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Estimation of the instantaneous spike train variability

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  • Rajdl, Kamil
  • Kostal, Lubomir

Abstract

The variability of neuronal spike trains is usually measured by the Fano factor or the coefficient of variation of interspike intervals, but their estimation is problematic, especially with limited amount of data. In this paper we show that it is in fact possible to estimate a quantity equivalent to the Fano factor and the squared coefficient of variation based on the intervals from only one specific (random) time. This leads to two very simple but precise Fano factor estimators, that can be interpreted as estimators of instantaneous variability. We derive their properties, evaluate their accuracy in various situations and show that they are often more accurate than the standard estimators. The presented estimators are particularly suitable for the case where variability changes rapidly.

Suggested Citation

  • Rajdl, Kamil & Kostal, Lubomir, 2023. "Estimation of the instantaneous spike train variability," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    • Handle: RePEc:eee:chsofr:v:177:y:2023:i:c:s0960077923011827
    DOI: 10.1016/j.chaos.2023.114280
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    References listed on IDEAS

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    1. Contreras-Uribe, T.J. & Garay-Jiménez, L.I. & Guzmán-Vargas, L., 2017. "A point process analysis of electrogastric variability," Chaos, Solitons & Fractals, Elsevier, vol. 94(C), pages 16-22.
    2. Yuan, Ying & Zhuang, Xin-tian & Liu, Zhi-ying & Huang, Wei-qiang, 2012. "Time-clustering behavior of sharp fluctuation sequences in Chinese stock markets," Chaos, Solitons & Fractals, Elsevier, vol. 45(6), pages 838-845.
    3. Dylan Festa & Amir Aschner & Aida Davila & Adam Kohn & Ruben Coen-Cagli, 2021. "Neuronal variability reflects probabilistic inference tuned to natural image statistics," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    4. Telesca, Luciano & Lapenna, Vincenzo & Scalcione, Emanuele & Summa, Donato, 2007. "Searching for time-scaling features in rainfall sequences," Chaos, Solitons & Fractals, Elsevier, vol. 32(1), pages 35-41.
    5. D’Onofrio, Giuseppe & Lansky, Petr & Tamborrino, Massimiliano, 2019. "Inhibition enhances the coherence in the Jacobi neuronal model," Chaos, Solitons & Fractals, Elsevier, vol. 128(C), pages 108-113.
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