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

Visual System01:26

Visual System

Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
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.
Association Areas of the Cortex01:21

Association Areas of the Cortex

Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...

You might also read

Related Articles

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

Sort by
Same author

Real time optical edge enhancement using a Hughes liquid crystal light valve.

Applied optics·2010
Same author

Liquid crystal television spatial light modulators.

Applied optics·2010
Same author

Real-time optical holographic tracking of multiple objects.

Applied optics·2010
Same author

Retinal model with adaptive contrast sensitivity and resolution.

Applied optics·2010
Same author

Optimum correlation detection by prewhitening.

Applied optics·2010
Same author

Real-time white light spatial frequency and density pseudocolor encoder.

Applied optics·2010
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 9, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Optical implementation of a feature-based neural network with application to automatic target recognition.

T H Chao, W W Stoner

    Applied Optics
    |September 8, 2010
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an optical neural network using neocognitron principles for pattern recognition. The novel design achieves shift-invariant correlation, enabling effective space-object discrimination with high accuracy.

    Related Experiment Videos

    Last Updated: Jun 9, 2026

    End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
    03:31

    End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

    Published on: December 15, 2023

    Area of Science:

    • Computer Science
    • Optics
    • Artificial Intelligence

    Background:

    • The neocognitron paradigm provides a framework for hierarchical feature extraction in neural networks.
    • Optical implementations offer potential advantages in speed and parallelism for complex computations.

    Purpose of the Study:

    • To introduce a novel optical neural network architecture based on the neocognitron paradigm.
    • To demonstrate shift-invariant multichannel Fourier optical correlation for enhanced pattern recognition.
    • To achieve successful space-object discrimination using the developed optical neural network.

    Main Methods:

    • An optical neural network was designed incorporating the neocognitron paradigm.
    • Shift-invariant multichannel Fourier optical correlation was implemented within each processing layer.
    • Multilayer processing was achieved through iterative feedback and Fourier filter updates.
    • The neural network was trained with characteristic features for pattern recognition tasks.

    Main Results:

    • The optical neural network demonstrated successful pattern recognition capabilities.
    • Intraclass fault tolerance and interclass discrimination were achieved.
    • Experimental validation was performed on a two-layer network for space-object discrimination.

    Conclusions:

    • The proposed optical neural network architecture effectively performs pattern recognition.
    • The integration of shift-invariant Fourier optical correlation enhances discrimination capabilities.
    • The system shows promise for applications such as space-object identification.