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

Modeling with Differential Equations01:25

Modeling with Differential Equations

117
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
117
The Nernst Equation02:59

The Nernst Equation

47.5K
Nonstandard Reaction Conditions
The interconnection between standard cell potentials and various thermodynamic parameters such as the standard free energy change ΔG° and equilibrium constant K has been previously explored. For example, a redox reaction involving zinc(II) and tin(II) ions at 1 M concentration with Eºcell = +0.291 V and ΔG° = −56.2 kJ is spontaneous.
47.5K
Introduction to Differential Equations01:20

Introduction to Differential Equations

168
A differential equation is a mathematical expression that establishes a relationship between a function and its derivatives. These equations are fundamental in modeling dynamic systems across various fields of science and engineering. The order of a differential equation is defined by the highest order derivative present in the equation. A first-order differential equation includes only the first derivative, while a second-order differential equation includes up to the second derivative of the...
168
The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

4.0K
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....
4.0K
Neuronal Communication01:28

Neuronal Communication

3.8K
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...
3.8K
Neural Circuits01:25

Neural Circuits

2.9K
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...
2.9K

You might also read

Related Articles

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

Sort by
Same author

Capillary Density and Neuronal Homeostasis in Human Primary Visual Cortex.

Microcirculation (New York, N.Y. : 1994)·2026
Same author

All Models are Wrong, Some are Annotated: Automating Metadata in Biomedical Repositories.

bioRxiv : the preprint server for biology·2026
Same author

Stochasticity in action potential backpropagation: consequences for neuronal computation.

Frontiers in cellular neuroscience·2026
Same author

From FAIR to CURE: guidelines for computational models of biological systems.

NPJ systems biology and applications·2026
Same author

The quantitative trait locus stiff2 controls stalk bending strength and root architecture in maize.

Journal of genetics and genomics = Yi chuan xue bao·2025
Same author

Computer models predict differential dendritic vulnerability with ischemia and spreading depression.

bioRxiv : the preprint server for biology·2025
Same journal

Indemics: An Interactive High-Performance Computing Framework for Data Intensive Epidemic Modeling.

ACM transactions on modeling and computer simulation : a publication of the Association for Computing Machinery·2014
Same journal

Massive parallelization of serial inference algorithms for a complex generalized linear model.

ACM transactions on modeling and computer simulation : a publication of the Association for Computing Machinery·2014
Same journal

A Distributed Platform for Global-Scale Agent-Based Models of Disease Transmission.

ACM transactions on modeling and computer simulation : a publication of the Association for Computing Machinery·2014
See all related articles

Related Experiment Video

Updated: Feb 22, 2026

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

3D Modeling of Dendritic Spines with Synaptic Plasticity

Published on: May 18, 2020

7.4K

Multithreaded Stochastic PDES for Reactions and Diffusions in Neurons.

Zhongwei Lin1, Carl Tropper2, Robert A Mcdougal3

  • 1State Key Laboratory of High Performance Computing and College of Information System and Management, National University of Defense Technology, Changsha, Hunan, China; zwlin@nudt.edu.cn.

ACM Transactions on Modeling and Computer Simulation : a Publication of the Association for Computing Machinery
|September 26, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces Neuron Time Warp-Multi Thread (NTW-MT), a novel parallel simulator for accurate cell molecular dynamics. NTW-MT enhances computational efficiency for simulating neuronal chemical reactions.

More Related Videos

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

2.2K
Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions
07:38

Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions

Published on: June 7, 2024

2.4K

Related Experiment Videos

Last Updated: Feb 22, 2026

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

3D Modeling of Dendritic Spines with Synaptic Plasticity

Published on: May 18, 2020

7.4K
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

2.2K
Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions
07:38

Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions

Published on: June 7, 2024

2.4K

Area of Science:

  • Computational neuroscience
  • Biophysical modeling
  • Stochastic simulation

Background:

  • Cellular processes involve stochastic behavior at low molecular counts.
  • Accurate simulation of molecular dynamics requires scalable stochastic reaction-diffusion simulators.

Purpose of the Study:

  • To introduce Neuron Time Warp-Multi Thread (NTW-MT), the first parallel discrete event simulator for stochastic neuronal chemical reactions.
  • To optimize simulation performance on multi-core architectures for neuronal models.

Main Methods:

  • Developed NTW-MT, an optimistic, thread-based parallel discrete event simulator.
  • Utilized a multi-level queue for event sets and single roll-back messages to reduce overhead.
  • Implemented asynchronous Global Virtual Time computation and optimized memory management for cache locality.

Main Results:

  • Verified the simulator on a calcium buffer model.
  • Demonstrated superior performance of NTW-MT compared to process-based and single-queue threaded simulators on a calcium wave model.
  • Showcased scalability on larger and more detailed calcium-induced calcium release (CICR) models.

Conclusions:

  • NTW-MT offers superior performance and scalability for stochastic reaction-diffusion simulations in neurons.
  • The simulator effectively models molecular dynamics in cellular environments.
  • NTW-MT represents a significant advancement in computational neuroscience tools.