2021
DOI: 10.1103/physreve.103.042315
|View full text |Cite
|
Sign up to set email alerts
|

What adaptive neuronal networks teach us about power grids

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

1
13
0

Year Published

2021
2021
2023
2023

Publication Types

Select...
4
3
1

Relationship

3
5

Authors

Journals

citations
Cited by 39 publications
(18 citation statements)
references
References 87 publications
1
13
0
Order By: Relevance
“…We note that, as it has been shown in Ref. 29, the phase oscillator models with inertia are a subclass of phase oscillator models with adaptivity. In particular, considering L = 0 in (26) completely resembles the finding for phase oscillator models with inertia in (13).…”
Section: Stability Of M-splay Statessupporting
confidence: 61%
See 2 more Smart Citations
“…We note that, as it has been shown in Ref. 29, the phase oscillator models with inertia are a subclass of phase oscillator models with adaptivity. In particular, considering L = 0 in (26) completely resembles the finding for phase oscillator models with inertia in (13).…”
Section: Stability Of M-splay Statessupporting
confidence: 61%
“…In the following, we study the linear stability of generalized splay states in a globally coupled network of N coupled phase oscillators with inertia 25,26,28,29,75,76,78 of the form…”
Section: B Application To the Kuramoto-sakaguchi Model With Inertiamentioning
confidence: 99%
See 1 more Smart Citation
“…Various synchronization patterns are known such as cluster synchronization where the network splits into groups of synchronous elements Dahms et al (2012), or partial synchronization patterns such as chimera states where the system splits into coexisting domains of coherent (synchronized) and incoherent (desynchronized) states Kuramoto and Battogtokh (2002), Abrams and Strogatz (2004), Panaggio and Abrams (2015), Sawicki (2019), Schöll (2020), Schöll et al (2020). These patterns were also explored in adaptive networks Seliger et al (2002), Aoki and Aoyagi (2009), Timms and English (2014), Kasatkin et al (2017), Berner et al (2019b), Berner et al (2021b), Berner et al (2021d), and in particular in adaptive two-layer networks of phase oscillators Kasatkin and Nekorkin (2018), Berner et al (2020b). Moreover, the role of synchronization is an important aspect in the field of network physiology, where multi-component physiological systems continuously interact in an integrated network to coordinate their functions Bashan et al (2012), Ivanov and Bartsch (2014), Bartsch et al (2015), Moorman et al (2016), Lin et al (2016).…”
Section: Introductionmentioning
confidence: 99%
“…This work introduces a methodology to study synchronization in adaptive networks with heterogeneous plasticity (adaptation) rules. As a paradigmatic system, we consider an adaptively coupled phase oscillator network [69][70][71][72][73][74][75], which is proven to be useful for predicting and describing phenomena occurring in more realistic and detailed models [76][77][78][79]. More specifically, in the spirit of the master stability function approach, we consider the synchronization problem as the interplay between network structure and a heterogeneous adaptation rule arising from distance-(or location-)dependent synaptic plasticity.…”
Section: Introductionmentioning
confidence: 99%