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

52.9K
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.
52.9K
Parallel Processing01:20

Parallel Processing

144
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
144
Visual System01:26

Visual System

485
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...
485

You might also read

Related Articles

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

Sort by
Same author

Decoupling Optical Functions via Ratio-Tunable Conjugated Copolymers with Hydrogen-Bond-Enforced Rigidity for Quenching-Resistant NIR-II Phototheranostics.

ACS nano·2026
Same author

pH and polyphenol-controlled release dual-responsive intelligent hydrogel absorbent pad strategy: methylcellulose‑sodium alginate and anthocyanin@MOFs for chilled pork preservation.

Food research international (Ottawa, Ont.)·2026
Same author

Unmodified orientable osteoclastic cytomembrane bionic fluorescent magnetic nanocarbons as high-efficiency multifunctional platforms for antiresorptive compound discovery.

Colloids and surfaces. B, Biointerfaces·2026
Same author

Optical access of spin-polarized excited states in Cs(PbMgZnCd)Br<sub>3</sub> nanocrystals.

Nature communications·2026
Same author

Excited-state magneto-optical effects in organic semiconductors.

Chemical communications (Cambridge, England)·2026
Same author

Coupling Hydrological Connectivity with Environmental Thresholds for Targeted Legacy Phosphorus Management in Subtropical Watersheds.

Environmental science & technology·2026

Related Experiment Video

Updated: May 30, 2025

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution
08:48

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Published on: September 5, 2012

11.8K

All-optical perception based on partially coherent optical neural networks.

Rui Chen, Yijun Ma, Chuang Zhang

    Optics Express
    |January 29, 2025
    PubMed
    Summary

    This study introduces a partially coherent optical neural network (PCONN) that bypasses the need for coherent light sources. This innovation enables faster, more energy-efficient optical computing for object detection using natural light.

    More Related Videos

    How to Build a Dichoptic Presentation System That Includes an Eye Tracker
    05:48

    How to Build a Dichoptic Presentation System That Includes an Eye Tracker

    Published on: September 6, 2017

    8.4K
    Using Optical Coherence Tomography and Optokinetic Response As Structural and Functional Visual System Readouts in Mice and Rats
    07:08

    Using Optical Coherence Tomography and Optokinetic Response As Structural and Functional Visual System Readouts in Mice and Rats

    Published on: January 10, 2019

    9.9K

    Related Experiment Videos

    Last Updated: May 30, 2025

    Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution
    08:48

    Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

    Published on: September 5, 2012

    11.8K
    How to Build a Dichoptic Presentation System That Includes an Eye Tracker
    05:48

    How to Build a Dichoptic Presentation System That Includes an Eye Tracker

    Published on: September 6, 2017

    8.4K
    Using Optical Coherence Tomography and Optokinetic Response As Structural and Functional Visual System Readouts in Mice and Rats
    07:08

    Using Optical Coherence Tomography and Optokinetic Response As Structural and Functional Visual System Readouts in Mice and Rats

    Published on: January 10, 2019

    9.9K

    Area of Science:

    • Optoelectronics
    • Artificial Intelligence
    • Image Processing

    Background:

    • Coherent optical neural networks (CONN) require coherent light sources and electro-optic modulators, limiting their computational power and energy efficiency.
    • These limitations hinder the practical application of CONN in demanding tasks like object detection.

    Purpose of the Study:

    • To propose a novel partially coherent optical neural network (PCONN) transmission model.
    • To enable optical neural networks to operate without coherent light sources or active electro-optic modulation.
    • To enhance computational speed and energy efficiency for optical object detection.

    Main Methods:

    • Development of a PCONN transmission model utilizing mutual intensity modulation.
    • Direct computation and inference using natural light after simple filtering, eliminating the need for laser inputs and electro-optic modulators.
    • Simulation-based evaluation on benchmark datasets (MNIST, Fashion-MNIST, ISDD).

    Main Results:

    • Achieved classification accuracies of 96.80% (MNIST) and 86.77% (Fashion-MNIST).
    • Attained 94.69% accuracy in binary classification on the ISDD dataset.
    • Estimated 100x faster inference speed and 50x greater energy efficiency compared to traditional CONN.

    Conclusions:

    • The proposed PCONN model overcomes the limitations of coherent optical neural networks.
    • PCONN facilitates full optical perception from light acquisition to inference using natural light.
    • The model shows significant potential for practical object detection applications.