Inferring collective synchrony observing spiking of one or several neurons

Fuente: arXiv
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Autores principales: Pikovsky, Arkady, Rosenblum, Michael
Formato: Preprint
Publicado: 2025
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author Pikovsky, Arkady
Rosenblum, Michael
author_facet Pikovsky, Arkady
Rosenblum, Michael
contents We tackle a quantification of synchrony in a large ensemble of interacting neurons from the observation of spiking events. In a simulation study, we efficiently infer the synchrony level in a neuronal population from a point process reflecting spiking of a small number of units and even from a single neuron. We introduce a synchrony measure (order parameter) based on the Bartlett covariance density; this quantity can be easily computed from the recorded point process. This measure is robust concerning missed spikes and, if computed from observing several neurons, does not require spike sorting. We illustrate the approach by modeling populations of spiking or bursting neurons, including the case of sparse synchrony.
format Preprint
id arxiv_https___arxiv_org_abs_2501_07696
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Inferring collective synchrony observing spiking of one or several neurons
Pikovsky, Arkady
Rosenblum, Michael
Neurons and Cognition
We tackle a quantification of synchrony in a large ensemble of interacting neurons from the observation of spiking events. In a simulation study, we efficiently infer the synchrony level in a neuronal population from a point process reflecting spiking of a small number of units and even from a single neuron. We introduce a synchrony measure (order parameter) based on the Bartlett covariance density; this quantity can be easily computed from the recorded point process. This measure is robust concerning missed spikes and, if computed from observing several neurons, does not require spike sorting. We illustrate the approach by modeling populations of spiking or bursting neurons, including the case of sparse synchrony.
title Inferring collective synchrony observing spiking of one or several neurons
topic Neurons and Cognition
url https://arxiv.org/abs/2501.07696