Éditeur(s) : HAL CCSD
Résumé : 6 pages
In a Spiking Neural Networks (SNN), spike emissions are sparsely and irregularly distributed both in time and in the network architecture. Since a current feature of SNNs is a low average activity, efficient implementations of SNNs are usually based on an Event-Driven Simulation (EDS). On the other hand, simulations of large scale neural networks can take advantage of distributing the neurons on a set of processors (either workstation cluster or parallel computer). This article presents DAMNED, a large scale SNN simulation framework able to gather the benefits of EDS and parallel computing. Two levels of parallelism are combined: Distributed mapping of the neural topology, at the network level, and local multithreaded allocation of resources for simultaneous processing of events, at the neuron level. Based on the causality of events, a distributed solution is proposed for solving the complex problem of scheduling without synchronization barrier.
https://hal.archives-ouvertes.fr/hal-00015137

hal-00015137
https://hal.archives-ouvertes.fr/hal-00015137
https://hal.archives-ouvertes.fr/hal-00015137v2/document
https://hal.archives-ouvertes.fr/hal-00015137/file/hal_damned.pdf
ARXIV : cs.NE/0512018

Éditeur(s) : HAL CCSD
Résumé : International audience
Proceedings of the European Symposium on Artificial Neural Networks
Bruges, Belgium

halshs-00410041
https://halshs.archives-ouvertes.fr/halshs-00410041

Éditeur(s) : HAL CCSD
Résumé : International audience
“Affective Computing is computing that relates to, arises from, or deliberately influences emotions” (R.W. Picard, MIT Press, 1997). Specifically, psychosocial equilibrium is now considered as a major issue for both individual and public health. Predicting human emotions via non-intrusive methods is a great challenge triggered by the rise of intelligent assisting systems. In order to determine the emotional state of a subject having a potential psychosocial disorder, it is required to process both neutral and expressive emotions. A computer-engineered method aiming to provide emotional information from videos is proposed. An original approach for affect classification and its interpretation is presented.The database is part of the HUMAINE database, and consists of a large volume of video clips, each displaying individuals, depressed or not, talking with a conversational agent. Both the high-dimensionality of data and its temporal aspect require a pre-training that consists of cleaning, sorting and feature selection, in order to make relevant information usable for further processing by MachineLearning methods. Next step consists in classifying the affective state of subjects according to their positivity or negativity (Russell's model, 1980). By means of six independent classifiers, each running a Support Vector Machine algorithm, separate performances are obtained for each class of emotions. The results reveal an interesting “neutral area phenomenon”, where emotions tend to be harder to be detected.Though more frequent, neutral emotions seem to carry less discriminant information than expressive ones. This issue will be a key for designing a tuning step in further research.
Caribbean Academy Of Science
Deshaies, France

hal-01408029
https://hal.univ-antilles.fr/hal-01408029

Éditeur(s) : HAL CCSD
Résumé : Document soutenu à l'atelier Fouille de Données Complexes de la conférence EGC2017
International audience
"L'informatique affective est l'informatique qui se réfère à, résulte de, ou influence délibérément les émotions" (Picard (1997)). Singulièrement, l'équi-libre psychosocial est désormais considéré comme une préoccupation majeure pour la santé, tant sur le plan individuel que public. Prédire les émotions hu-maines via des méthodes non-invasives est un challenge propulsé notamment par le développement des systèmes d'accompagnement intelligents. Afin de dé-terminer l'état émotionel d'un individu, ayant potentiellement un trouble psy-chosocial, il convient de traiter tant les émotions expressives que neutres. Une approche originale de classification de l'information émotionelle à partir de vi-déos est proposée.
Extraction et Gestion des Connaissances
Grenoble, France

Droits : http://creativecommons.org/licenses/by-nc-sa/
hal-01513329
https://hal.archives-ouvertes.fr/hal-01513329
https://hal.archives-ouvertes.fr/hal-01513329/document
https://hal.archives-ouvertes.fr/hal-01513329/file/egcArticle%20scholet.pdf

Éditeur(s) : HAL CCSD D-side publications, Evere, Belgium
Résumé : International audience
This article underlines the learning and discrimination capabilities of a model of associative memory based on artificial networks of spiking neurons. Inspired from neuropsychology and neurobiology, the model implements top-down modulations, as in neocortical layer V pyramidal neurons, with a learning rule based on synaptic plasticity (STDP), for performing a multimodal association learning task. A temporal correlation method of analysis proves the ability of the model to associate specific activity patterns to different samples of stimulation. Even in the absence of initial learning and with continuously varying weights, the activity patterns become stable enough for discrimination.
Proceedings of 14 European Symposium on Artificial Neural Networks (ESANN 2006)

hal-00115420
https://hal.archives-ouvertes.fr/hal-00115420
https://hal.archives-ouvertes.fr/hal-00115420/document
https://hal.archives-ouvertes.fr/hal-00115420/file/esann06_HAL.pdf
ARXIV : cs.NE/0611104