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

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
Protein Networks02:26

Protein Networks

2.9K
2.9K
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
Network Function of a Circuit01:25

Network Function of a Circuit

840
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
840
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

490
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...
490
Population Growth00:57

Population Growth

28.6K
Population size is dynamic, increasing with birth rates and immigration, and decreasing with death rates and emigration. In ideal conditions with unlimited resources, populations can increase exponentially, which plots as a J-shaped growth rate curve of population size against time. This type of curve is characteristic of newly-introduced invasive species, or populations that have suffered catastrophic declines and are rebounding.
28.6K

You might also read

Related Articles

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

Sort by
Same author

Stochastic choice drives variability in patch foraging decisions in humans and rats.

Communications psychology·2026
Same author

The Computational Bottleneck of Basal Ganglia Output (and What to Do About it).

eNeuro·2025
Same author

Motor Cortex Latent Dynamics Encode Spatial and Temporal Arm Movement Parameters Independently.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2024
Same author

Tracking subjects' strategies in behavioural choice experiments at trial resolution.

eLife·2024
Same author

Motor cortex latent dynamics encode spatial and temporal arm movement parameters independently.

bioRxiv : the preprint server for biology·2023
Same author

Activity Subspaces in Medial Prefrontal Cortex Distinguish States of the World.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2022

Related Experiment Video

Updated: Feb 6, 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

2.7K

Dynamical networks: Finding, measuring, and tracking neural population activity using network science.

Mark D Humphries1

  • 1Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom.

Network Neuroscience (Cambridge, Mass.)
|August 10, 2018
PubMed
Summary

Network science offers scalable tools to analyze large neural recordings, visualizing and quantifying neuron interactions. This approach helps understand neural computation and population dynamics in systems neuroscience.

Keywords:
Calcium imagingGraph theoryMultineuron recordingsNetwork theoryNeural ensemblesSystems neuroscience

More Related Videos

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

6.3K
Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
07:57

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

9.2K

Related Experiment Videos

Last Updated: Feb 6, 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

2.7K
A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

6.3K
Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
07:57

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

9.2K

Area of Science:

  • Systems Neuroscience
  • Computational Neuroscience
  • Network Science

Background:

  • Systems neuroscience aims to record from increasing numbers of neurons simultaneously to understand neural computation.
  • Analyzing large-scale neural recordings presents significant challenges in visualization, description, and quantification of neuronal interactions.

Purpose of the Study:

  • To propose network science as a scalable analytical framework for large-scale neural population recordings.
  • To demonstrate how network science can address the challenges of visualizing, describing, and quantifying neuronal interactions.

Main Methods:

  • Representing neurons as nodes and their interactions as links to form a network from simultaneous recordings.
  • Applying network science analytical tools to visualize and describe neuronal population activity.
  • Quantifying circuit manipulations, tracking population dynamics, and defining neural population concepts like cell assemblies.

Main Results:

  • Network science provides a unified framework to visualize and describe arbitrarily large neuronal recordings.
  • The network approach enables quantitative analysis of neural circuit perturbations and population dynamics over time.
  • Network science facilitates the quantitative definition of theoretical constructs such as cell assemblies.

Conclusions:

  • Network science offers powerful, scalable tools to analyze complex neural population data.
  • Integrating network science into the analysis of population recordings will advance the understanding of neural computation.
  • This approach provides both qualitative and quantitative insights into brain function at the population level.