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

Relationship Formation02:12

Relationship Formation

What do you think is the single most influential factor in determining with whom you become friends and whom you form romantic relationships? You might be surprised to learn that the answer is simple: the people with whom you have the most contact. This most important factor is proximity. You are more likely to be friends with people you have regular contact with. For example, there are decades of research that shows that you are more likely to become friends with people who live in your dorm,...
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...
Stability of structures01:14

Stability of structures

In mechanical engineering, the stability of systems under various forces is critical for designing durable and efficient structures. One fundamental way to explore these concepts is by analyzing systems like two rods connected at a pivot point, O, with a torsional spring of spring constant k at the pivot point. This system is similar in appearance to a scissor jack used to change tires on a car. In this case, the arms of the linkage (equivalent to the rods in this system) are entirely vertical,...
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.
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...
Multimachine Stability01:25

Multimachine Stability

Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:

You might also read

Related Articles

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

Sort by
Same author

Antithetic population response to antibiotics in a polybacterial community.

Science advances·2020
Same author

Attractor dynamics of a Boolean model of a brain circuit controlled by multiple parameters.

Chaos (Woodbury, N.Y.)·2018
Same author

Detection of generalized synchronization using echo state networks.

Chaos (Woodbury, N.Y.)·2018
Same author

Emergent bimodal firing patterns implement different encoding strategies during gamma-band oscillations.

Frontiers in computational neuroscience·2013
Same author

Integration of cellular signals in chattering environments.

Progress in biophysics and molecular biology·2012
Same author

Zero-lag synchronization and bubbling in delay-coupled lasers.

Physical review. E, Statistical, nonlinear, and soft matter physics·2012

Related Experiment Video

Updated: Jul 14, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Consistency of heterogeneous synchronization patterns in complex weighted networks.

D Malagarriga1, A E P Villa2, J Garcia-Ojalvo3

  • 1Departament de Física, Universitat Politècnica de Catalunya. Edifici Gaia, Rambla Sant Nebridi 22, 08222 Terrassa, Spain.

Chaos (Woodbury, N.Y.)
|April 3, 2017
PubMed
Summary

Complex networks exhibit diverse synchronization patterns, not just homogeneous ones. Heterogeneous coupling and asymmetry in small networks promote varied synchronization, enhancing network consistency.

More Related Videos

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

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

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

Published on: June 9, 2023

Related Experiment Videos

Last Updated: Jul 14, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

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

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

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

Published on: June 9, 2023

Area of Science:

  • Complex systems
  • Network science
  • Dynamical systems

Background:

  • Synchronization in complex networks is typically assumed to be uniform across all nodes.
  • Understanding diverse synchronization behaviors is crucial for network analysis.

Purpose of the Study:

  • To investigate the emergence of diverse synchronization patterns within a single complex network.
  • To analyze how network properties like coupling heterogeneity and asymmetry influence synchronization diversity and consistency.

Main Methods:

  • Simulations of interacting oscillators in complex weighted networks.
  • Analysis of synchronization patterns and their persistence (consistency) under varying initial conditions.
  • Comparison of synchronization in heterogeneous networks versus regular networks.

Main Results:

  • Subsets of oscillators within a single network can synchronize in distinct ways.
  • Increased heterogeneity in coupling weights and asymmetry promote diverse synchronization patterns in small networks.
  • Complex weighted networks demonstrate richer consistency of coexistent synchronization patterns compared to regular networks.

Conclusions:

  • Complex networks can exhibit multiple, coexisting synchronization patterns, challenging the assumption of homogeneity.
  • Network topology, particularly heterogeneity and asymmetry, plays a key role in shaping synchronization dynamics and consistency.
  • The findings provide insights into the prevalence of specific network topologies observed in experimental data.