Journal of Biological Physics

, Volume 34, Issue 3–4, pp 325–340 | Cite as

Reconstruction of Underlying Nonlinear Deterministic Dynamics Embedded in Noisy Spike Trains

  • Yoshiyuki Asai
  • Alessandro E. P. Villa
Original Paper


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.


Preferred firing sequence Cell assemblies Temporal pattern of spikes Deterministic nonlinear dynamics Denoising time series 



This study was partially funded by the binational JSPS/INSERM grant SYRNAN (2007–2008) and the Japan–France Research Cooperative Program.


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Copyright information

© Springer Science + Business Media B.V. 2008

Authors and Affiliations

  1. 1.The Center for Advanced Medical Engineering and InformaticsOsaka University, JapanToyonaka, OsakaJapan
  2. 2.Grenoble Institut des Neurosciences, CR Inserm U 836-UJF-CEA-CHUUniversité Joseph FourierGrenoble Cedex 9France
  3. 3.Neuroheuristic Research Group, Information Science InstituteHEC-University of LausanneLausanneSwitzerland

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