Robust partial synchronization of delay-coupled networks

Su, Libo; Wei, Yanling; Michiels, Wim; Steur, Erik; Nijmeijer, Henk

doi:10.1063/1.5111745

Cited by 6 publications

(4 citation statements)

References 23 publications

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“…The 3 nephron pairs are separated from each other by arterial segments, and the synchronization metrics and phase angle differences between different nephron pairs are similar to those generated by the unpaired nephrons. This set of results is consistent with a pattern of partial synchronization (Pikovsky et al, 2001;Dahms et al, 2012;Pecora et al, 2014;Steur et al, 2016;Su et al, 2019).…”

Section: Resultssupporting

confidence: 88%

“…Thus, one major result of the study is the finding that not all nephrons behave identically, despite the fact that they all interact, a condition referred to as partial synchronization or cluster synchronization. Analysis of systems whose dynamics conform to this description have attributed its origin to coupling with delays ( Dahms et al, 2012 ; Steur et al, 2016 ; Su et al, 2019 ); to variable coupling strength Pikovsky et al (2001) ; and to symmetries and symmetry breaking in the network structure Pecora et al (2014) . The weak couplings lead to variable phase angle differences, as shown in Tables 1 B and 2 B, and the variable phase differences form variable delays that are the most likely causes of partial synchronization in the nephron-arterial network.…”

Section: Discussionmentioning

confidence: 99%

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Interacting information streams on the nephron arterial network

Marsh,

Wexler,

Holstein-Rathlou

2023

Front. Netw. Physiol.

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Blood flow and glomerular filtration in the kidney are regulated by two mechanisms acting on the afferent arteriole of each nephron. The two mechanisms operate as limit cycle oscillators, each responding to a different signal. The myogenic mechanism is sensitive to a transmural pressure difference across the wall of the arteriole, and tubuloglomerular feedback (TGF) responds to the NaCl concentration in tubular fluid flowing into the nephron’s distal tubule,. The two mechanisms interact with each other, synchronize, cause oscillations in tubular flow and pressure, and form a bimodal electrical signal that propagates into the arterial network. The electrical signal enables nephrons adjacent to each other in the arterial network to synchronize, but non-adjacent nephrons do not synchronize. The arteries supplying the nephrons have the morphologic characteristics of a rooted tree network, with 3 motifs characterizing nephron distribution. We developed a model of 10 nephrons and their afferent arterioles in an arterial network that reproduced these structural characteristics, with half of its components on the renal surface, where experimental data suitable for model validation is available, and the other half below the surface, from which no experimental data has been reported. The model simulated several interactions: TGF-myogenic in each nephron with TGF modulating amplitude and frequency of the myogenic oscillation; adjacent nephron-nephron with strong coupling; non-adjacent nephron-nephron, with weak coupling because of electrical signal transmission through electrically conductive arterial walls; and coupling involving arterial nodal pressure at the ends of each arterial segment, and between arterial nodes and the afferent arterioles originating at the nodes. The model predicted full synchronization between adjacent nephrons pairs and partial synchronization among weakly coupled nephrons, reproducing experimental findings. The model also predicted aperiodic fluctuations of tubular and arterial pressures lasting longer than TGF oscillations in nephrons, again confirming experimental observations. The model did not predict complete synchronization of all nephrons.

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Section: Resultssupporting

confidence: 88%

Section: Discussionmentioning

confidence: 99%

Interacting information streams on the nephron arterial network

Marsh,

Wexler,

Holstein-Rathlou

2023

Front. Netw. Physiol.

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“…One topic which has attracted attention from the scientific community is the analysis and control of complex networks [312,313], which describe a wide variety of physical and social systems [314], from population interactions, brain activity, and language patterns to Internet traffic behavior among other interesting phenomena. One control problem derived from the complex systems control is the synchronization of delay coupled networks [315,316]. In this sense, a formal stability analysis for the synchronization of complex networks, in particular for a set of oscillators, is usually given for linearized systems in a vicinity of the equilibrium point [317] by computing the stability regions in the delay parameters or using the circle criterion [318].…”

Section: Complex Delay Complex Networkmentioning

confidence: 99%

A Panoramic Sketch about the Robust Stability of Time-Delay Systems and Its Applications

et al. 2020

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This paper presents a brief review on the current applications and perspectives on the stability of complex dynamical systems, with an emphasis on three main classes of systems such as delay-free systems, time-delay systems, and systems with uncertainties in its parameters, which lead to some criteria with necessary and/or sufficient conditions to determine stability and/or stabilization in the domains of frequency and time. Besides, criteria on robust stability and stability of nonlinear time-delay systems are presented, including some numerical approaches.

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“…Moreover, in that same paper, conditions for the emergence of diffusion-driven oscillations were presented. Results for different synchronous regimes, such as synchronization, 13,14 partial synchronization, 15,16 and pattern formation including standing, traveling, and rotating waves, 1,17,18 are found in given references.…”

Section: Introductionmentioning

confidence: 99%

Detecting coexisting oscillatory patterns in delay coupled Lur’e systems

Rogov

Pogromsky

Steur

et al. 2021

Chaos: An Interdisciplinary Journal of Nonlinear Science

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DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:

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Robust partial synchronization of delay-coupled networks

Abstract: Reconstructing bifurcation diagrams only from time-series data generated by electronic circuits in discrete-time dynamical systems Chaos:

Cited by 6 publications

References 23 publications

Interacting information streams on the nephron arterial network

Interacting information streams on the nephron arterial network

A Panoramic Sketch about the Robust Stability of Time-Delay Systems and Its Applications

Detecting coexisting oscillatory patterns in delay coupled Lur’e systems

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