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

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
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

81
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
81
Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs01:21

Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs

1.7K
The fundamental mathematical principles, such as calculus and graphs, play crucial roles in analyzing drug movement and determining pharmacokinetic parameters. Differential calculus examines rates of change and helps to determine the dissolution rate of drugs in biofluids, as well as how drug concentrations change over time. For instance, it can help calculate the rate of elimination of a drug from the body based on its concentration-time profile.
On the other hand, integral calculus focuses on...
1.7K
Neural Regulation01:37

Neural Regulation

39.5K
Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
39.5K

You might also read

Related Articles

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

Sort by
Same author

A Structured Computational Roadmap for Lipidomics in R: Reproducible Workflows from Raw Data to Functional Insight.

Metabolites·2026
Same author

CpGene: a web application for epigenetic signature identification from DNA methylation arrays.

Bioinformatics (Oxford, England)·2026
Same author

Merging multimodal digital biomarkers into "Digital Neuro Fingerprints" for precision neurology in dementias: the promise of the right treatment for the right patient at the right time in the age of AI.

Frontiers in digital health·2026
Same author

AI agents in Alzheimer's disease management: challenges and future directions.

Frontiers in aging neuroscience·2026
Same author

Web-Based Application for Hashimoto's Disease Prediction Based on Thyroid Hormone Levels and Machine Learning Analysis.

Advances in experimental medicine and biology·2026
Same author

Comprehensive Differential Gene Expression Analysis in Glioblastoma Using PyDESeq2: A Comparison with Normal Brain Tissue.

Advances in experimental medicine and biology·2026

Related Experiment Video

Updated: Jul 22, 2025

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

9.9K

Spiking Neural Networks and Mathematical Models.

Mirto M Gasparinatou1, Nikolaos Matzakos2, Panagiotis Vlamos3

  • 1Ionian University, Corfu, Greece. mgasparinatou@ionio.gr.

Advances in Experimental Medicine and Biology
|July 24, 2023
PubMed
Summary

This review compares four mathematical models of single neurons, essential for understanding neural networks in fields like medicine and pharmacology. It evaluates their biological accuracy, computational demands, and practical uses.

Keywords:
Hodgkin-HuxleyIzhikevichLeaky integrate and fireMathematical modelsMorris-LecarNeural networksSingle-compartment model

More Related Videos

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.4K
Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

7.1K

Related Experiment Videos

Last Updated: Jul 22, 2025

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

9.9K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.4K
Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

7.1K

Area of Science:

  • Computational neuroscience
  • Mathematical modeling in biology
  • Neuroscience and artificial intelligence

Background:

  • Neural networks are crucial in diverse scientific fields, including medicine, engineering, and pharmacology.
  • Understanding single neuron function is key to deciphering complex brain operations and neural network behavior.
  • Mathematical models simulating neuronal information transmission are vital tools for neuroscientists.

Approach:

  • This review critically examines four prominent single-compartment mathematical neuron models: Hodgkin-Huxley, Izhikevich, Leaky Integrate-and-Fire, and Morris-Lecar.
  • A comparative analysis is presented, focusing on biological plausibility, computational complexity, and diverse applications.
  • The evaluation is based on current scientific literature and modern research findings.

Key Points:

  • The Hodgkin-Huxley model offers high biological detail but is computationally intensive.
  • The Izhikevich model provides a balance between biological realism and computational efficiency.
  • Leaky Integrate-and-Fire and Morris-Lecar models are simpler, computationally faster, and suitable for large-scale network simulations.

Conclusions:

  • The choice of mathematical neuron model depends critically on the specific research question, balancing biological fidelity with computational resources.
  • Accurate modeling of individual neuron behavior is fundamental for advancing our understanding of neural computation and brain function.
  • This comparative review aids researchers in selecting appropriate models for simulating neural networks across various scientific disciplines.