Integration and transmission of distributed deterministic neural activity in feed-forward networks

Détails

ID Serval
serval:BIB_0027554B88C4
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Integration and transmission of distributed deterministic neural activity in feed-forward networks
Périodique
Brain Research
Auteur(s)
Asai Y., Villa A.E.P.
ISSN
0006-8993
Statut éditorial
Publié
Date de publication
01/2012
Peer-reviewed
Oui
Volume
1434
Pages
17-33
Langue
anglais
Résumé
A ten layer feed-forward network characterized by diverging/converging patterns of projection between successive layers of regular spiking (RS) neurons is activated by an external spatiotemporal input pattern fed to Layer 1 in presence of stochastic background activities fed to all layers. We used three dynamical systems to derive the external input spike trains including the temporal information, and three types of neuron models for the network, i.e. either a network formed either by neurons modeled by exponential integrate-and-fire dynamics (RS-EIF, Fourcaud-Trocmé et al., 2003), or by simple spiking neurons (RS-IZH, Izhikevich, 2004) or by multiple-timescale adaptive threshold neurons (RS-MAT, Kobayashi et al., 2009), given five intensities for the background activity. The assessment of the temporal structure embedded in the output spike trains was carried out by detecting the preferred firing sequences for the reconstruction of de-noised spike trains (Asai and Villa, 2008). We confirmed that the RS-MAT model is likely to be more efficient in integrating and transmitting the temporal structure embedded in the external input. We observed that this structure could be propagated not only up to the 10th layer but in some cases it was retained better beyond the 4th downstream layers. This study suggests that diverging/converging network structures, by the propagation of synfire activity, could play a key role in the transmission of complex temporal patterns of discharges associated to deterministic nonlinear activity.
Mots-clé
Developmental Biology, General Neuroscience, Molecular Biology, Clinical Neurology
Pubmed
Web of science
Création de la notice
04/08/2017 10:09
Dernière modification de la notice
20/08/2019 13:22
Données d'usage