Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Nov 27, 2025

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.7K

Explosive synchronization in temporal networks: A comparative study.

Tanu Singla1, M Rivera2

  • 1Tecnológico de Monterrey, Calle del Puente 222, Colonia Ejidos de Huipulco, Tlalpan, Ciudad de México 14380, México.

Chaos (Woodbury, N.Y.)
|December 2, 2020
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

1.7K
The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the...
1.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

An alternate approach to simulate the dynamics of perturbed liquid drops.

Chaos (Woodbury, N.Y.)·2022
Same author

Entrainment of aperiodic and periodic oscillations in the Mercury Beating Heart system using external periodic forcing.

Chaos (Woodbury, N.Y.)·2019
Same journal

Gap junction architecture and synchronization clusters in the thalamic reticular nuclei.

Chaos (Woodbury, N.Y.)·2026
Same journal

Exact computation of Lyapunov exponents via system parameters in multi-triangle chaotic maps: Bifurcation analysis and circuit realization.

Chaos (Woodbury, N.Y.)·2026
Same journal

Integrating score-based generative modeling and neural ODEs for accurate representation of multiscale chaotic dynamics.

Chaos (Woodbury, N.Y.)·2026
Same journal

A data-driven tuberculosis model with behavioral changes and saturated treatment: Optimal control and cost-effectiveness study.

Chaos (Woodbury, N.Y.)·2026
Same journal

Breathers, rational solutions, and their exact physical spectra in F = 1 spinor Bose-Einstein condensates.

Chaos (Woodbury, N.Y.)·2026
Same journal

Finite invariant sets with bridging points in logistic IFS.

Chaos (Woodbury, N.Y.)·2026
See all related articles

This study compares explosive synchronization (ES) in temporal networks of phase oscillators. We analyzed how oscillator movement and changing connections affect the coupling needed for synchronization.

Area of Science:

  • Complex Systems
  • Nonlinear Dynamics
  • Network Science

Background:

  • Explosive Synchronization (ES) is a phenomenon observed in coupled oscillator systems.
  • Temporal networks, where connections change over time, introduce unique dynamics compared to static networks.

Purpose of the Study:

  • To comparatively study Explosive Synchronization (ES) in phase oscillator networks with temporal characteristics.
  • To investigate the impact of different temporal network models on the critical coupling required for ES.

Main Methods:

  • Modeled temporal networks using two configurations: mobile oscillators in a 2D box and static oscillators with random partner switching.
  • Investigated mobile oscillators with fixed neighbors versus coupling to all within a 'circle of vision'.
  • Monitored network degree, oscillator velocities, circle of vision radius, and connection probability.

More Related Videos

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis
05:59

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

Published on: October 6, 2023

3.0K
Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.7K

Related Experiment Videos

Last Updated: Nov 27, 2025

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.7K
Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis
05:59

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

Published on: October 6, 2023

3.0K
Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.7K

Main Results:

  • Compared critical coupling values for inducing ES across different temporal network configurations.
  • Identified how network topology and oscillator dynamics influence the onset of explosive synchronization.
  • Quantified the relationship between movement parameters and synchronization thresholds.

Conclusions:

  • The study provides insights into the mechanisms driving Explosive Synchronization in dynamic network environments.
  • Findings highlight the importance of network temporal properties in understanding synchronization phenomena.
  • Results contribute to the broader understanding of complex systems and emergent behaviors in coupled oscillators.