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

Variance01:15

Variance

The deviations show how spread out the data are about the mean. A positive deviation occurs when the data value exceeds the mean, whereas a negative deviation occurs when the data value is less than the mean. If the deviations are added, the sum is always zero. So one cannot simply add the deviations to get the data spread. By squaring the deviations, the numbers are made positive; thus, their sum will also be positive.The standard deviation measures the spread in the same units as the data.
Neural Regulation01:37

Neural Regulation

Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
Electrostatic Boundary Conditions01:16

Electrostatic Boundary Conditions

Consider an external electric field propagating through a homogeneous medium. When the electric field crosses the surface boundary of the medium, it undergoes a discontinuity. The electric field can be resolved into normal and tangential components. The amount by which the field changes at any boundary is given by the difference between the field components above and below the surface boundary.
The surface integral of an electric field is given by Gauss's law in integral form and is related to...
Differential Form of Maxwell's Equations01:17

Differential Form of Maxwell's Equations

James Clerk Maxwell (1831–1879) was one of the significant contributors to physics in the nineteenth century. He is probably best known for having combined existing knowledge of the laws of electricity and the laws of magnetism with his insights to form a complete overarching electromagnetic theory, represented by Maxwell's equations. The four basic laws of electricity and magnetism were discovered experimentally through the work of physicists such as Oersted, Coulomb, Gauss, and Faraday.
Propagation of Action Potentials01:23

Propagation of Action Potentials

The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
Variability: Analysis01:11

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...

You might also read

Related Articles

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

Sort by
Same author

Exploring farmers' perceptions of the value and management of dairy-bred calves in block calving dairy systems.

Journal of dairy science·2026
Same author

Neural field modeling and analysis of consciousness states in the brain.

Neuroscience of consciousness·2025
Same author

Generation of surrogate brain maps preserving spatial autocorrelation through random rotation of geometric eigenmodes.

Imaging neuroscience (Cambridge, Mass.)·2025
Same author

Empirical estimation of the eigenmodes of macroscale cortical dynamics: Reconciling neural field eigenmodes and resting-state networks.

Neuroimage. Reports·2025
Same author

Electrical circuit model of spatiotemporal trade dynamics: Foundations and derivation of the gravity model.

PloS one·2025
Same author

Unification of alpha, mu, and tau rhythms and their beta-band harmonics via eigenmodes: spectral peaks, topography, and reactivity.

Journal of theoretical biology·2025
Same journal

Discrete-time exploitative competition model of different stage-specific predators.

Journal of mathematical biology·2026
Same journal

Spatiotemporal SEIQR Epidemic Modeling with Optimal Control for Vaccination, Treatment, and Social Measures.

Journal of mathematical biology·2026
Same journal

Phenotypic plasticity trade-offs in an age-structured model of bacterial growth under stress.

Journal of mathematical biology·2026
Same journal

Intraspecific interactions facilitate mutualism across multilayer networks under weak selection.

Journal of mathematical biology·2026
Same journal

A two-species competition model on a compact metric graph for the invasion and competition of Aedes Aegypti and Aedes Albopictus mosquitoes in Florida.

Journal of mathematical biology·2026
Same journal

Superinfection and the hypnozoite reservoir for Plasmodium vivax: a multitype branching process approximation.

Journal of mathematical biology·2026
See all related articles

Related Experiment Video

Updated: May 22, 2026

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

Neural field theory with variance dynamics.

P A Robinson1

  • 1School of Physics, University of Sydney, Sydney, NSW 2006, Australia. robinson@physics.usyd.edu.au

Journal of Mathematical Biology
|May 12, 2012
PubMed
Summary
This summary is machine-generated.

This study incorporates neural voltage variance feedback into neural field models, revealing its impact on system stability and dynamics. Findings clarify model limitations and offer new insights into neural system behavior near bifurcations.

More Related Videos

External Excitation of Neurons Using Electric and Magnetic Fields in One- and Two-dimensional Cultures
08:32

External Excitation of Neurons Using Electric and Magnetic Fields in One- and Two-dimensional Cultures

Published on: May 7, 2017

Related Experiment Videos

Last Updated: May 22, 2026

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

External Excitation of Neurons Using Electric and Magnetic Fields in One- and Two-dimensional Cultures
08:32

External Excitation of Neurons Using Electric and Magnetic Fields in One- and Two-dimensional Cultures

Published on: May 7, 2017

Area of Science:

  • Computational Neuroscience
  • Theoretical Neuroscience
  • Neural Field Theory

Background:

  • Traditional neural field models focus on mean neural activity and variance without feedback.
  • Second-order quantities like variance have not been integrated into the dynamics of these models.

Purpose of the Study:

  • To investigate the effects of neural voltage variance feedback on neural system steady states and dynamics.
  • To determine the impact of variance feedback on fixed points and the variance itself.
  • To clarify the validity of models with and without variance dynamics.

Main Methods:

  • Linear neural field theory was used to estimate neural voltage variance.
  • This variance was incorporated into the nonlinear firing rate-voltage response function.
  • Fixed points and variance were recalculated with the updated response function.

Main Results:

  • Variance feedback alters the stability of neural systems, particularly near saddle-node bifurcations.
  • Stability against saddle-node bifurcation is reduced in purely cortical systems.
  • In corticothalamic systems, stability changes depend on the initial state.
  • New estimates for critical variance scalings near bifurcations were derived.

Conclusions:

  • Including variance dynamics provides a more comprehensive understanding of neural systems.
  • The findings highlight limitations of previous models that ignored variance feedback.
  • New theoretical tools and insights are provided for analyzing neural dynamics and bifurcations.