Effects of structural modifications on cluster synchronization patterns

Li, Qiang; Peron, Thomas; Stankovski, Tomislav; Ji, Peng

doi:10.1007/s11071-022-07383-w

Cited by 4 publications

(1 citation statement)

References 35 publications

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“…Synchronization is an important behavior that individuals coordinate with their neighbors 6 . Many kinds of meaningful synchronization behaviors of complex networks have been studied, including asymptotic synchronization, 7 local synchronization, 8 quasi‐synchronization, 9 quantum synchronization, 10 cluster synchronization, 11 phase synchronization, 12 partial synchronization, 13 complete synchronization, 14 output synchronization, 15 outer synchronization, 16 projective synchronization, 17 map synchronization, 18 periodic synchronization, 19 and adaptive synchronization 20 . These synchronization criteria can be suitable for different processes.…”

Section: Introductionmentioning

confidence: 99%

Adaptive control for synchronization of time‐delayed complex networks with multi‐weights based on semi‐linear hyperbolic PDEs

Yang,

Qiu

2024

Adaptive Control & Signal

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SummaryThis paper studies the adaptive synchronization of complex spatio‐temporal networks modeled by semi‐linear hyperbolic partial differential equations (CSTNSLHPDEs) as well as considering time‐invariant and time‐varying delays in a one‐dimensional space. Firstly, a distributed adaptive controller is proposed, where different nodes are with different adaptive gains. Secondly, four cases, CSTNSLHPDEs with time‐invariant delays and one single weight, with time‐invariant delays and multi‐weights, with time‐varying delays and one single weight, and with time‐varying delays and multi‐weights, are successively analyzed, and synchronization conditions of these four cases are obtained by using the proposed distributed adaptive controller. In the end, examples illustrate the effectiveness of the proposed distributed adaptive controller.

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Section: Introductionmentioning

confidence: 99%

Adaptive control for synchronization of time‐delayed complex networks with multi‐weights based on semi‐linear hyperbolic PDEs

Yang,

Qiu

2024

Adaptive Control & Signal

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Switching clusters’ synchronization for discrete space-time complex dynamical networks via boundary feedback controls

Zhang

2023

Pattern Recognition

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Delta-alpha cross-frequency coupling for different brain regions

Lukarski,

Petkoski,

et al. 2023

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Neural interactions occur on different levels and scales. It is of particular importance to understand how they are distributed among different neuroanatomical and physiological relevant brain regions. We investigated neural cross-frequency couplings between different brain regions according to the Desikan–Killiany brain parcellation. The adaptive dynamic Bayesian inference method was applied to EEG measurements of healthy resting subjects in order to reconstruct the coupling functions. It was found that even after averaging over all subjects, the mean coupling function showed a characteristic waveform, confirming the direct influence of the delta-phase on the alpha-phase dynamics in certain brain regions and that the shape of the coupling function changes for different regions. While the averaged coupling function within a region was of similar form, the region-averaged coupling function was averaged out, which implies that there is a common dependence within separate regions across the subjects. It was also found that for certain regions the influence of delta on alpha oscillations is more pronounced and that oscillations that influence other are more evenly distributed across brain regions than the influenced oscillations. When presenting the information on brain lobes, it was shown that the influence of delta emanating from the brain as a whole is greatest on the alpha oscillations of the cingulate frontal lobe, and at the same time the influence of delta from the cingulate parietal brain lobe is greatest on the alpha oscillations of the whole brain.

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Effects of structural modifications on cluster synchronization patterns

Cited by 4 publications

References 35 publications

Adaptive control for synchronization of time‐delayed complex networks with multi‐weights based on semi‐linear hyperbolic PDEs

Adaptive control for synchronization of time‐delayed complex networks with multi‐weights based on semi‐linear hyperbolic PDEs

Switching clusters’ synchronization for discrete space-time complex dynamical networks via boundary feedback controls

Delta-alpha cross-frequency coupling for different brain regions

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