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

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

507
Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
507
Neuronal Communication01:28

Neuronal Communication

5.6K
Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
5.6K
The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

3.2K
A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential....
3.2K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

335
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
335
Neural Circuits01:25

Neural Circuits

3.0K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
3.0K
Diffusion01:12

Diffusion

176.7K
Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
176.7K

You might also read

Related Articles

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

Sort by
Same author

Sampling times influence the estimate of parameters in the Weibull dissolution model.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences·2015
Same author

On the estimate of the rate constant in the homogeneous dissolution model.

Drug development and industrial pharmacy·2012
Same author

Homogeneous diffusion layer model of dissolution incorporating the initial transient phase.

International journal of pharmaceutics·2011
Same author

Random effects in drug dissolution.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences·2010
Same journal

Modeling the impact of budget limitation on the screening and treatment pathway of HPV-induced precancerous cervical lesions.

Mathematical biosciences and engineering : MBE·2026
Same journal

Modeling the effects of trait-mediated dispersal on coexistence of two species: Competition and non-consumptive predator-prey.

Mathematical biosciences and engineering : MBE·2026
Same journal

A close look at the viral reduction rate in target cell limited models.

Mathematical biosciences and engineering : MBE·2026
Same journal

A stochastic agent-based model for simulating tumor-immune dynamics and evaluating therapeutic strategies.

Mathematical biosciences and engineering : MBE·2026
Same journal

Addressing domain shift via imbalance-aware domain adaptation in embryo development assessment.

Mathematical biosciences and engineering : MBE·2026
Same journal

Effect of drug resistance on an HIV epidemic in heterogeneous populations.

Mathematical biosciences and engineering : MBE·2026
See all related articles

Related Experiment Video

Updated: May 5, 2026

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

2.3K

Diffusion approximation of neuronal models revisited.

Jakub Cupera1

  • 1Institute of Physiology, Academy of Sciences of the Czech Republic, Videnska 1083, 142 20 Prague 4, Czech Republic. xcupera@mail.muni.cz.

Mathematical Biosciences and Engineering : MBE
|November 20, 2013
PubMed
Summary
This summary is machine-generated.

This study compares diffusion approximations for leaky integrate-and-fire neuronal models. The best approximation for neuronal firing depends on the properties of random postsynaptic potentials.

More Related Videos

3D Modeling of Dendritic Spines with Synaptic Plasticity
07:13

3D Modeling of Dendritic Spines with Synaptic Plasticity

Published on: May 18, 2020

6.7K
Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism
08:44

Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism

Published on: October 17, 2025

861

Related Experiment Videos

Last Updated: May 5, 2026

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

2.3K
3D Modeling of Dendritic Spines with Synaptic Plasticity
07:13

3D Modeling of Dendritic Spines with Synaptic Plasticity

Published on: May 18, 2020

6.7K
Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism
08:44

Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism

Published on: October 17, 2025

861

Area of Science:

  • Computational neuroscience
  • Mathematical modeling of neural systems

Background:

  • Leaky integrate-and-fire (LIF) neuronal models are fundamental tools in computational neuroscience.
  • These models often employ diffusion approximations to simplify complex dynamics, particularly concerning postsynaptic potentials (PSPs).
  • The accuracy of these approximations is crucial for reliable simulations of neural activity.

Purpose of the Study:

  • To numerically compare the probability distributions of first-passage times between the original LIF model and its various diffusion approximations.
  • To identify the most suitable diffusion approximation for LIF neuronal models with reversal potentials.
  • To analyze the impact of postsynaptic potential amplitude properties on approximation accuracy.

Main Methods:

  • Numerical simulation of the original leaky integrate-and-fire model with reversal potentials.
  • Implementation and simulation of various diffusion approximations for the LIF model.
  • Comparison of first-passage time distributions between the exact model and its approximations.
  • Analysis of random postsynaptic potential amplitude distributions.

Main Results:

  • Significant differences were observed in the first-passage time distributions among the diffusion approximations and the original model.
  • The suitability of each diffusion approximation was found to be dependent on the specific characteristics of the postsynaptic potential amplitudes.
  • A simple example demonstrated a direct correlation between the quality of the approximation and the properties of the random PSP amplitudes.

Conclusions:

  • No single diffusion approximation is universally superior for all leaky integrate-and-fire models with reversal potentials.
  • Accurate modeling of postsynaptic potential amplitude statistics is critical for selecting appropriate diffusion approximations.
  • Further research into the properties of PSPs can lead to more refined and accurate neuronal simulations.