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

Kernel discriminant analysis and clustering with parsimonious Gaussian process models

This work presents a family of parsimonious Gaussian process models which allow to build, from a finite sample, a model-based classifier in an infinite dimensional space. The proposed parsimonious models are obtained by constraining the eigen-decomposition of the Gaussian processes modeling each class. This allows in particular to use non-linear mapping functions which project the observations into infinite dimensional spaces. It is also demonstrated that the building of the classifier can be directly done from the observation space through a kernel function. The proposed classification method is thus able to classify data of various types such as categorical data, functional data or networks. Furthermore, it is possible to classify mixed data by combining different kernels. The methodology is as well extended to the unsupervised classification case. Experimental results on various data sets demonstrate the effectiveness of the proposed method

Robust supervised classification with mixture models: Learning from data with uncertain labels

International audienceIn the supervised classification framework, human supervision is required for labeling a set of learning data which are then used for building the classifier. However, in many applications, human supervision is either imprecise, difficult or expensive. In this paper, the problem of learning a supervised multi-class classifier from data with uncertain labels is considered and a model-based classification method is proposed to solve it. The idea of the proposed method is to confront an unsupervised modelling of the data with the supervised information carried by the labels of the learning data in order to detect inconsistencies. The method is able afterward to build a robust classifier taking into account the detected inconsistencies into the labels. Experiments on artificial and real data are provided to highlight the main features of the proposed method as well as an application to object recognition under weak supervision

Une nouvelle méthode de classification en grande dimension pour la reconnaissance de formes

Nous proposons une nouvelle modélisation gaussienne adaptée aux données de grande dimension pour la discrimination et la classification automatique. Notre modélisation est basée sur l'hypothèse que les données de grande dimension vivent dans des sous-espaces dont la dimension intrinsèque est inférieure à la dimension de l'espace. Pour ce faire, notre approche recherche les sous-espaces spécifiques dans lesquels vivent chacune des classes. De plus, nous régularisons les matrices de covariance des classes en supposant que les classes sont sphériques à la fois dans leur espace propre et son supplémentaire. Nous utilisons ensuite ce nouveau modèle en analyse discriminante et en classification automatique dans le cadre de la reconnaissance d'objets dans des images naturelles

Current Research: Discovery and Recovery of a 14th Century Dugout Canoe on the Red River, Caddo Parish, Louisiana

In June 2017, Jenna Bradley and Robert Cornett were boating down the Red River in northern Caddo Parish, Louisiana, when they noticed an unusual log protruding from a sandy bank near the town of Belcher. After realizing that it was a dugout canoe, they contacted the Louisiana Department of Wildlife and Fisheries, and eventually word of the find was transmitted to state archaeologist Chip McGimsey at the Louisiana Division of Archaeology. The following day, Bradley and Cornett led Jeffrey Girard and Jameel Damlouji of the Louisiana Archaeological Society to the site. It was obvious that it was a dugout canoe of comparable size and form to one found in 1983 at the base of a steep cutbank on the east side of a now cut off channel of the Red River approximately 12 km (7 miles) downstream. At the time, the 1983 canoe was thought to be the largest prehistoric watercraft in the Southeastern United States measuring 9.35 m (or 30 ft. 8 inches) long and 56 cm (1 ft. 10 inches) in diameter. The newly discovered canoe is a little larger, measuring 10.2 m long (33.4 ft.) and approximately 60 cm (2.0 ft.) in diameter. Both boats have similar shapes with step-like seats carved into the ends, and both probably are made from cypress logs, although the wood of the recent find has not been identified with certainty

On Norman Wilde’s “The Meaning of Rights”

International audienc

La Sagesse de la Multitude

In lieu of an abstract, here is a brief excerpt of the content: "L’objection la plus ancienne et la plus redoutable à la démocratie fait valoir que le gouvernement par le peuple dessert le gouvernement pour le peuple. Les citoyens manquant pour la plupart de sagesse ou de compétence, le bien commun serait mieux assuré en confiant le pouvoir à un individu éclairé ou à une élite experte. Une réponse commune à cette objection concède la prémisse mais affirme la priorité au gouvernement par le peuple sur le gouvernement pour le peuple : le droit égal à la participation devrait l’emporter sur la promotion de la compétence, même si celle-ci est requise par le bon gouvernement. La démocratie se trouve alors réduite à un ensemble de procédures équitables, traitant les citoyens en égaux ; elle ne se définit plus par la poursuite du bien commun. Il est toutefois une autre réponse à l’objection, qui évite cette dérive vers un procéduralisme étroit. Elle consiste à nier la prémisse et à affirmer la sagesse politique du peuple. Il n’est pas vrai que le gouvernement pour le peuple serait mieux assuré en confiant le pouvoir à un petit nombre de sages ou d’experts, fussent-ils les meilleurs parmi les citoyens. Cette thèse remarquable peut paraître improbable. Sa défense peut pourtant s’appuyer sur l’un des arguments les plus intrigants élaborés par la philosophie politique aristotélicienne, qui inspire et éclaire les controverses philosophiques contemporaines sur la valeur du régime démocratique : l’argument de la sagesse de la multitude.

De la presse en démocratie

Les nouveaux médias représentent-ils une menace ou un progrès pour la presse en régime démocratique ? À partir d’une analyse du rôle politique de la presse, qui contribue au droit de chacun à gouverner, Charles Girard s’interroge sur le renouvellement du métier de journaliste et sur les modes de délibération démocratique