Saccade learning with concurrent cortical and subcortical basal ganglia loops

The Basal Ganglia is a central structure involved in multiple cortical and subcortical loops. Some of these loops are believed to be responsible for saccade target selection. We study here how the very specific structural relationships of these saccadic loops can affect the ability of learning spatial and feature-based tasks. We propose a model of saccade generation with reinforcement learning capabilities based on our previous basal ganglia and superior colliculus models. It is structured around the interactions of two parallel cortico-basal loops and one tecto-basal loop. The two cortical loops separately deal with spatial and non-spatial information to select targets in a concurrent way. The subcortical loop is used to make the final target selection leading to the production of the saccade. These different loops may work in concert or disturb each other regarding reward maximization. Interactions between these loops and their learning capabilities are tested on different saccade tasks. The results show the ability of this model to correctly learn basic target selection based on different criteria (spatial or not). Moreover the model reproduces and explains training dependent express saccades toward targets based on a spatial criterion. Finally, the model predicts that in absence of prefrontal control, the spatial loop should dominate

The seven donkeys: Super A.I. performance in animal categorization by an immature Human brain

This paper reports image categorization performance exhibited by an immature Human brain, that beats current state-of-the art convolutional networks with regards to the training procedure (limited size of the training set and limited training budget). This observation highlights the limits of the current A.I. trend for backpropagation-trained neural networks dedicated to computer vision, as well as its differences with natural neural networks. Based on the identified limitations, I then introduces a new image categorization challenge (the seven donkey challenge)

Integration of navigation and action selection functionalities in a computational model of cortico-basal ganglia-thalamo-cortical loops

This article describes a biomimetic control architecture affording an animat both action selection and navigation functionalities. It satisfies the survival constraint of an artificial metabolism and supports several complementary navigation strategies. It builds upon an action selection model based on the basal ganglia of the vertebrate brain, using two interconnected cortico-basal ganglia-thalamo-cortical loops: a ventral one concerned with appetitive actions and a dorsal one dedicated to consummatory actions. The performances of the resulting model are evaluated in simulation. The experiments assess the prolonged survival permitted by the use of high level navigation strategies and the complementarity of navigation strategies in dynamic environments. The correctness of the behavioral choices in situations of antagonistic or synergetic internal states are also tested. Finally, the modelling choices are discussed with regard to their biomimetic plausibility, while the experimental results are estimated in terms of animat adaptivity

Avenir des catalogues collectifs nationaux (L\u27)

Ce rapport vise à analyser le mode de fonctionnement des deux grands catalogues collectifs français, le Système universitaire de documentation (SUDOC) et le Catalogue collectif de France (CCFR), de mesurer leur audience et leurs usages et de suggérer toutes recommandations qui permettraient d’en faire, encore davantage demain qu’aujourd’hui, des outils transparents et visibles, y compris sur un plan international. Leur performance doit servir les attentes des usagers anonymes comme celles de la communauté scientifique et des professionnels du livre

Sequential Action Selection for Budgeted Localization in Robots

International audienceRecent years have seen a fast growth in the number of applications of Machine Learning algorithms from Computer Science to Robotics. Nevertheless, while most such attempts were successful in maximizing robot performance after a long learning phase, to our knowledge none of them explicitly takes into account the budget in the algorithm evaluation: e.g. budget limitation on the learning duration or on the maximum number of possible actions by the robot. In this paper we introduce an algorithm for robot spatial localization based on image classification using a sequential budgeted learning framework. This aims to allow the learning of policies under an explicit budget. In this case our model uses a constraint on the number of actions that can be used by the robot. We apply this algorithm to a localization problem on a simulated environment. Our approach enables to reduce the problem to a classification task under budget constraint. The model has been compared, on the one hand, to simple neural networks for the classification part and, on the other hand, to different techniques of policy selection. The results show that the model can effectively learn an efficient policy (i.e. alternating between sensor measurement and movement to get additional information in different positions) in order to optimize its localization performance under each tested fixed budget