Paper Info
Title
Deterministic neural dynamics transmitted through neural networks.
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.
Year
DOI
Venue
2008
10.1016/j.neunet.2008.06.014
Neural Networks
Keywords
Field
DocType
Precise firing sequences,Neural dynamics,Mutual information,Spike train analyses
Biological system,Computer science,Stochastic process,Temporal database,Mutual information,Deterministic system,Artificial intelligence,Jitter,Artificial neural network,Spiking neural network,Deterministic system (philosophy),Machine learning
Journal
Volume
Issue
ISSN
21
6
0893-6080
Citations 
PageRank 
References 
4
0.47
10
Authors
3
Name
Order
Citations
PageRank
Yoshiyuki Asai1307.56
Apratim Guha2633.45
Alessandro E P Villa382.17