2008 Special Issue: Deterministic neural dynamics transmitted through neural networks

Authors:
Yoshiyuki Asai;Apratim Guha;Alessandro E. P. Villa
Affiliations:
The Center for Advanced Medical Engineering and Informatics, Osaka University, Toyonaka Osaka, Japan and Neuroheuristic Research Group, Information Science Institute, University of Lausanne, CH-10 ...;Department of Statistics and Applied Probability, Faculty of Science, National University of Singapore, 6 Science Drive 2, Singapore;Université Joseph Fourier, Neuroheuristic Research Group, Grenoble Institute of Neuroscience, INSERM U 836-UJF-CEA-CHU, Grenoble, F-38043, France and Neuroheuristic Research Group, Informatio ...
Venue:
Neural Networks
Year:
2008

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Abstract

Precise spatiotemporal sequences of neuronal discharges (i.e., intervals between epochs repeating more often than expected by chance), have been observed in a large set of experimental electrophysiological recordings. Sensitivity to temporal information, by itself, does not demonstrate that dynamics embedded in spike trains can be transmitted through a neural network. This study analyzes how synaptic transmission through three archetypical types of neurons (regular-spiking, thalamo-cortical and resonator), simulated by a simple spiking model, can affect the transmission of precise timings generated by a nonlinear deterministic system (i.e., the Zaslavskii mapping in the present study). The results show that cells with subthreshold oscillations (resonators) are very sensitive to stochastic inputs, and are not a good candidate for transmitting temporally coded information. Thalamo-cortical neurons may transmit very well temporal patterns in the absence of background activity, but jitter accumulates along the synaptic chain. Conversely, we observed that cortical regular-spiking neurons can propagate filtered temporal information in a reliable way through the network, and with high temporal accuracy. We discuss the results in the general framework of neural dynamics and brain theories.