Paper Info
Title
Effect of the Background Activity on the Reconstruction of Spike Train by Spike Pattern Detection
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.
Year
DOI
Venue
2008
10.1007/978-3-540-87559-8_63
ICANN (2)
Keywords
Field
DocType
spike pattern detection,input spike train,complex temporally organized activity,background activity,different characteristic,regular spiking,model neuron,deterministic information,spike train,different type,spiking neuron model,spiking neuron,neural network,spatial organization
Attractor,Biological neuron model,Spike train,Pattern recognition,Random neural network,Computer science,Input/output,Artificial intelligence,Spiking neural network,Artificial neural network,Machine learning,Electrophysiology
Conference
Volume
ISSN
Citations 
5164
0302-9743
0
PageRank 
References 
Authors
0.34
6
2
Name
Order
Citations
PageRank
Yoshiyuki Asai1307.56
Alessandro E. Villa210.75