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

Vision01:24

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
Neural Circuits01:25

Neural Circuits

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...
Observational Learning01:12

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning because...

You might also read

Related Articles

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

Sort by
Same author

[Efficacy of rituximab in patients with EB virus-related hemophagocytic syndrome caused by B lymphocyte infection].

Zhonghua yi xue za zhi·2026
Same author

Real-world 5-year outcomes with durvalumab after chemoradiotherapy in unresectable stage III NSCLC.

ESMO open·2026
Same author

[Investigation and analysis of wrist musculoskeletal disorders by ultrasound physicians].

Zhonghua lao dong wei sheng zhi ye bing za zhi = Zhonghua laodong weisheng zhiyebing zazhi = Chinese journal of industrial hygiene and occupational diseases·2025
Same author

The current research status of non-destructive testing technologies for egg quality: internal freshness - a review.

British poultry science·2025
Same author

Early-stage fertilised egg viability detection based on machine vision.

British poultry science·2025
Same author

[Comparative study with propensity score matching of gastrectomy versus total gastrectomy for the safety and prognosis of Siewert types Ⅱ and Ⅲ adenocarcinoma of the esophagogastric junction].

Zhonghua wei chang wai ke za zhi = Chinese journal of gastrointestinal surgery·2025
Same journal

Multifunctional reconfigurable terahertz metasurface based on vanadium dioxide phase transition: achieving broadband absorption and efficient polarization conversion.

Applied optics·2026
Same journal

High-Q-factor electromagnetically induced transparency utilizing quasi-bound states in the continuum in an all-dielectric terahertz metasurface.

Applied optics·2026
Same journal

Automated stitching interferometry for high-precision metrology of X-ray mirrors.

Applied optics·2026
Same journal

Experimental demonstration of an approach to designing a metal-dielectric DBR resonant cavity structure.

Applied optics·2026
Same journal

High-precision wavefront reconstruction from a single-shot interferogram using a physics-driven hybrid feature calibration network.

Applied optics·2026
Same journal

Ultra-high-Q Fano resonance based on coupled topological corner states in Kagome photonic crystals.

Applied optics·2026
See all related articles

Related Experiment Video

Updated: Jun 10, 2026

A Gaze-Contingent Display Framework for Perceptual Learning Research with Simulated Central Vision Loss
07:12

A Gaze-Contingent Display Framework for Perceptual Learning Research with Simulated Central Vision Loss

Published on: April 11, 2025

Local learning algorithm for optical neural networks.

Y Qiao, D Psaltis

    Applied Optics
    |August 21, 2010
    PubMed
    Summary
    This summary is machine-generated.

    A novel anti-Hebbian learning rule simplifies optical neural networks by avoiding backpropagation. This local learning algorithm effectively updates synaptic weights using only local information and a global error signal.

    Related Experiment Videos

    Last Updated: Jun 10, 2026

    A Gaze-Contingent Display Framework for Perceptual Learning Research with Simulated Central Vision Loss
    07:12

    A Gaze-Contingent Display Framework for Perceptual Learning Research with Simulated Central Vision Loss

    Published on: April 11, 2025

    Area of Science:

    • Artificial Intelligence
    • Optical Computing
    • Machine Learning

    Background:

    • Traditional neural network training relies on backpropagation, which is complex for optical implementations.
    • Optical neural networks offer potential for faster computation but face implementation challenges.

    Purpose of the Study:

    • To introduce a new local learning algorithm for two-layer optical neural networks.
    • To overcome the limitations of backpropagation in optical neural network training.

    Main Methods:

    • Developed an anti-Hebbian local learning rule.
    • The rule uses only local input, output, and a global error signal for weight updates.
    • Simulations were conducted to evaluate the algorithm's effectiveness.

    Main Results:

    • The proposed learning rule avoids the need for error backpropagation.
    • Synaptic weights are guaranteed to update in the error descent direction.
    • Simulations demonstrate computational effectiveness and simpler optical implementation.

    Conclusions:

    • The anti-Hebbian local learning algorithm is a viable and effective method for training optical neural networks.
    • This approach simplifies optical implementation and maintains computational efficiency.