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.

Detecting dependencies between spike trains of pairs ofneurons through copulas

SACERDOTE, Laura Lea;ZUCCA, CRISTINA
2012

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.
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Neural connectivity; Spike times; Leaky integrate and fire models; Diffusion processes; Copulas; Dependencies.
Laura Sacerdote; Massimiliano Tamborrino; Cristina Zucca
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/90046
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