Detecting dependencies between spike trains of pairs of neurons through copulas

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Standard

Detecting dependencies between spike trains of pairs of neurons through copulas. / Sacerdote, Laura ; Tamborrino, Massimiliano; Zucca, Cristina.

I: Brain Research, Bind 1434, 12.09.2011, s. 243-256.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Sacerdote, L, Tamborrino, M & Zucca, C 2011, 'Detecting dependencies between spike trains of pairs of neurons through copulas', Brain Research, bind 1434, s. 243-256. https://doi.org/10.1016/j.brainres.2011.08.064

APA

Sacerdote, L., Tamborrino, M., & Zucca, C. (2011). Detecting dependencies between spike trains of pairs of neurons through copulas. Brain Research, 1434, 243-256. https://doi.org/10.1016/j.brainres.2011.08.064

Vancouver

Sacerdote L, Tamborrino M, Zucca C. Detecting dependencies between spike trains of pairs of neurons through copulas. Brain Research. 2011 sep. 12;1434:243-256. https://doi.org/10.1016/j.brainres.2011.08.064

Author

Sacerdote, Laura ; Tamborrino, Massimiliano ; Zucca, Cristina. / Detecting dependencies between spike trains of pairs of neurons through copulas. I: Brain Research. 2011 ; Bind 1434. s. 243-256.

Bibtex

@article{fb6a3e3d71a747a399e8224b26ab59c1,
title = "Detecting dependencies between spike trains of pairs of neurons through copulas",
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 In- tegrate and Fire models. The method discerns dependencies determined by the surround- ing network, from those determined by direct interactions between the two neurons. Furthermore, the method recognizes the presence of delays in the spike propagation.",
keywords = "Faculty of Science, Neural connectivity, Spike times , Leaky integrate and fire models, Diffusion processes, Copulas, Dependences",
author = "Laura Sacerdote and Massimiliano Tamborrino and Cristina Zucca",
year = "2011",
month = sep,
day = "12",
doi = "10.1016/j.brainres.2011.08.064",
language = "English",
volume = "1434",
pages = "243--256",
journal = "Brain Research",
issn = "0006-8993",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Detecting dependencies between spike trains of pairs of neurons through copulas

AU - Sacerdote, Laura

AU - Tamborrino, Massimiliano

AU - Zucca, Cristina

PY - 2011/9/12

Y1 - 2011/9/12

N2 - 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 In- tegrate and Fire models. The method discerns dependencies determined by the surround- ing network, from those determined by direct interactions between the two neurons. Furthermore, the method recognizes the presence of delays in the spike propagation.

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 In- tegrate and Fire models. The method discerns dependencies determined by the surround- ing network, from those determined by direct interactions between the two neurons. Furthermore, the method recognizes the presence of delays in the spike propagation.

KW - Faculty of Science

KW - Neural connectivity

KW - Spike times

KW - Leaky integrate and fire models

KW - Diffusion processes

KW - Copulas

KW - Dependences

U2 - 10.1016/j.brainres.2011.08.064

DO - 10.1016/j.brainres.2011.08.064

M3 - Journal article

C2 - 21981802

VL - 1434

SP - 243

EP - 256

JO - Brain Research

JF - Brain Research

SN - 0006-8993

ER -

ID: 40770129