Chimera states in a network of identical oscillators with symmetric coexisting attractors

Chen, Mingshu; Veeman, Dhinakaran; Wang, Zhen; Karthikeyan, Anitha

doi:10.1140/epjs/s11734-022-00480-2

Cited by 6 publications

(3 citation statements)

References 44 publications

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“…Networks of neurons, however, are typically not isolated but interact with other networks. Therefore, recent work dealt with interactions of chimera states across coupled layers in multilayer networks (e.g., Majhi et al, 2016;Maksimenko et al, 2016;Andrzejak et al, 2017;Andrzejak et al, 2018;Sawicki et al, 2019;Ruzzene et al, 2020;Vadivasova et al, 2020;Rontogiannis and Provata, 2021;Chen et al, 2022). Moreover, various neuron models were used instead of the simple phase oscillators as network nodes (e.g., Sakaguchi, 2006;Hizanidis et al, 2014;Hizanidis et al, 2016;Santos et al, 2017;Chouzouris et al, 2018;Calim et al, 2020;Gerster et al, 2020;Provata and Venetis, 2020;Glaze and Bahar, 2021;Rontogiannis and Provata, 2021;Simo et al, 2021;Zhang and Dai, 2022).…”

Section: Chimera Statesmentioning

confidence: 99%

What Models and Tools can Contribute to a Better Understanding of Brain Activity?

Goodfellow

Andrzejak

Masoller

et al. 2022

Front. Netw. Physiol.

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Despite impressive scientific advances in understanding the structure and function of the human brain, big challenges remain. A deep understanding of healthy and aberrant brain activity at a wide range of temporal and spatial scales is needed. Here we discuss, from an interdisciplinary network perspective, the advancements in physical and mathematical modeling as well as in data analysis techniques that, in our opinion, have potential to further advance our understanding of brain structure and function.

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Section: Chimera Statesmentioning

confidence: 99%

What Models and Tools can Contribute to a Better Understanding of Brain Activity?

Goodfellow

Andrzejak

Masoller

et al. 2022

Front. Netw. Physiol.

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“…The study of chimera states in multilayer networks showed from early on that couplings between network layers can induce or destabilize chimera states in individual layers [7][8][9][10][11][12][13]19,[21][22][23][24][25][26][27][28][29][30][31][32] .…”

Section: Introductionmentioning

confidence: 99%

“…Apart from ring networks [6][7][8][10][11][12][13][14][15][16][17][18][19][22][23][24][25][26][27][28][29] , two-dimensional lattices [30][31][32] with a multiplex inter-layer coupling were analyzed. Further studies addressed the role of coupling delays 12,[22][23][24][25] , inter-layer couplings with temporal fluctuations 10,27 , of limited duration 26 , and with nonlinear inertia 9 , as well as intra-layer coupling strengths adapting to the phase difference of interacting oscillators 21,33 .…”

Section: Introductionmentioning

confidence: 99%

Chimera states in multiplex networks: Chameleon-like across-layer synchronization

Andrzejak

Espinoso

2023

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Different across-layer synchronization types of chimera states in multilayer networks have been discovered recently. We investigate possible relations between them, for example, if the onset of some synchronization type implies the onset of some other type. For this purpose, we use a two-layer network with multiplex inter-layer coupling. Each layer consists of a ring of non-locally coupled phase oscillators. While oscillators in each layer are identical, the layers are made non-identical by introducing mismatches in the oscillators’ mean frequencies and phase lag parameters of the intra-layer coupling. We use different metrics to quantify the degree of various across-layer synchronization types. These include phase-locking between individual interacting oscillators, amplitude and phase synchronization between the order parameters of each layer, generalized synchronization between the driver and response layer, and the alignment of the incoherent oscillator groups’ position on the two rings. For positive phase lag parameter mismatches, we get a cascaded onset of synchronization upon a gradual increase of the inter-layer coupling strength. For example, the two order parameters show phase synchronization before any of the interacting oscillator pairs does. For negative mismatches, most synchronization types have their onset in a narrow range of the coupling strength. Weaker couplings can destabilize chimera states in the response layer toward an almost fully coherent or fully incoherent motion. Finally, in the absence of a phase lag mismatch, sufficient coupling turns the response dynamics into a replica of the driver dynamics with the phases of all oscillators shifted by a constant lag.

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Investigating the route to synchronization in real-world neuronal networks of autaptic photosensitive neurons

Dang,

Bayani,

Tian

et al. 2024

Chaos, Solitons & Fractals

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Chimera states in a network of identical oscillators with symmetric coexisting attractors

Cited by 6 publications

References 44 publications

What Models and Tools can Contribute to a Better Understanding of Brain Activity?

What Models and Tools can Contribute to a Better Understanding of Brain Activity?

Chimera states in multiplex networks: Chameleon-like across-layer synchronization

Investigating the route to synchronization in real-world neuronal networks of autaptic photosensitive neurons

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