Detecting dependencies between spike trains of pairs of neurons through copulas

Laura  Sacerdote; Massimiliano Tamborrino; Cristina Zucca

doi:10.1016/j.brainres.2011.08.064

Detecting dependencies between spike trains of pairs of neurons through copulas

Laura Sacerdote, Massimiliano Tamborrino, Cristina Zucca

Department of Mathematical Sciences

16 Citations (Scopus)

1625 Downloads (Pure)

Abstract

The dynamics of a neuron are influenced by the connections with the network where it lies. Recorded spike trains exhibit patterns due to the interactions between neurons. However, the structure of the network is not known. A challenging task is to investigate it from the analysis of simultaneously recorded spike trains. We develop a non-parametric method based on copulas, that we apply to simulated data according to different bivariate Leaky Integrate and Fire models. The method discerns dependencies determined by the surrounding network, from those determined by direct interactions between the two neurons. Furthermore, the method recognizes the presence of delays in the spike propagation. This article is part of a Special Issue entitled "Neural Coding".

Original language	English
Journal	Brain Research
Volume	1434
Pages (from-to)	243-256
ISSN	0006-8993
DOIs	https://doi.org/10.1016/j.brainres.2011.08.064
Publication status	Published - 24 Jan 2012

Keywords

Faculty of Science
Neural connectivity
Spike times
Leaky integrate and fire models
Diffusion processes
Copulas
Dependences

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AB - The dynamics of a neuron are influenced by the connections with the network where it lies. Recorded spike trains exhibit patterns due to the interactions between neurons. However, the structure of the network is not known. A challenging task is to investigate it from the analysis of simultaneously recorded spike trains. We develop a non-parametric method based on copulas, that we apply to simulated data according to different bivariate Leaky Integrate and Fire models. The method discerns dependencies determined by the surrounding network, from those determined by direct interactions between the two neurons. Furthermore, the method recognizes the presence of delays in the spike propagation. This article is part of a Special Issue entitled "Neural Coding".

KW - Faculty of Science

KW - Neural connectivity

KW - Spike times

KW - Leaky integrate and fire models

KW - Diffusion processes

KW - Copulas

KW - Dependences

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SP - 243

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JO - Brain Research

JF - Brain Research

ER -

Detecting dependencies between spike trains of pairs of neurons through copulas

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Keywords

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