Effect of the Background Activity on the Reconstruction of Spike Train by Spike Pattern Detection

Authors:
Yoshiyuki Asai;Alessandro E. Villa
Affiliations:
The Center for Advanced Medical Engineering and Informatics, Osaka University, Toyonaka, Japan and NeuroHeuristic Research Group, Information Science Institute, University of Lausanne, Switzerland;NeuroHeuristic Research Group, Information Science Institute, University of Lausanne, Switzerland and Institut des Neurosciences, Université Joseph Fourier, NeuroHeuristic Research Group, Gre ...
Venue:
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Year:
2008

Citing 2
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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.