Effect of the background activity on the reconstruction of spike train by spike pattern detection

Details

Serval ID
serval:BIB_35AD15A11B17
Type
Article: article from journal or magazin.
Collection
Publications
Title
Effect of the background activity on the reconstruction of spike train by spike pattern detection
Journal
Lecture Notes in Computer Science
Author(s)
Asai  Y., Villa  A. E. P.
ISSN
0302-9743
Publication state
Published
Issued date
2008
Peer-reviewed
Oui
Volume
5164
Pages
607-616
Language
english
Notes
Asai2008607
Abstract
Deterministic nonlinearity 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 five archetypical types to input spike trains including deterministic information generated by a chaotic attractor. The comparison between input. and output spike trains is carried out by the pattern grouping algorithm (PGA) as a function of the intensity of the background activity for each neuronal type. The results show that the thalamo-cortical, regular spiking and intrinsically busting model neurons can be good candidate in transmitting temporal information with different characteristics in a spatially organized neural network.
Keywords
Attractor, Neurons, Model
Web of science
Create date
23/08/2010 15:52
Last modification date
20/08/2019 13:23
Usage data