Synchronization hypothesis in the Winfree model
W Oukil (USTHB, IMB), A Kessi (USTHB), Ph Thieullen (IMB)

TL;DR
This paper investigates the conditions under which synchronization occurs in the Winfree oscillator model, identifying a new hypothesis that delineates the parameter domain for synchronization regardless of oscillator count or frequency distribution.
Contribution
The authors propose a new hypothesis for synchronization in the Winfree model and demonstrate its necessity through numerical counter-examples, expanding understanding of synchronization conditions.
Findings
Synchronization domain includes both weak and strong coupling regimes.
Domain of synchronization is independent of the number of oscillators.
A numerical counter-example confirms the necessity of the hypothesis.
Abstract
We consider oscillators coupled by a mean field as in the Winfree model. The model is governed by two parameters: the coupling strength and the spectrum width of the frequencies of each oscillator. In the uncoupled regime, , each oscillator possesses its own natural frequency, and the difference between the phases of any two oscillators grows linearly in time. We say that oscillators are synchronized if the difference between any two phases is uniformly bounded in time. We identify a new hypothesis for the existence of synchronization. The domain in of synchronization contains coupling values that are both weak and strong. Moreover the domain is independent of the number of oscillators and the distribution of the frequencies. We give a numerical counter-example which shows that this hypothesis is necessary for the existence of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNonlinear Dynamics and Pattern Formation · stochastic dynamics and bifurcation · Neural dynamics and brain function
