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

Brain Waves01:23

Brain Waves

2.4K
Brain waves are electrical signals generated by the neurons in the brain, which are regularly monitored to measure mental activities. Brain waves and their frequency ranges can be measured using an electroencephalogram or EEG. There are four main types of brain waves, each with distinct characteristics:
2.4K

You might also read

Related Articles

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

Sort by
Same author

An ECG biomarker for sudden cardiac death discovered with deep learning.

Nature·2026
Same author

Prenatal and postnatal household air pollution and longitudinal growth trajectories: results from long-term follow up of the GRAPHS cohort.

Environment international·2026
Same author

Isoscapes as a Regional-Scale Tool for Tracing Groundwater Uranium Cycling in the Northern Plains, United States.

Environmental science & technology·2025
Same author

Physical Exercise or Cognitive Behavioral Therapy for Takotsubo Cardiomyopathy: A Randomized Controlled Trial.

Circulation. Heart failure·2025
Same author

Addressing Commonly Asked Questions in Urogynecology: Accuracy and Limitations of ChatGPT.

International urogynecology journal·2025
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

Related Experiment Video

Updated: Nov 5, 2025

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

34.0K

Mean-Field Models for EEG/MEG: From Oscillations to Waves.

Áine Byrne1, James Ross2, Rachel Nicks2

  • 1School of Mathematics and Statistics, Science Centre, University College Dublin, South Belfield, Dublin 4, Ireland. aine.byrne@ucd.ie.

Brain Topography
|May 16, 2021
PubMed
Summary

This study introduces a new spiking neuron network model that precisely describes neuronal population activity. This advanced model, incorporating synchrony, offers a more biologically realistic approach than traditional neural mass models for brain rhythm research.

Keywords:
Brain rhythmsGap-junction couplingNeural fieldNeural massSynaptic couplingSynchronyWaves

More Related Videos

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

10.4K
Concurrent Recording of Co-localized Electroencephalography and Local Field Potential in Rodent
08:31

Concurrent Recording of Co-localized Electroencephalography and Local Field Potential in Rodent

Published on: November 30, 2017

12.5K

Related Experiment Videos

Last Updated: Nov 5, 2025

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

34.0K
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

10.4K
Concurrent Recording of Co-localized Electroencephalography and Local Field Potential in Rodent
08:31

Concurrent Recording of Co-localized Electroencephalography and Local Field Potential in Rodent

Published on: November 30, 2017

12.5K

Area of Science:

  • Computational Neuroscience
  • Theoretical Neuroscience
  • Neurodynamics

Background:

  • Traditional neural mass models (NMMs) offer simplified representations of large neuronal populations, primarily for understanding brain rhythms.
  • Despite their utility, NMMs are phenomenological and cannot fully capture the complex dynamics observed in biological neural tissue.
  • Existing models lack detailed mechanisms for synaptic and gap-junction interactions, limiting their biological realism.

Purpose of the Study:

  • To introduce and analyze a next-generation neural mass model derived from a simple spiking neuron network.
  • To extend this model to a spatially extended planar cortex for simulating large-scale brain activity.
  • To demonstrate the model's utility in electroencephalography (EEG) and magnetoencephalography (MEG) research, specifically investigating gap-junction coupling's role in synaptic waves.

Main Methods:

  • Developed a spiking neuron network model with both synaptic and gap-junction interactions.
  • Derived an exact mean-field description for the spiking neuron network, forming the basis of the new neural mass model.
  • Extended the mean-field model to a spatially extended planar cortex and applied it to simulate EEG/MEG data.

Main Results:

  • The new mean-field model incorporates an additional dynamical equation for within-population synchrony, enhancing its descriptive power.
  • The model successfully captures a richer repertoire of neural responses compared to traditional phenomenological NMMs.
  • Simulations revealed the significant role of local gap-junction coupling in shaping large-scale synaptic waves, relevant for EEG/MEG interpretation.

Conclusions:

  • The developed spiking neuron network-derived mean-field model provides a more neurobiologically grounded approach to modeling brain activity and rhythms.
  • This next-generation mass model offers improved accuracy and a wider range of dynamics compared to conventional NMMs.
  • The model serves as a valuable tool for EEG/MEG analysis, aiding in the understanding of neural synchrony and the functional impact of gap junctions.