Spike train patterning and forecastability

Bio Systems
André Longtin, D M Racicot

Abstract

Theories of neural coding rely on a knowledge of correlations between firing events. These correlations are also useful to validate biophysical models for the neural activity. We present a methodology for validating models based on the assessment of linear and non-linear correlations between variables derived from the spike train. The firing pattern of an electroreceptor is analyzed in this framework. We show that a purely stochastic model fails to capture the essential correlations between interspike intervals, even though it reproduces the interval histogram and certain spike train spectral features. However, a biophysical model, based on the Fitzhugh-Nagumo equations with noise, does exhibit many of the correlations seen in the data, including those between successive firing phases.

References

Jan 1, 1966·Annual Review of Physiology·G P MooreJ P Segundo
Aug 24, 1987·Physical Review Letters·J D Farmer, J J Sidorowich
Jun 13, 1994·Physical Review Letters·T Sauer
Mar 1, 1995·Chaos·Andre Longtin

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Citations

Jan 27, 2011·Experimental Brain Research·Oscar Avila-Akerberg, Maurice J Chacron
Jun 24, 2004·Bulletin of Mathematical Biology·Alberto Capurro, C P Malta
Sep 22, 2007·Neural Computation·Eilif MullerKarlheinz Meier
Jan 10, 2012·Chaos·J Leo van HemmenAndreas N Vollmayr
Jan 8, 2013·IEEE Transactions on Bio-medical Engineering·Ali Shahidi ZandiGuy A Dumont
Oct 16, 2016·Physical Review. E·José A ReinosoCristina Masoller
Mar 17, 2011·Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics·Zbyněk Pawlas, Petr Lansky
Dec 21, 2011·Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics·Eugenio Urdapilleta

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