Deterministic nonlinear spike train filtered by spiking neuron model

Authors:
Yoshiyuki Asai;Takashi Yokoi;Alessandro E. P. Villa
Affiliations:
National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan and Information Science Institute, University of Lausanne, Switzerland;National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan;Information Science Institute, University of Lausanne, Switzerland and Université Joseph Fourier, NeuroHeuristic Research Group, Institut des Neurosciences, Grenoble, France and INSERM, Greno ...
Venue:
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Year:
2007

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Abstract

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.