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Statistical Coupling Between time Point-Processes

Abstract : The observation of physical phenomena often goes through the recording of discrete time series of events, that can be represented with marked point processes. The robust estimation of the correlation between two point processes can, therefore, unveil physical mechanisms underlying the observed phenomena. However, the robust estimation of correlation between two, or more, point-processes is hindered by the signal noise (leading to false and missing point detections), the important density of points, and possible time-shift between coupled points. We propose a statistical framework that uses hypothesis testing to estimate coupling between time pointprocesses. Using simulations, we show that our framework accurately estimates the coupling between two time point-processes even for noisy signal (with false point detections), for high density of points and in the presence of a time shift between coupled points. By applying our statistical framework to the recordings of neuron population activity in mouse visual cortex, we measure the functional coupling between individual neurons, and cluster neurons into functional ensembles.
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https://hal-pasteur.archives-ouvertes.fr/pasteur-03699883
Contributor : Thibault Lagache Connect in order to contact the contributor
Submitted on : Monday, June 20, 2022 - 3:50:42 PM
Last modification on : Friday, June 24, 2022 - 3:52:23 AM

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Distributed under a Creative Commons Attribution - NonCommercial 4.0 International License

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Samuel Kubler, Jean-Christophe Olivo-Marin, Thibault Lagache. Statistical Coupling Between time Point-Processes. 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), Mar 2022, Kolkata, France. pp.1-4, ⟨10.1109/ISBI52829.2022.9761557⟩. ⟨pasteur-03699883⟩

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