2020
DOI: 10.1063/5.0006218
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Flocking transition within the framework of Kuramoto paradigm for synchronization: Clustering and the role of the range of interaction

Abstract: A Kuramoto-type approach to address flocking phenomena is presented. First, we analyze a simple generalization of the Kuramoto model for interacting active particles, which is able to show the flocking transition (the emergence of coordinated movements in a group of interacting self-propelled agents). In the case of all-to-all interaction, the proposed model reduces to the Kuramoto model for phase synchronization of identical motionless noisy oscillators. In general, the nature of this non-equilibrium phase tr… Show more

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Cited by 18 publications
(8 citation statements)
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“…Then, we quantified the temporal averages of global‐level synchronization of n oscillating signals by the mean of the order parameter: <r()t>goodbreak=1Lt=1Lr()t where L = 205 is the total time points of the BOLD signal and r ( t ) is the Kuramoto order parameter (Escaff & Delpiano, 2020), which is the canonical model for studying synchronization phenomena: r()tgoodbreak=1n||j=1neiθj()t and was used as a time‐dependent measure of phase synchrony. The metastability was then determined as the SD of r ( t ) (Alderson et al, 2018; Deco & Kringelbach, 2016; Wildie & Shanahan, 2012).…”
Section: Methodsmentioning
confidence: 99%
“…Then, we quantified the temporal averages of global‐level synchronization of n oscillating signals by the mean of the order parameter: <r()t>goodbreak=1Lt=1Lr()t where L = 205 is the total time points of the BOLD signal and r ( t ) is the Kuramoto order parameter (Escaff & Delpiano, 2020), which is the canonical model for studying synchronization phenomena: r()tgoodbreak=1n||j=1neiθj()t and was used as a time‐dependent measure of phase synchrony. The metastability was then determined as the SD of r ( t ) (Alderson et al, 2018; Deco & Kringelbach, 2016; Wildie & Shanahan, 2012).…”
Section: Methodsmentioning
confidence: 99%
“…The first and last 10 time points were removed to minimize border effects inherent to the transform. Then, the mean phase synchrony of each time point r(t) was measured using the Kuramoto order parameter [28]:…”
Section: Calculating the Synchronization Of The Somatomotor Networkmentioning
confidence: 99%
“…Variants of the Kuramoto model have been suggested from the aspect of time delay [5], inertia effect [6,7], presence of noise [8], and so forth, which is basically a reflection of reality or a generalization of the Kuramoto model. Coupled oscillator models have provided frameworks in the theoretical and practical study of swarming [9][10][11][12][13][14] or flocking [15,16] behavior of natural and artificial systems.…”
Section: Introductionmentioning
confidence: 99%