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Journal Article

Recurrence-Based Synchronization Analysis of Weakly Coupled Bursting Neurons under External ELF Fields

Authors

Nkomidio,  Aissatou Mboussi
External Organizations;

/persons/resource/eulalie

Ngamga,  Eulalie Joelle
Potsdam Institute for Climate Impact Research;

Nbendjo,  Blaise Romeo Nana
External Organizations;

/persons/resource/Juergen.Kurths

Kurths,  Jürgen
Potsdam Institute for Climate Impact Research;

/persons/resource/Marwan

Marwan,  Norbert
Potsdam Institute for Climate Impact Research;

External Ressource
Fulltext (public)

entropy-24-00235-v2.pdf
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Citation

Nkomidio, A. M., Ngamga, E. J., Nbendjo, B. R. N., Kurths, J., Marwan, N. (2022): Recurrence-Based Synchronization Analysis of Weakly Coupled Bursting Neurons under External ELF Fields. - Entropy, 24, 2, 235.
https://doi.org/10.3390/e24020235


Cite as: https://publications.pik-potsdam.de/pubman/item/item_26807
Abstract
We investigate the response characteristics of a two-dimensional neuron model exposed to an externally applied extremely low frequency (ELF) sinusoidal electric field and the synchronization of neurons weakly coupled with gap junction. We find, by numerical simulations, that neurons can exhibit different spiking patterns, which are well observed in the structure of the recurrence plot (RP). We further study the synchronization between weakly coupled neurons in chaotic regimes under the influence of a weak ELF electric field. In general, detecting the phases of chaotic spiky signals is not easy by using standard methods. Recurrence analysis provides a reliable tool for defining phases even for noncoherent regimes or spiky signals. Recurrence-based synchronization analysis reveals that, even in the range of weak coupling, phase synchronization of the coupled neurons occurs and, by adding an ELF electric field, this synchronization increases depending on the amplitude of the externally applied ELF electric field. We further suggest a novel measure for RP-based phase synchronization analysis, which better takes into account the probabilities of recurrences.