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

Oscillations In An LC Circuit01:30

Oscillations In An LC Circuit

3.2K
An idealized LC circuit of zero resistance can oscillate without any source of emf by shifting the energy stored in the circuit between the electric and magnetic fields. In such an LC circuit, if the capacitor contains a charge q before the switch is closed, then all the energy of the circuit is initially stored in the electric field of the capacitor. This energy is given by
3.2K
Forced Oscillations01:06

Forced Oscillations

8.0K
When an oscillator is forced with a periodic driving force, the motion may seem chaotic. The motions of such oscillators are known as transients. After the transients die out, the oscillator reaches a steady state, where the motion is periodic, and the displacement is determined.
8.0K
Protein Networks02:26

Protein Networks

4.6K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.6K
Damped Oscillations01:07

Damped Oscillations

7.3K
In the real world, oscillations seldom follow true simple harmonic motion. A system that continues its motion indefinitely without losing its amplitude is termed undamped. However, friction of some sort usually dampens the motion, so it fades away or needs more force to continue. For example, a guitar string stops oscillating a few seconds after being plucked. Similarly, one must continually push a swing to keep a child swinging on a playground.
Although friction and other non-conservative...
7.3K
Network Covalent Solids02:18

Network Covalent Solids

16.2K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.2K
Limits with Oscillating Discontinuities01:19

Limits with Oscillating Discontinuities

485
An oscillating discontinuity is a type of discontinuity in which a function’s values fluctuate infinitely often as the input approaches a particular point. Unlike jump discontinuities, where the function suddenly shifts between two values, or infinite discontinuities, where the function diverges without bound, an oscillating discontinuity arises from rapid back-and-forth variation. Because the function never stabilizes toward a single value, no finite limit exists at that point.One of the...
485

You might also read

Related Articles

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

Sort by
Same author

Open neuroinformatics infrastructure ecosystem for federated multisite studies.

bioRxiv : the preprint server for biology·2026
Same author

Precise spike-timing information in the brainstem is well aligned with the needs of communication and the perception of environmental sounds.

PLoS biology·2025
Same author

Robust working memory in a two-dimensional continuous attractor network.

Cognitive neurodynamics·2024
Same author

Whole brain functional connectivity: Insights from next generation neural mass modelling incorporating electrical synapses.

PLoS computational biology·2024
Same author

Transcallosal white matter and cortical gray matter variations in autistic adults ages 30-73 years: A bi-tensor free water imaging approach.

Research square·2024
Same author

Brain anatomy and dynamics: A commentary on "Does the brain behave like a (complex) network? I. Dynamics" by Papo and Buldú (2024).

Physics of life reviews·2024
Same journal

Erratum: Low-dimensional model for adaptive networks of spiking neurons [Phys. Rev. E 111, 014422 (2025)].

Physical review. E·2026
Same journal

Disentangling the effects of many-body forces on depletion interactions.

Physical review. E·2026
Same journal

Charge transport and mode transition in dual-energy electron beam diodes.

Physical review. E·2026
Same journal

Optimization of multisite reactions in complex compartmentalized media.

Physical review. E·2026
Same journal

Origin of geometric cohesion in nonconvex granular materials: Interplay between interdigitation and rotational constraints enhancing frictional stability.

Physical review. E·2026
Same journal

Interaction of walkers with a standing Faraday wave.

Physical review. E·2026
See all related articles

Related Experiment Video

Updated: Feb 10, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.7K

Clusters in nonsmooth oscillator networks.

Rachel Nicks1, Lucie Chambon2, Stephen Coombes1

  • 1Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham NG7 2RD, United Kingdom.

Physical Review. E
|May 20, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces piecewise linear (PWL) oscillator models to explicitly analyze cluster states in coupled networks. This method enhances understanding of network dynamics and stability, applicable across various scientific fields.

More Related Videos

CRISPR Gene Editing Tool for MicroRNA Cluster Network Analysis
10:40

CRISPR Gene Editing Tool for MicroRNA Cluster Network Analysis

Published on: April 25, 2022

2.9K
Generation of Local CA1 γ Oscillations by Tetanic Stimulation
08:02

Generation of Local CA1 γ Oscillations by Tetanic Stimulation

Published on: August 14, 2015

9.5K

Related Experiment Videos

Last Updated: Feb 10, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.7K
CRISPR Gene Editing Tool for MicroRNA Cluster Network Analysis
10:40

CRISPR Gene Editing Tool for MicroRNA Cluster Network Analysis

Published on: April 25, 2022

2.9K
Generation of Local CA1 γ Oscillations by Tetanic Stimulation
08:02

Generation of Local CA1 γ Oscillations by Tetanic Stimulation

Published on: August 14, 2015

9.5K

Area of Science:

  • Dynamical systems theory
  • Network science
  • Computational neuroscience

Background:

  • The Master Stability Function (MSF) is a key tool for analyzing synchronous states in coupled oscillator networks.
  • Extending MSF to cluster states often relies on numerical methods due to difficulties in finding explicit periodic orbits.

Purpose of the Study:

  • To develop an analytical framework for investigating cluster states in coupled oscillator networks using piecewise linear (PWL) models.
  • To demonstrate how PWL models facilitate explicit analysis of periodic orbits and their stability.

Main Methods:

  • Utilizing piecewise linear (PWL) oscillator models to enable explicit construction of periodic orbits.
  • Augmenting the variational approach with saltation matrices to handle the discontinuities in PWL systems.
  • Analyzing specific network models, including integrate-and-fire and diffusively coupled planar PWL nodes.

Main Results:

  • PWL models allow for explicit variational analysis of periodic orbits and cluster states.
  • The stability of cluster states is shown to depend on both single node dynamics and network connectivity.
  • The developed method is applicable to a broad range of PWL systems in biology, physics, and engineering.

Conclusions:

  • Piecewise linear oscillator models provide a powerful analytical tool for studying cluster synchronization in complex networks.
  • The interplay between individual oscillator dynamics and network structure significantly influences overall network stability.
  • This framework offers explicit insights into network dynamics previously reliant on numerical approximations.