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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.

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Related Experiment Video

Updated: Jun 20, 2026

Preparation of Liquid Crystal Networks for Macroscopic Oscillatory Motion Induced by Light
07:56

Preparation of Liquid Crystal Networks for Macroscopic Oscillatory Motion Induced by Light

Published on: September 20, 2017

Optical neural network with pocket-sized liquid-crystal televisions.

F T Yu, T Lu, X Yang

    Optics Letters
    |September 22, 2009
    PubMed
    Summary

    A compact optical neural network was built using high-resolution liquid-crystal televisions. This research details the system design and experimental results of this novel liquid-crystal television neural network.

    Area of Science:

    • Optics
    • Computer Science
    • Artificial Intelligence

    Background:

    • Optical neural networks offer potential advantages in speed and parallelism over electronic counterparts.
    • Liquid-crystal televisions (LCTVs) provide a low-cost, high-resolution platform for implementing optical systems.

    Purpose of the Study:

    • To construct a compact optical neural network utilizing high-resolution LCTVs.
    • To investigate the system design considerations for such a device.
    • To experimentally demonstrate the functionality of the LCTV-based neural network.

    Main Methods:

    • Development of a compact optical system architecture.
    • Integration of high-resolution liquid-crystal televisions as key components for network processing.
    • Experimental setup for demonstrating the network's operational capabilities.

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    Last Updated: Jun 20, 2026

    Preparation of Liquid Crystal Networks for Macroscopic Oscillatory Motion Induced by Light
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    Published on: September 20, 2017

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    Main Results:

    • Successful construction of a compact optical neural network.
    • Demonstration of the system's ability to perform neural network functions.
    • Validation of the LCTV-based approach for optical computing.

    Conclusions:

    • The study confirms the feasibility of using high-resolution LCTVs for compact optical neural networks.
    • The reported design considerations and experimental results provide a foundation for future development in optical artificial intelligence hardware.