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 Concept Videos

Atomic Nuclei: Types of Nuclear Relaxation01:28

Atomic Nuclei: Types of Nuclear Relaxation

Nuclear relaxation restores the equilibrium population imbalance and can occur via spin–lattice or spin–spin mechanisms, which are first-order exponential decay processes.
In spin–lattice or longitudinal relaxation, the excited spins exchange energy with the surrounding lattice as they return to the lower energy level. Among several mechanisms that contribute to spin–lattice relaxation, magnetic dipolar interactions are significant. Here, the excited nucleus transfers energy to a nearby...
Relaxation of Skeletal Muscles01:29

Relaxation of Skeletal Muscles

The period of muscle contraction primarily influences the duration of stimulation at the neuromuscular junction (NMJ), the presence of free calcium ions in the sarcoplasm, and the availability of energy or ATP to support contractions.
When an action potential reaches the axon terminal, it depolarizes the membrane and opens voltage-gated sodium channels. Sodium ions enter the cell, further depolarizing the presynaptic membrane. This depolarization causes voltage-gated calcium channels to open.
Atomic Nuclei: Nuclear Relaxation Processes01:23

Atomic Nuclei: Nuclear Relaxation Processes

In the absence of an external magnetic field, nuclear spin states are degenerate and randomly oriented. When a magnetic field is applied, the spins begin to precess and orient themselves along (lower energy) or against (higher energy) the direction of the field. At equilibrium, a slight excess population of spins exists in the lower energy state. Because the direction of the magnetic field is fixed as the z-axis,  the precessing magnetic moments are randomly oriented around the z-axis. This...
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system.
Stability of Equilibrium Configuration01:23

Stability of Equilibrium Configuration

Understanding the stability of equilibrium configurations is a fundamental part of mechanical engineering. In any system, there are three distinct types of equilibrium: stable, neutral, and unstable.
A stable equilibrium occurs when a system tends to return to its original position when given a small displacement, and the potential energy is at its minimum. An example of a stable equilibrium is when a cantilever beam is fixed at one end and a weight is attached to the other end. If the weight...
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...

You might also read

Related Articles

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

Sort by
Same author

Anomaly, class division, and decoupling in income dynamics.

Physical review. E·2026
Same author

Exploring how deep learning decodes anomalous diffusion via Grad-CAM.

Nature communications·2026
Same author

Coherence enhanced by detrained oscillators: Breaking π-reflection symmetry.

Chaos (Woodbury, N.Y.)·2025
Same author

Evaluating the impact of NPC1 single nucleotide polymorphisms on entry efficiency of filoviruses in vitro: Agent-based model approach.

Journal of theoretical biology·2025
Same author

Anderson's negative-<i>U</i> chemistry in amorphous silicon nitride: A complex system approach.

Science advances·2025
Same author

Quantitative evaluation of methods to analyze motion changes in single-particle experiments.

Nature communications·2025

Related Experiment Video

Updated: Jul 2, 2026

New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies
05:59

New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies

Published on: October 6, 2023

Relaxation of synchronization on complex networks.

Seung-Woo Son1, Hawoong Jeong, Hyunsuk Hong

  • 1Department of Physics, Institute for the BioCentury, Korea Advanced Institute of Science and Technology, Daejeon 305-701, Korea. sonswoo@kaist.ac.kr

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|September 4, 2008
PubMed
Summary

We investigated collective synchronization in coupled oscillators. Relaxation time to achieve synchronization is independent of network size in the strong-coupling regime, revealing insights into information transfer and system recovery.

More Related Videos

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study
05:33

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study

Published on: September 8, 2021

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
06:40

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

Related Experiment Videos

Last Updated: Jul 2, 2026

New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies
05:59

New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies

Published on: October 6, 2023

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study
05:33

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study

Published on: September 8, 2021

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
06:40

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

Area of Science:

  • Physics
  • Complex Systems
  • Network Science

Background:

  • Collective synchronization is crucial for information transfer and system recovery in coupled oscillator systems.
  • Understanding relaxation dynamics is key to analyzing how these systems achieve order and respond to perturbations.

Purpose of the Study:

  • To investigate the relaxation dynamics of collective synchronization in large networks of coupled oscillators.
  • To determine the influence of network structural properties and system size on synchronization relaxation time (tau).

Main Methods:

  • Numerical simulations were employed to measure relaxation time (tau) across various complex network structures.
  • Analysis focused on the strong-coupling regime (K>Kc) and the impact of initial phase fluctuations.

Main Results:

  • In the strong-coupling regime, relaxation time (tau) logarithmically increases with network size (N) due to initial phase fluctuations.
  • After accounting for initial phase fluctuations, relaxation time becomes independent of system size.
  • Analytical derivation of relaxation dynamics confirmed independence from system size, aligning with simulation results.

Conclusions:

  • Network structure and local interactions are less relevant to synchronization relaxation dynamics in the strong-coupling regime than initially assumed.
  • The findings provide a fundamental understanding of synchronization recovery and information processing in complex networks.