Detecting dependencies between spike trains of pairs of neurons through copulas

Laura Sacerdote, Massimiliano Tamborrino, Cristina Zucca

16 Citationer (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".

OriginalsprogEngelsk
TidsskriftBrain Research
Vol/bind1434
Sider (fra-til)243-256
ISSN0006-8993
DOI
StatusUdgivet - 24 jan. 2012

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  • Det Natur- og Biovidenskabelige Fakultet

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