Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Compartment Models: Single-Compartment Model

Compartment Models: Single-Compartment Model

The single-compartment model serves as a simplified representation of the human body. This model assumes that the body functions as a single, well-mixed open compartment. When a drug is administered intravenously, it enters the body and quickly distributes uniformly. The drug then undergoes biotransformation and elimination, ultimately leaving the body. The volume of this compartment is referred to as the apparent volume of distribution into which the drug can uniformly distribute. In this...

Multicompartment Models: Overview

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...

Compartment Models: Two-Compartment Model

Compartment Models: Two-Compartment Model

The two-compartment model divides the body into central and peripheral compartments to account for varying blood perfusion rates among organs and tissues, affecting drug distribution. The central compartment includes blood and highly perfused tissues with rapid drug distribution, while the peripheral compartment contains tissues with slower drug distribution. After a single IV bolus dose, the drug concentration is high in plasma and low in tissues. The drug distribution between compartments...

Mechanistic Models: Overview of Compartment Models

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...

Two-Compartment Open Model: Overview

Two-Compartment Open Model: Overview

Multicompartmental models are crucial tools in pharmacokinetics, providing a framework to understand how drugs move within the body. The two-compartment model is a crucial subtype, segmenting the body into central and peripheral compartments. The central compartment represents areas with high blood flow, such as plasma and highly perfused organs like the kidneys and liver, while the peripheral compartment signifies tissues with lower blood flow, like adipose tissue and muscle tissue.
The...

Three-Compartment Open Model

Three-Compartment Open Model

The three-compartment open model is a pharmacokinetic model used to describe the distribution and elimination of drugs following extravascular administration. It comprises a central compartment representing the plasma and two peripheral compartments. The highly perfused peripheral compartment represents organs and tissues with a rich blood supply, such as the liver, kidneys, and lungs. The scarcely perfused peripheral compartment represents tissues with lower blood supply, such as adipose...

You might also read

Related Articles

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

Sort by

Same author

The Neural Analysis Toolkit Unifies Semi-Analytical Techniques to Simplify, Understand, and Simulate Dendrites.

Neuroinformatics·2026

Same author

A Theoretical Comparison of Alternative Male Mating Strategies in Cephalopods and Fishes.

Bulletin of mathematical biology·2024

Same author

Conductance-based dendrites perform Bayes-optimal cue integration.

PLoS computational biology·2024

Same author

The energy challenges of artificial superintelligence.

Frontiers in artificial intelligence·2023

Same author

Collecting Marine Gnathiid Isopod Fish Parasites with Light Traps.

Journal of visualized experiments : JoVE·2023

Same author

NMDA-driven dendritic modulation enables multitask representation learning in hierarchical sensory processing pathways.

Proceedings of the National Academy of Sciences of the United States of America·2023

Search research articles

Related Experiment Video

Updated: May 7, 2026

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Published on: January 30, 2018

The Green's function formalism as a bridge between single- and multi-compartmental modeling.

Willem A M Wybo¹, Klaus M Stiefel, Benjamin Torben-Nielsen

¹Blue Brain Project, Brain Mind Institute, EPFL, Lausanne, Switzerland, willem.wybo@epfl.ch.

Biological Cybernetics

|September 17, 2013

Summary

This study introduces a novel artificial synapse model that mimics dendritic processing. This allows simplified point-neuron models to achieve complex neuronal computations, enhancing brain network simulations.

More Related Videos

Dual-Color Fluorescence Cross-Correlation Spectroscopy to Study Protein-Protein Interaction and Protein Dynamics in Live Cells

Dual-Color Fluorescence Cross-Correlation Spectroscopy to Study Protein-Protein Interaction and Protein Dynamics in Live Cells

Published on: December 11, 2021

Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids

Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids

Published on: May 27, 2020

Related Experiment Videos

Last Updated: May 7, 2026

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Published on: January 30, 2018

Dual-Color Fluorescence Cross-Correlation Spectroscopy to Study Protein-Protein Interaction and Protein Dynamics in Live Cells

Dual-Color Fluorescence Cross-Correlation Spectroscopy to Study Protein-Protein Interaction and Protein Dynamics in Live Cells

Published on: December 11, 2021

Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids

Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids

Published on: May 27, 2020

Area of Science:

Computational neuroscience
Neuro-modeling

Background:

Neurons process information via dendritic integration.
Current brain network simulations often use simplified point-neurons, omitting dendritic morphology.
This simplification limits the accuracy of neuronal computation models.

Purpose of the Study:

To develop an artificial synapse model that simulates dendritic processing.
To enable point-neurons to perform computations previously requiring complex multi-compartmental models.
To improve the efficiency and accuracy of brain network simulations.

Main Methods:

Utilized an analytic solution of the cable equation to model dendritic integration.
Employed Green's function formalism for a closed-form cable equation.
Developed an artificial synapse incorporating these methods.

Main Results:

Point-neurons equipped with the new synapse model achieved results comparable to multi-compartmental models.
Demonstrated computational advantages for morphologically detailed neurons with few simulated synapses.
Successfully mimicked dendritic processing without explicit dendritic simulation.

Conclusions:

The proposed artificial synapse effectively integrates dendritic processing into point-neuron models.
This approach enhances the computational power of simplified neuronal models.
Offers a promising method for more accurate and efficient brain network simulations.