Is it possible to discriminate odors with common words ?

Several experiments have been performed in order to study the cognitive processes which are involved in odor recognition. The current report summarizes experimental protocol and analyzes collected data. The goal is to try to recognize odors from descriptors which are selected by subjects from a list. Different groups have to choose in several descriptor lists, some with profound descriptors and some with a few surface descriptors. Profound descriptors are supposed to involved more cognition than surface descriptors. Subjects also have to name the odors. Recorded data are first analyzed, and then learned by an incremental neural classifier. The problem is hard to be learned. It seems very difficult to discriminate the different odors from the sets of descriptors. A variant of the learning algorithm, less sensitive to difficult examples, is proposed. The pertinence of surface descriptors is discussed.Des expériences ont été réalisées pour étudier les processus cognitifs impliqués dans la reconnaissance des odeurs. Ce rapport résume le protocole expérimental et étudie les données collectées. Le but est d'essayer de discriminer des odeurs à partir de descripteurs qui sont choisis par les sujets dans une liste. Plusieurs groupes travaillent avec différentes listes de descripteurs, ces descripteurs pouvant être de surface ou profonds. Les descripteurs profonds sont supposés être imliqués dans des traitememts plus cognitifs que les descripteurs de surface. Les sujets doivent également nommer les odeurs. Les données recueillies sont d'abord analysées, puis apprises par un classifieur neuronal incrémental. Le problème est difficile à apprendre. Il semble très délicat de discriminer les odeurs à partir des jeux de descripteurs. Une variante de l'algorithme d'apprentissage, moins sensible aux exemples difficiles, est proposée. La pertinence des descripteurs de surface est discutée

Simulation of Large Spiking Neural Networks on Distributed Architectures, The "DAMNED" Simulator

International audienceThis paper presents a spiking neural network simulator suitablefor biologically plausible large neural networks, named DAMNEDfor “Distributed And Multi-threaded Neural Event-Driven”. The simulatoris designed to run efficiently on a variety of hardware. DAMNEDmakes use of multi-threaded programming and non-blocking communicationsin order to optimize communications and computations overlap.This paper details the even-driven architecture of the simulator. Someoriginal contributions are presented, such as the handling of a distributedvirtual clock and an efficient circular event queue taking into accountspike propagation delays. DAMNED is evaluated on a cluster of computersfor networks from 103 to 105 neurons. Simulation and networkcreation speedups are presented. Finally, scalability is discussed regardingnumber of processors, network size and activity of the simulated NN

Multi-User Blood Alcohol Content Estimation in a Realistic Simulator using Artificial Neural Networks and Support Vector Machines

Abstract. We instrumented a realistic car simulator to extract low level data related to the driver’s use of the vehicle controls. After proceeding these data, we generated features that were fed to a Multi-Layer Perceptron (MLP) and Support Vector Machines (SVM). Our goal was determine if the driver’s Blood Alcohol Content (BAC) was over 0.4g.l−1 or not, and even estimate the BAC value. Our device process the vehicle’s controls data and then outputs the user BAC. We discuss the results of the prototype using the MLP and SVM algorithms in both single-user and multi-user context for detection of drunk drivers and estimation of the BAC value. The prototype performed better with single user base than with multi-user, and provided comparable results with MLP and SVM. This paper corrects a small error in our previous publication in ESANN’12 [3]