title: Spike train patterning and forecastability.
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.
authors.affiliation: Département de Physique, Université d'Ottawa, Ontario, Canada.
authors.forename: A
authors.initials: A
authors.lastname: Longtin
authors.forename: D M
authors.initials: DM
authors.lastname: Racicot
mesh_terms: D000200:Action Potentials; D000818:Animals; D004555:Electric Fish; D008954:Models, Biological; D009474:Neurons / Q000502:physiology*