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PubMed · 8971202

Spike train patterning and forecastability.

A Longtin·D M Racicot
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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.

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A Longtin, D M Racicot. 1997. Spike train patterning and forecastability.. https://doi.org/10.1016/0303-2647(96)01636-x

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