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

Simulation methods in neuronal modelling

M T Giraudo1, L Sacerdote

  • 1Department of Mathematics, University of Torino, Italy.

Bio Systems
|January 14, 1999
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

Comparison of CT and chemical-shift MRI for differentiating thymoma from non-thymomatous conditions in myasthenia gravis: value of qualitative and quantitative assessment.

Clinical radiology·2016
Same author

On the classification of experimental data modeled via a stochastic leaky integrate and fire model through boundary values.

Bulletin of mathematical biology·2006
Same author

New parameter relationships determined via stochastic ordering for spike activity in a reversal potential neural model.

Bio Systems·2001
Same author

A qualitative comparison of some diffusion models for neural activity via stochastic ordering.

Biological cybernetics·2000
Same author

Jump-diffusion processes as models for neuronal activity.

Bio Systems·1997
Same author

On the comparison of Feller and Ornstein-Uhlenbeck models for neural activity.

Biological cybernetics·1995
Same journal

Ruliological Resilience: Pattern Restoration and Robustness in Wolfram Patterns. A Basis for Regeneration, Not Just in Cone Shells?

Bio Systems·2026
Same journal

The quantum-to-classical transducer: A thermodynamic and quantum mechanical framework for the emergence of bioenergetics.

Bio Systems·2026
Same journal

Forward-backward gene expression binarization for boolean state inference over a known regulatory network.

Bio Systems·2026
Same journal

Partial-label metric ceilings for evaluating gene regulatory networks inferred from single-cell foundation models.

Bio Systems·2026
Same journal

The impedance mismatch theory: A non-equilibrium thermodynamic framework for a shared energetic stress pathway in neurodegeneration.

Bio Systems·2026
Same journal

Immune signal-status misclassification: A theoretical framework for biological status assignment and failed status resolution.

Bio Systems·2026
See all related articles

Simulating diffusion processes for neural firing times can overestimate results. This study introduces a refined simulation technique to accurately model spiking activity and firing time distributions, improving upon existing methods.

Area of Science:

  • Computational Neuroscience
  • Mathematical Biology
  • Stochastic Processes

Background:

  • Interspike distributions are often modeled using first-passage-time distributions of diffusion processes.
  • Analytical solutions for first-passage-time problems are mathematically challenging, necessitating simulation methods.
  • Existing simulation techniques may introduce overestimations in first-passage-time calculations.

Purpose of the Study:

  • To identify potential overestimations in first-passage-time simulations for diffusion processes.
  • To propose an improved simulation technique for accurate estimation of firing times and their distributions.
  • To apply the refined simulation method to model neural spiking activity with time-varying thresholds.

Main Methods:

  • Investigated first-passage-time problems in diffusion processes.

Related Experiment Videos

  • Developed and validated a novel simulation technique to address overestimations.
  • Applied the simulation algorithm to a diffusion process with a time-varying threshold.

    • Main Results:

    • Pinpointed sources of overestimation in standard first-passage-time simulations.
    • Demonstrated the accuracy of the proposed simulation method against known numerical evaluations.
    • Successfully modeled spiking activity using the improved simulation technique.

    Conclusions:

    • The proposed simulation method provides a more accurate approach to studying diffusion processes for neural modeling.
    • This technique is valuable for analyzing firing times and distributions in computational neuroscience.
    • The method is effective even when dealing with complex, time-varying boundaries.