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

Graded Potential01:19

Graded Potential

5.3K
Graded potentials are localized fluctuations in the cell membrane's electrical charge, commonly found in the dendrites of neurons. The magnitude of these potential changes depends on the strength of the initiating stimulus. In a membrane at its resting potential, a graded potential signifies a voltage shift either above -70 mV or below -70 mV.
Graded potentials fall into two categories: depolarizing and hyperpolarizing. Depolarizing graded potentials typically occur when sodium (Na+) or...
5.3K
Postsynaptic Potential (PSP)01:32

Postsynaptic Potential (PSP)

3.6K
Postsynaptic potential (PSP) refers to a change in the electrical potential of a neuron when neurotransmitters released by presynaptic neurons bind to postsynaptic receptors. This potential can either be excitatory, leading to depolarization and ultimately action potential generation, or inhibitory, leading to hyperpolarization and suppression of the postsynaptic neuron.
There are two types of receptors: ionotropic and metabotropic.
The ionotropic receptor is the membrane protein that has an...
3.6K
Action Potentials01:41

Action Potentials

134.5K
Overview
134.5K
Action Potential: Phases of Stimulation01:28

Action Potential: Phases of Stimulation

8.1K
The action potential is a complex electrical event that occurs in excitable cells, such as neurons and muscle cells. It consists of several distinct phases, each with specific characteristics.
Resting Phase:
In this phase, the cell's membrane is at its resting potential, typically around -70 millivolts (mV) for neurons. Inside the cell, there is a higher concentration of potassium ions (K+) and a lower concentration of sodium ions (Na+). Voltage-gated sodium channels are closed, and...
8.1K
Neural Circuits01:25

Neural Circuits

1.8K
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...
1.8K
Action Potential01:14

Action Potential

8.9K
Neurons communicate by firing action potentials—the electrochemical signal that is propagated along the axon. The signal results in the release of neurotransmitters at axon terminals, thereby transmitting information to the nervous system. An action potential is a specific "all-or-none" change in membrane potential that results in a rapid spike in voltage.
Membrane potential in neurons
Neurons typically have a resting membrane potential of about -70 millivolts (mV). When they receive...
8.9K

You might also read

Related Articles

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

Sort by
Same author

Molecular Dynamics Simulations of γ-Belite(010)-Water Interfaces With High-Dimensional Neural Network Potentials.

Chemistry (Weinheim an der Bergstrasse, Germany)·2026
Same author

Long-Range Interactions in High-Dimensional Neural Network Potentials: A Benchmark Study for Small Organic Molecules.

The journal of physical chemistry. B·2025
Same author

Computation of the heat capacity of water from first principles.

The Journal of chemical physics·2025
Same author

Superionic Surface Li-Ion Transport in Carbonaceous Materials.

Nano letters·2025
Same author

Free energy profiles for chemical reactions in solution from high-dimensional neural network potentials: The case of the Strecker synthesis.

The Journal of chemical physics·2025
Same author

Iterative charge equilibration for fourth-generation high-dimensional neural network potentials.

The Journal of chemical physics·2025
Same journal

Coadsorption of Atmospheric Surface-Active Organics at the Aqueous Interface: A Molecular Dynamics Study.

Annual review of physical chemistry·2026
Same journal

Control of Chemical Reactions in Radiofrequency Ion Traps.

Annual review of physical chemistry·2026
Same journal

Theories of Chiral-Induced Spin Selectivity: A Pedagogical Overview.

Annual review of physical chemistry·2026
Same journal

Quantum Computing Beyond Ground-State Electronic Structure: A Review of Progress Toward Quantum Chemistry Out of the Ground State.

Annual review of physical chemistry·2026
Same journal

First-Principles Simulations of Chemical Transformations in Nanoporous Materials and Industrial Catalysts.

Annual review of physical chemistry·2026
Same journal

Structure and Dynamics of Microhydrated Complexes Revealed with Rotational Spectroscopy.

Annual review of physical chemistry·2026
See all related articles

Related Experiment Video

Updated: Oct 8, 2025

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

1.9K

Neural Network Potentials: A Concise Overview of Methods.

Emir Kocer1, Tsz Wai Ko1, Jörg Behler1

  • 1Institut für Physikalische Chemie, Theoretische Chemie, Universität Göttingen, Göttingen, Germany; email: ekocer@uni-goettingen.de, tko@uni-goettingen.de, joerg.behler@uni-goettingen.de.

Annual Review of Physical Chemistry
|January 4, 2022
PubMed
Summary
This summary is machine-generated.

Machine learning potentials (MLPs) now enable large-scale atomistic simulations in chemistry and materials science. This review focuses on artificial neural network-based MLPs, discussing their differences and challenges.

Keywords:
atomistic simulationsmachine learningmolecular dynamicsneural network potentialspotential energy surfaces

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

10.5K
Concurrent Recording of Co-localized Electroencephalography and Local Field Potential in Rodent
08:31

Concurrent Recording of Co-localized Electroencephalography and Local Field Potential in Rodent

Published on: November 30, 2017

12.5K

Related Experiment Videos

Last Updated: Oct 8, 2025

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

1.9K
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

10.5K
Concurrent Recording of Co-localized Electroencephalography and Local Field Potential in Rodent
08:31

Concurrent Recording of Co-localized Electroencephalography and Local Field Potential in Rodent

Published on: November 30, 2017

12.5K

Area of Science:

  • Computational Chemistry
  • Materials Science
  • Physics

Background:

  • Machine learning potentials (MLPs) have matured significantly over the last two decades.
  • MLPs are now applicable to large-scale atomistic simulations across various scientific disciplines.
  • Artificial neural networks (ANNs) are a key machine learning algorithm used in constructing MLPs.

Purpose of the Study:

  • To review MLPs based on artificial neural networks.
  • To highlight conceptual differences among various MLPs.
  • To discuss open challenges in the field of MLPs.

Main Methods:

  • Focus on MLPs that use ANNs to map atomic structure to potential energy.
  • Discuss differences concerning system dimensionality and electrostatic interactions.
  • Analyze the use of predefined versus learnable descriptors for atomic structures.

Main Results:

  • ANN-based MLPs are a significant group with diverse implementations.
  • Key differences exist in handling system dimensionality, long-range electrostatics, and charge transfer.
  • Descriptor type (predefined vs. learnable) is a crucial distinguishing factor.

Conclusions:

  • MLPs, particularly those using ANNs, are powerful tools for atomistic simulations.
  • Understanding conceptual differences is vital for selecting and developing appropriate MLPs.
  • Addressing open challenges will further advance the capabilities of MLPs in scientific research.