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 Experiment Videos

Local network parameters can affect inter-network phase lags in central pattern generators.

S R Jones1, N Kopell

  • 1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA. srjones@nmr.mgh.harvard.edu

Journal of Mathematical Biology
|October 1, 2005
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Is there a ubiquitous spectrolaminar motif of local field potential power across primate neocortex?

bioRxiv : the preprint server for biology·2024
Same author

Patterns of ethanol intake in male rats with partial dopamine transporter deficiency.

Genes, brain, and behavior·2023
Same author

Teaching Hardy-Weinberg Equilibrium using Population-Level Punnett Squares: Facilitating Calculation for Students with Math Anxiety.

CBE life sciences education·2021
Same author

NeuroDAC: an open-source arbitrary biosignal waveform generator.

Journal of neural engineering·2020
Same author

The brain gene expression profile of dopamine D2/D3 receptors and associated signaling proteins following amphetamine self-administration.

Neuroscience·2015
Same author

MAOA expression predicts vulnerability for alcohol use.

Molecular psychiatry·2015
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

This study examines how combined weak and strong coupling mechanisms in neural networks create stable phase lags. It reveals how local network parameters, like inhibition decay time, influence inter-network phase differences in central pattern generators.

Area of Science:

  • Computational Neuroscience
  • Systems Neuroscience
  • Biophysics

Background:

  • Oscillators exhibit distinct coupling mechanisms (weak vs. strong) influencing phase lag stability.
  • Central pattern generators (CPGs) often integrate both weak and strong coupling features.
  • Understanding phase lag generation is crucial for CPG function.

Purpose of the Study:

  • To analyze phase lags in systems combining weakly coupled networks of strongly coupled oscillators.
  • To investigate the impact of local network parameters on inter-network phase lags.
  • To model the crayfish CPG for swimming.

Main Methods:

  • Application of geometrical singular perturbation theory.
  • Analysis of coupled relaxation oscillator networks.

Related Experiment Videos

  • Modeling of neural network dynamics.

    • Main Results:

    • Demonstrated how combined weak and strong coupling mechanisms produce stable phase lags.
    • Identified specific local network parameters, such as decay time of inhibition, that modulate inter-network phase lags.
    • Provided a theoretical framework applicable to biological CPGs.

    Conclusions:

    • The interplay between local strong coupling and weak inter-network coupling dictates phase lag dynamics in CPGs.
    • Local network parameters offer a mechanism for fine-tuning inter-network phase relationships.
    • The findings offer insights into the control of rhythmic motor behaviors like swimming.