Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.neunet.2008.06.014
Title: Deterministic neural dynamics transmitted through neural networks
Authors: Asai, Y.
Guha, A. 
Villa, A.E.P.
Keywords: Mutual information
Neural dynamics
Precise firing sequences
Spike train analyses
Issue Date: Aug-2008
Citation: Asai, Y., Guha, A., Villa, A.E.P. (2008-08). Deterministic neural dynamics transmitted through neural networks. Neural Networks 21 (6) : 799-809. ScholarBank@NUS Repository. https://doi.org/10.1016/j.neunet.2008.06.014
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. © 2008 Elsevier Ltd. All rights reserved.
Source Title: Neural Networks
URI: http://scholarbank.nus.edu.sg/handle/10635/105092
ISSN: 08936080
DOI: 10.1016/j.neunet.2008.06.014
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