Sparse neural networks with large learning diversity

Coded recurrent neural networks with three levels of sparsity are introduced. The first level is related to the size of messages, much smaller than the number of available neurons. The second one is provided by a particular coding rule, acting as a local constraint in the neural activity. The third one is a characteristic of the low final connection density of the network after the learning phase. Though the proposed network is very simple since it is based on binary neurons and binary connections, it is able to learn a large number of messages and recall them, even in presence of strong erasures. The performance of the network is assessed as a classifier and as an associative memory

Turbo codes and turbo algorithms

In the first part of this paper, several basic ideas that prompted the coming of turbo codes are commented on. We then present some personal points of view on the main advances obtained in past years on turbo coding and decoding such as the circular trellis termination of recursive systematic convolutional codes and double-binary turbo codes associated with Max-Log-MAP decoding. A novel evaluation method, called genieinitialised iterative processing (GIIP), is introduced to assess the error performance of iterative processing. We show that using GIIP produces a result that can be viewed as a lower bound of the maximum likelihood iterative decoding and detection performance. Finally, two wireless communication systems are presented to illustrate recent applications of the turbo principle, the first one being multiple-input/multiple-output channel iterative detection and the second one multi-carrier modulation with linear precoding

Minimum information required for written word recognition

International audienceReading is an automated process. One of the remarkable human abilities is that we can read even partially erased or hidden words. We carried out a study on written word recognition in order to decipher how much information is required at least to identify a word. Experimental software was designed in C++ language to measure the amount of information in pixels and reaction time. The results showed we could identify words at a very low display rate and suggest that prior knowledge on the category of words play a mediating role in written word recognition

Dynamic reorganization of functional brain networks during picture naming.

International audienceFor efficient information processing during cognitive activity, functional brain networks have to rapidly and dynamically reorganize on a sub-second time scale. Tracking the spatiotemporal dynamics of large scale networks over this short time duration is a very challenging issue. Here, we tackle this problem by using dense electroencephalography (EEG) recorded during a picture naming task. We found that (i) the picture naming task can be divided into six brain network states (BNSs) characterized by significantly high synchronization of gamma (30–45 Hz) oscillations, (ii) fast transitions occur between these BNSs that last from 30 msec to 160 msec, (iii) based on the state of the art of the picture naming task, we consider that the spatial location of their nodes and edges, as well as the timing of transitions, indicate that each network can be associated with one or several specific function (from visual processing to articulation) and (iv) the comparison with previously-used approach aimed at localizing the sources showed that the network-based approach reveals networks that are more specific to the performed task. We speculate that the persistence of several brain regions in successive BNSs participates to fast and efficient information processing in the brain

Organisation de la mémoire dans un turbo décodeur utilisant l'algorithme SUB-MAP

L'implémentation de l'algorithme MAP (ou BCJR, APP,...) requiert une mémoire de sauvegarde des probabilités qui peut être de taille importante. Des procédés de turbo décodage utilisant peu de mémoire, tout en gardant de bons pouvoirs de correction, ont été définis. Ces derniers ont été validés par simulation (C, VHDL). Les structures suggérées offrent au concepteur un panorama de solutions techniques pour implémenter un turbo décodeur à partir de l'algorithme SUB-MAP

Spatiotemporal Analysis of Brain Functional Connectivity

International audienceBrain functions are based on interactions between neural assemblies distributed within and across distinct cerebral regions. During cognitive tasks, these interactions are dynamic and take place at the millisecond time scale. In this context, the excellent temporal resolution (<1 ms) of the Electroencephalographic -EEG- signals allows for detection of very short-duration events and therefore, offers the unique opportunity to follow, over time, the dynamic properties of cognitive processes. In this paper we propose a new algorithm to track the functional brain connectivity dynamics. During picture recognition and naming task, this algorithm aims at segmenting high resolution (hr) EEG functional connectivity microstates. The proposed algorithm is based on the K-means clustering of the connectivity graphs obtained from the Phase Locking Values (PLV). Results show that the algorithm is able to track the brain functional connectivity dynamics during picture naming task

Connectivité de sources en EEG-hr et dynamique des réseaux cérébraux fonctionnels

National audienceLe traitement l'information par le cerveau est un processus dynamique qui met en jeu une réorganisation rapide des réseaux cérébraux fonctionnels, sur une échelle de temps très courte (> nombre d'électrodes), (ii) estimer les dépendances statistiques (connectivité fonctionnelle) entre les sources reconstruites , (iii) caractériser les réseaux identifiés (sous forme des noeuds connectés par des liens formant un graphe) par des analyses basées sur la théorie des graphes et (vi) segmenter, dans le temps, le processus cognitif sous la forme d'une séquence d'états de connectivité fonctionnelle (fcSs : 'functional connectivity states'). Les résultats montrent qu'un traitement approprié du signal EEG permet d'identifier une dynamique spatio-temporelle dans les réseaux fonctionnels mis en jeu durant la tâche avec une excellente résolution temporelle (de l'ordre de la ms) et spatiale (~ 1000 régions d'intérêt). Cette dynamique correspond à une séquence de six fcSs (durée : 30 ms à 160 ms) caractérisés par une corrélation de phase significative des oscillations gamma (30-45 Hz). Des transitions rapides entre ces fcS sont observées et les réseaux associés à chaque fcS se recouvrent partiellement. Ces réseaux s'instancient sur des régions cérébrales pertinentes par rapport à la tâche de dénomination d'objets, depuis la perception de l'image jusqu'à l'articulation du nom. La méthode proposée ouvre de nombreuses perspectives quant à l'identification, à partir des données d'EEG de scalp, de réseaux cérébraux mis en jeu transitoirement lors d'activités cognitives. Abstract-The information processing in the human brain is a dynamic process that involves a rapid reorganization of functional brain networks, in a very short time scale (> number of electrodes), (ii) estimating the statistical dependencies (functional connectivity) between reconstructed sources (iii) characterizing the identified networks (in the form of nodes connected by edges forming a graph) by graph theory based analysis and (vi) segmenting, in time, the cognitive process as a sequence of functional connectivity states (fcSs). The results show that appropriate processing of the EEG signals can reveal the spatiotemporal dynamics of functional brain networks involved in the task with excellent temporal (on the order of ms) and spatial (~ 1000 regions of interest) resolution. This corresponds to a dynamic sequence of six fcSs (duration: 30 ms to 160 ms) with significant gamma phase synchronization (30-45 Hz). Rapid transitions between these fcS are observed and the networks associated with each fcS partially overlap. These networks disclose relevant brain regions related to picture naming task, from the perception of the image until the naming. The proposed method offers many opportunities in the identification, from the EEG data, of brain networks involved in cognitive activities