Reconstruction of underlying nonlinear deterministic dynamics embedded in noisy spike trains

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Serval ID
serval:BIB_98EA02BDD849
Type
Article: article from journal or magazin.
Collection
Publications
Title
Reconstruction of underlying nonlinear deterministic dynamics embedded in noisy spike trains
Journal
Journal of Biological Physics
Author(s)
Asai  Y., Villa  A. E. P.
ISSN
0092-0606
Publication state
Published
Issued date
2008
Peer-reviewed
Oui
Volume
34
Number
3-4
Pages
325-340
Language
english
Notes
Asai2008325
Abstract
An experimentally recorded time series formed by the exact times of occurrence of the neuronal spikes (spike train) is likely to be affected by observational noise that provokes events mistakenly confused with neuronal discharges, as well as missed detection of genuine neuronal discharges. The points of the spike train may also suffer a slight jitter in time due to stochastic processes in synaptic transmission and to delays in the detecting devices. This study presents a procedure aimed at filtering the embedded noise (denoising the spike trains) the spike trains based on the hypothesis that recurrent temporal patterns of spikes are likely to represent the robust expression of a dynamic process associated with the information carried by the spike train. The rationale of this approach is tested on simulated spike trains generated by several nonlinear deterministic dynamical systems with embedded observational noise. The application of the pattern grouping algorithm (PGA) to the noisy time series allows us to extract a set of points that form the reconstructed time series. Three new indices are defined for assessment of the performance of the denoising procedure. The results show that this procedure may indeed retrieve the most relevant temporal features of the original dynamics. Moreover, we observe that additional spurious events affect the performance to a larger extent than the missing of original points. Thus, a strict criterion for the detection of spikes under experimental conditions, thus reducing the number of spurious spikes, may raise the possibility to apply PGA to detect endogenous deterministic dynamics in the spike train otherwise masked by the observational noise.
Keywords
Preferred firing sequence, Cell assemblies, Temporal pattern of spikes, Deterministic nonlinear dynamics, Denoising time series
Web of science
Open Access
Yes
Create date
23/08/2010 16:52
Last modification date
01/10/2019 7:18
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