Transmission of Distributed Deterministic Temporal Information through a Diverging/Converging Three-Layers Neural Network

Détails

ID Serval
serval:BIB_DA1AE8AC9AA7
Type
Actes de conférence (partie): contribution originale à la littérature scientifique, publiée à l'occasion de conférences scientifiques, dans un ouvrage de compte-rendu (proceedings), ou dans l'édition spéciale d'un journal reconnu (conference proceedings).
Collection
Publications
Titre
Transmission of Distributed Deterministic Temporal Information through a Diverging/Converging Three-Layers Neural Network
Titre de la conférence
Artificial Neural Networks – ICANN 2010
Auteur(s)
Asai Y., Villa A.E.P.
Editeur
Springer Berlin Heidelberg
Adresse
Bonn, Germany
ISBN
978-3-642-15818-6
978-3-642-15819-3
ISSN
0302-9743
1611-3349
Statut éditorial
Publié
Date de publication
2010
Peer-reviewed
Oui
Volume
6352
Pages
145-154
Langue
anglais
Résumé
This study investigates the ability of a diverging/converging neural network to transmit and integrate a complex temporally organized activity embedded in afferent spike trains. The temporal information is originally generated by a deterministic nonlinear dynamical system whose parameters determine a chaotic attractor. We present the simulations obtained with a network formed by simple spiking neurons (SSN) and a network formed by a multiple-timescale adaptive threshold neurons (MAT). The assessment of the temporal structure embedded in the spike trains is carried out by sorting the preferred firing sequences detected by the pattern grouping algorithm (PGA). The results suggest that adaptive threshold neurons are much more efficient in maintaining a specific temporal structure distributed across multiple spike trains throughout the layers of a feed-forward network.
Mots-clé
Spiking neural networks, synfire chains, adaptive threshold neurons, computational neuroscience, preferred firing sequences
Web of science
Création de la notice
04/08/2017 11:02
Dernière modification de la notice
17/11/2018 7:10
Données d'usage