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

Propagation of Action Potentials01:23

Propagation of Action Potentials

6.0K
The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
6.0K
Action Potential01:31

Action Potential

8.0K
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...
8.0K
Action Potentials01:41

Action Potentials

131.6K
Overview
131.6K
Graded Potential01:19

Graded Potential

4.1K
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...
4.1K
Action Potential: Phases of Stimulation01:28

Action Potential: Phases of Stimulation

6.1K
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...
6.1K
Postsynaptic Potential (PSP)01:32

Postsynaptic Potential (PSP)

2.8K
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...
2.8K

You might also read

Related Articles

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

Sort by
Same author

Atmospheric Oxidation Kinetics of Monochloramine by Hydroxyl Radical, Carbonyl Oxide, and Sulfur Trioxide Catalyzed by Water.

The journal of physical chemistry. A·2026
Same author

Reinventing Density Functional Theory with Machine Learning on Integral Features.

Journal of chemical theory and computation·2026
Same author

Nonadiabatic dynamics starting from reactant and transition state in luminol chemiluminescence.

The Journal of chemical physics·2026
Same author

PpF: a density functional fine-tuned for noncovalent interactions of protein and peptide residues.

Chemical science·2026
Same author

Dynamics of Electronically Inelastic and Reactive Collisions of O(<sup>1</sup>D) with N<sub>2</sub> Based on Machine-Learned Coupled Potential Energy Surfaces.

Journal of chemical theory and computation·2026
Same author

Macropa Scaffold Expansion for Actinium-225 Chelation: A Synthetic Strategy, Labeling Kinetics, and Theoretical Calculations.

Inorganic chemistry·2026

Related Experiment Video

Updated: Jul 26, 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

Parametrically Managed Activation Function for Fitting a Neural Network Potential with Physical Behavior Enforced by

Farideh Badichi Akher1, Yinan Shu1, Zoltan Varga1

  • 1Department of Chemistry and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States.

The Journal of Physical Chemistry. A
|June 12, 2023
PubMed
Summary

This study introduces a novel activation function for neural networks, enhancing machine-learned potentials by enforcing physical constraints like vanishing interactions. This improves accuracy in sparse data regions, exemplified by new ozone potential energy surfaces.

More Related Videos

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
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

1.8K

Related Experiment Videos

Last Updated: Jul 26, 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
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
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

1.8K

Area of Science:

  • Computational Chemistry
  • Machine Learning
  • Quantum Chemistry

Background:

  • Machine-learned potential energy surfaces (PES) are increasingly used but struggle with accuracy in data-sparse regions.
  • Existing methods often require specific functional forms or extensive data for reliable extrapolation.
  • Incorporating physical knowledge into machine learning models is crucial for improved performance.

Purpose of the Study:

  • To develop a method for integrating human intelligence, specifically physical constraints, into machine-learned potentials.
  • To address the unreliability of neural network outputs in regions with limited training data.
  • To present a novel activation function that enforces low-dimensional constraints within neural networks.

Main Methods:

  • Introduced a new type of activation function for feedforward neural networks that parametrically depends on all input variables.
  • Demonstrated the ability of this activation function to enforce physical constraints, such as interaction potentials vanishing at large subsystem separations.
  • Applied the method to generate improved potential energy surfaces for the 14 lowest 3A' states of ozone (O3).
  • Presented a generalized method, parametrically managed diabatization by deep neural network (PM-DDNN), as an advancement over previous techniques.

Main Results:

  • Successfully enforced the physically realistic condition that interaction potentials approach zero at large subsystem separations without specific functional forms or additional data.
  • Generated an improved set of potential energy surfaces for ozone, demonstrating the practical application of the method.
  • Showcased the generality of the approach for incorporating various low-dimensional or lower-level knowledge into machine-learned potentials.

Conclusions:

  • The developed activation function effectively integrates physical knowledge into machine-learned potentials, enhancing their reliability and extrapolation capabilities.
  • The method provides a convenient way to improve the accuracy of neural network-based PES, particularly in regions with sparse data.
  • The generalized PM-DDNN method offers further advancements in machine learning for quantum chemistry applications.