https://doi.org/10.1140/epjs/s11734-025-01831-5
Regular Article
Coherence-resonance patterning in neural map networks under Gaussian and Lévy noise
1
Institute of Physics, Saratov State University, 83 Astrakhanskaya st., 410012, Saratov, Saratov region, Russia
2
School of Digital Sciences, Digital University Kerala, 695317, Pallipuram, Kerala, India
a
rybalovaev@gmail.com
b
work-a.ryabov@yandex.ru
c
sishushankarmuni@gmail.com
d
strelkovagi@sgu.ru
Received:
12
April
2025
Accepted:
22
July
2025
Published online:
1
August
2025
We explore numerically the spatiotemporal dynamics of networks of nonlocally coupled Rulkov maps (discrete-time neuron models) under Gaussian and Lévy noise. We show how the network dynamics depends on the control parameters of individual elements, the coupling parameters, and noise generators. We reveal for the first time and analyze the possibility of observing coherence-resonance solitary states, coherence-resonance solitary state chimeras, and coherence-resonance phase chimeras. We show that changes in the coupling radius can influence the number of incoherence clusters of solitary state chimeras. Conversely, when the coupling strength is varied, there are transitions between coherence-resonance solitary state chimeras and solitary states. Furthermore, we demonstrate that coherence-resonance solitary state chimeras can exist within a specific range of the scale parameter of Lévy noise, which decreases as the stability index of the noise source is reduced.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2025
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
