Jove
Visualize
Contact Us

Related Concept Videos

Protein Networks02:26

Protein Networks

4.5K
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,...
4.5K
Protein Networks02:26

Protein Networks

2.8K
2.8K
Optimal Foraging00:48

Optimal Foraging

13.8K
How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
13.8K
Network Covalent Solids02:18

Network Covalent Solids

16.1K
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...
16.1K
What is Behavior?00:54

What is Behavior?

10.2K
Behaviors are actions that an organism engages in—they can be related to finding food, reproducing, defending against threats, and many other possible actions. Behaviors include activities related to the environment around the animal—such as migration—as well as social interactions within a species or population. Many behaviors involve motor output—that is, muscle movements—while others involve less visible actions, such as learning.
10.2K
Optimization Problems01:26

Optimization Problems

60
Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...
60

You might also read

Related Articles

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

Sort by
Same author

Effects of the interplay between topology and function of an integrated urban development on patterns of user movement.

Scientific reports·2024
Same author

Adaptivity: a path towards general swarm intelligence?

Frontiers in robotics and AI·2023
Same author

Beyond Bio-Inspired Robotics: How Multi-Robot Systems Can Support Research on Collective Animal Behavior.

Frontiers in robotics and AI·2022
Same author

Author Correction: Transition from simple to complex contagion in collective decision-making.

Nature communications·2022
Same author

Pomegranate seed polyphenol-based nanosheets as an efficient inhibitor of amyloid fibril assembly and cytotoxicity of HEWL.

RSC advances·2022
Same author

Transition from simple to complex contagion in collective decision-making.

Nature communications·2022
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: Jan 26, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.8K

Optimal network topology for responsive collective behavior.

David Mateo1, Nikolaj Horsevad1, Vahid Hassani1

  • 1Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore.

Science Advances
|April 6, 2019
PubMed
Summary
This summary is machine-generated.

The optimal network topology for collective response depends on signal frequency. Higher network connectivity aids slow signals, while lower connectivity benefits fast signals in multi-agent systems.

More Related Videos

Profiling Maternal Behavior Responses During Whole-Brain Imaging
07:12

Profiling Maternal Behavior Responses During Whole-Brain Imaging

Published on: January 24, 2025

1.4K
Author Spotlight: Collective Behavioral Analysis of the Nematode, Caenorhabditis elegans
03:32

Author Spotlight: Collective Behavioral Analysis of the Nematode, Caenorhabditis elegans

Published on: August 25, 2023

1.5K

Related Experiment Videos

Last Updated: Jan 26, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.8K
Profiling Maternal Behavior Responses During Whole-Brain Imaging
07:12

Profiling Maternal Behavior Responses During Whole-Brain Imaging

Published on: January 24, 2025

1.4K
Author Spotlight: Collective Behavioral Analysis of the Nematode, Caenorhabditis elegans
03:32

Author Spotlight: Collective Behavioral Analysis of the Nematode, Caenorhabditis elegans

Published on: August 25, 2023

1.5K

Area of Science:

  • Robotics
  • Complex Systems
  • Network Science

Background:

  • Agents in dynamic environments require effective information transfer via interaction networks for collective responses.
  • Network topology significantly influences the performance of distributed decision-making systems.

Purpose of the Study:

  • To investigate how network topology affects the collective response of a system to a driving signal.
  • To determine the optimal network structure for different signal frequencies and system sizes.

Main Methods:

  • Utilized an archetypal model of distributed decision-making.
  • Studied system capacity to follow a driving signal across various topologies and system sizes.
  • Conducted experiments with a swarm of robots.

Main Results:

  • Discovered a nontrivial relationship between driving signal frequency and optimal network topology.
  • Collective response to slow perturbations increases with network degree.
  • Collective response to fast perturbations decreases with network degree.

Conclusions:

  • Network topology is critical for effective collective operations in dynamic environments.
  • Dynamic rewiring of interaction networks is essential for adapting to different timescales.
  • Findings have implications for designing robust multi-agent and robotic systems.