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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

295
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
295
Propagation of Action Potentials01:23

Propagation of Action Potentials

8.3K
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...
8.3K
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

3.6K
Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
3.6K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

198
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...
198
Neural Circuits01:25

Neural Circuits

2.4K
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...
2.4K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

402
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
402

You might also read

Related Articles

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

Sort by
Same author

Single-cell transcriptomic analysis reveals dynamic changes in the microenvironment of nasopharyngeal carcinoma during metastatic progression.

Computational biology and chemistry·2026
Same author

Does the Apple airpods pro 2 hearing aid feature meet prescribed targets for standardized audiograms?

International journal of audiology·2026
Same author

Longitudinal Association Between Possible Sarcopenia and Stroke Under the AWGS 2025 Criteria: A Nationwide Prospective Cohort Study With a 9-Year Follow-Up.

Geriatrics & gerontology international·2026
Same author

How Speaker Configuration of Recorded Media Affects Subjective Ratings for Listeners With and Without Hearing Loss.

Journal of speech, language, and hearing research : JSLHR·2026
Same author

HSP47 inhibitor Col003 attenuates thromboinflammation after cerebral ischemia-reperfusion by suppressing GPVI-mediated CD84 shedding in platelets.

Molecular immunology·2026
Same author

Transboundary algal migration-induced systemic environmental risk in the Yangtze River Basin, China.

Fundamental research·2026

Related Experiment Video

Updated: Dec 5, 2025

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

949

Variational mean-field theory for training restricted Boltzmann machines with binary synapses.

Haiping Huang1

  • 1PMI Laboratory, School of Physics, Sun Yat-sen University, Guangzhou 510275, People's Republic of China.

Physical Review. E
|October 20, 2020
PubMed
Summary

This study introduces a new variational mean-field theory for unsupervised learning, unifying sensory inputs, synapses, and neural activity. The framework enhances artificial neural networks by extracting statistical regularities from raw data.

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

10.6K
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

2.1K

Related Experiment Videos

Last Updated: Dec 5, 2025

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

949
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

10.6K
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

2.1K

Area of Science:

  • Computational neuroscience
  • Machine learning theory

Background:

  • Unsupervised learning is crucial for the brain and artificial neural networks.
  • A unified theory for sensory inputs, synapses, and neural activity is missing.
  • Discrete synapses and complex interactions pose computational challenges.

Purpose of the Study:

  • To develop a unified theoretical framework for unsupervised learning.
  • To address the computational obstacles in modeling sensory inputs, synapses, and neural activity.
  • To provide insights into how data, synapses, and neural activity interact for learning.

Main Methods:

  • Proposed a variational mean-field theory considering synaptic weight distribution.
  • Decomposed unsupervised learning into maximization and expectation steps.
  • Utilized gradient ascent and message passing on a dual neural network.

Main Results:

  • The framework successfully integrates data, synapses, and neural activity.
  • Demonstrated insights into extracting statistical regularities from sensory inputs.
  • Verified the variational framework in restricted Boltzmann machines.

Conclusions:

  • The proposed theory offers a unified approach to unsupervised learning.
  • Provides a computational framework for understanding neural learning mechanisms.
  • Applicable to both biological and artificial neural systems for data analysis.