evaluation of the proposed method, and show that it performs better than state-of-the-art algorithms in the presence of clutter, thanks to turbo-style decoding

A Biologically Inspired Framework for Visual Information Processing and an Application on Modeling Bottom-Up Visual Attention

International audienceAn emerging trend in visual information processing is toward incorporating some interesting properties of the ventral stream in order to account for some limitations of machine learning algorithms. Selective attention and cortical magnification are two such important phenomena that have been the subject of a large body of research in recent years. In this paper, we focus on designing a new model for visual acquisition that takes these important properties into account