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

An algorithmic method for reducing conductance-based neuron models.

Michael E Sorensen1, Stephen P DeWeerth

  • 1Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.

Biological Cybernetics
|June 29, 2006
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

Electrical Stimulation of Distal Tibial Nerve During Stance Phase of Walking May Reverse Effects of Unilateral Paw Pad Anesthesia in the Cat.

Motor control·2022
Same author

Analyzing the Effects of Parameters for Tremor Modulation via Phase-Locked Electrical Stimulation on a Peripheral Nerve.

Journal of personalized medicine·2022
Same author

Fitts' Law Based Performance Metrics to Quantify Tremor in Individuals With Essential Tremor.

IEEE journal of biomedical and health informatics·2021
Same author

Optimization of Stimulation Parameters for Targeted Activation of Multiple Neurons Using Closed-Loop Search Methods.

Processes (Basel, Switzerland)·2021
Same author

A Wearable System for Attenuating Essential Tremor Based on Peripheral Nerve Stimulation.

IEEE journal of translational engineering in health and medicine·2020
Same author

Differential cardiovascular responses to cutaneous afferent subtypes in a nociceptive intersegmental spinal reflex.

Scientific reports·2019
Same journal

Harmonic memory in phasor neural networks.

Biological cybernetics·2026
Same journal

Correction: Decreased spinal inhibition leads to undiversified locomotor patterns.

Biological cybernetics·2026
Same journal

Foundational issues of network models in biology.

Biological cybernetics·2026
Same journal

Dynamical mechanisms for coordinating long-term working memory based on the precision of spike-timing in cortical neurons.

Biological cybernetics·2026
Same journal

Distinct dopaminergic spike-timing-dependent plasticity rules are suited to different functional roles.

Biological cybernetics·2026
Same journal

Fluctuation-response relations for a two-stage population of spiking neurons stimulated by common noise.

Biological cybernetics·2026
See all related articles

This study introduces an automated method to simplify complex neural models, reducing computational demands while preserving accuracy. The new approach requires less prior knowledge of neuron dynamics for effective model reduction.

Area of Science:

  • Computational Neuroscience
  • Mathematical Biology

Background:

  • Conductance-based neural models offer high biological realism but suffer from computational complexity.
  • Existing model reduction techniques often necessitate deep prior knowledge of the original model's dynamics.

Purpose of the Study:

  • To present an automated, algorithmic method for reducing conductance-based neuron models.
  • To simplify complex neuronal models while retaining essential dynamics and minimizing performance loss.

Main Methods:

  • Utilized the method of equivalent potentials for automated model reduction.
  • Developed a cost function based on state variable contribution to total conductance.

Main Results:

  • The algorithm successfully reduced model complexity with minimal loss in performance.

Related Experiment Videos

  • The method requires significantly less prior knowledge of the model's dynamics compared to traditional approaches.
  • Optimizing the cost function further improved the algorithm's performance.

    • Conclusions:

    • Automated reduction of conductance-based neuron models is feasible and effective.
    • The method of equivalent potentials, combined with a conductance-based cost function, offers a powerful tool for simplifying neural simulations.
    • This approach enhances the accessibility and analysis of complex neuronal models.