Titre
Deterministic nonlinear spike train filtered by spiking neuron model
Type
article
Institution
Externe
Périodique
Lecture Notes in Computer Science
Auteur(s)
Asai, Y.
Auteure/Auteur
Yokoi, T.
Auteure/Auteur
Villa, A. E. P.
Auteure/Auteur
Liens vers les personnes
ISSN
0302-9743
Statut éditorial
Publié
Date de publication
2007
Volume
4668
Première page
924
Dernière page/numéro d’article
933
Peer-reviewed
Oui
Langue
anglais
Notes
Asai2007924
Résumé
Deterministic nonlinear dynamics has been observed in experimental electrophysiological recordings performed in several areas of the brain. However, little is known about the ability to transmit a complex temporally organized activity through different types of spiking neurons. This study investigates the response of a spiking neuron model representing three archetypical types (regular spiking, thalamocortical and resonator) to input spike trains composed of deterministic (chaotic) and stochastic processes with weak background activity. The comparison of the input and output spike trains allows to assess the transmission of information contained in the deterministic nonlinear dynamics. The pattern grouping algorithm (PGA) was applied to the output of the neuron to detect the dynamical attractor embedded in the original input spike train. The results show that the model of the thalamo-cortical neuron can be a better candidate than regular spiking and resonator type neurons in transmitting temporal information in a spatially organized neural network.
Sujets
PID Serval
serval:BIB_0DB677984560
Date de création
2010-08-23T14:52:03.973Z
Date de création dans IRIS
2025-05-20T14:23:34Z