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

Rate models for conductance-based cortical neuronal networks.

Oren Shriki1, David Hansel, Haim Sompolinsky

  • 1Racah Institute of Physics, Hebrew University, Jerusalem 91904, Israel, and Center for Neural Computation, Hebrew University, Jerusalem 91904, Israel. orens@fiz.huji.ac.il

Neural Computation
|September 27, 2003
PubMed
Summary

Simplified rate models can accurately describe large neuronal networks, bridging biophysical interpretations and experimental validation. This work provides a precise mapping for conductance-based models, enhancing their use in neuroscience research.

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

Unraveling the geometry of visual relational reasoning.

Scientific reports·2026
Same author

Order parameters and phase transitions of continual learning in deep neural networks.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Synchrony timescales underlie irregular neocortical spiking.

Neuron·2025
Same author

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

Neuroscience of consciousness·2025
Same author

Reward-driven adaptation of movements requires strong recurrent basal ganglia-cortical loops.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

The fluctuation-based regime of thalamocortical circuitry.

bioRxiv : the preprint server for biology·2025

Area of Science:

  • Computational Neuroscience
  • Systems Neuroscience
  • Neuronal Network Modeling

Background:

  • Population rate models are crucial for understanding neuronal systems but lack clear biophysical interpretations, limiting their application.
  • Existing models struggle with experimental validation due to ambiguous parameter meanings.

Purpose of the Study:

  • To establish a precise link between conductance-based neuronal network models and simplified rate models.
  • To enable more accurate biophysical interpretations and experimental validation of neuronal network dynamics.

Main Methods:

  • Derived a parameter mapping between conductance-based and rate models for time-independent inputs.
  • Validated the subtractive effect assumption using a Hodgkin-Huxley model with a potassium A-current.

Related Experiment Videos

  • Extended the rate model to the dynamic domain using second-order differential equations for time-dependent inputs.

    • Main Results:

    • Demonstrated that conductance-based network models can be accurately represented by rate models under low synchrony conditions.
    • Successfully mapped rate model parameters to biophysical parameters of conductance-based models.
    • Developed a dynamic rate model capable of approximating the firing-rate response to time-dependent inputs.

    Conclusions:

    • This study bridges the gap between biophysically detailed and simplified neuronal network models.
    • The derived mapping enhances the interpretability and experimental testability of neuronal population dynamics.
    • The developed dynamic rate model offers a powerful tool for analyzing complex network responses in neuroscience.