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

Convolution Properties II01:17

Convolution Properties II

623
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
623
Convolution Properties I01:20

Convolution Properties I

640
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
640
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

1.0K
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
1.0K
Vision01:24

Vision

60.8K
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.
60.8K
Visual System01:26

Visual System

2.1K
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...
2.1K
Deconvolution01:20

Deconvolution

647
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
647

You might also read

Related Articles

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

Sort by
Same author

An all-optical signal processor enabling terabit-per-second real-time equalization.

Science (New York, N.Y.)·2026
Same author

Femtojoule optical Kerr switching with milliwatt-peak-power in silicon-organic hybrid nanocavity.

Nature communications·2026
Same author

Programmable Three-dimensional Photonic Neural Network Chip.

Nature communications·2026
Same author

Ultrafast avalanche photodiode exceeding 100 GHz bandwidth.

Nature communications·2025
Same author

Quantum light sources with configurable lifetime leveraging parity-time symmetry.

Nature communications·2025
Same author

Photonic edge intelligence chip for multi-modal sensing, inference and learning.

Nature communications·2025
Same journal

Erratum for the Research Article "Assessing the health risks of rice cadmium content standards in China" by H. Chu <i>et al</i>.

Science advances·2026
Same journal

Erratum for the Research Article "Developmental regulation of Erk signaling by mitotic kinases" by F. Chen <i>et al</i>.

Science advances·2026
Same journal

Magnetically levitated metasurface enabling tangible and bidirectional human-machine interaction.

Science advances·2026
Same journal

A general photoinduced manganese-catalyzed platform for the sequential difunctionalization of [1.1.1]propellane.

Science advances·2026
Same journal

Turning sound and force into light with AlN:Mn<sup>2+</sup> mechanoluminescence.

Science advances·2026
Same journal

Extreme dominance of Earth-origin heavy ions in the intense ring current near the Earth during the May 2024 super geomagnetic storm.

Science advances·2026
See all related articles
  1. Home
  3. Optical Logic Convolutional Neural Network.
  1. Home
  3. Optical Logic Convolutional Neural Network.

Related Experiment Video

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.1K

Optical logic convolutional neural network.

Wenkai Zhang1, Jingcheng Li1, Shiji Zhang1

  • 1Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, 430074 Wuhan, China.

Science Advances
|February 27, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Researchers developed an optical logic convolutional neural network (OLCNN) for AI tasks. This novel approach enables high-speed, energy-efficient optical computing for pattern recognition and image analysis.

More Related Videos

Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT
12:22

Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT

Published on: August 4, 2018

9.0K

Related Experiment Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.1K
Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT
12:22

Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT

Published on: August 4, 2018

9.0K

Area of Science:

  • Optical Computing
  • Artificial Intelligence
  • Machine Learning Hardware

Background:

  • Optical computing offers high-speed potential but faces challenges with analog methods and digital configurations.
  • Current optical digital computing lacks flexibility for applications like AI inference.
  • Environmental perturbations and reliance on converters limit optical analog computing.

Purpose of the Study:

  • To introduce and demonstrate an optical logic convolutional neural network (OLCNN) for efficient AI computation.
  • To overcome limitations of existing optical computing paradigms for AI tasks.
  • To pioneer a logic-driven approach for optical hardware in artificial intelligence.

Main Methods:

  • Proposed and demonstrated an optical logic convolutional neural network (OLCNN) architecture.
  • Implemented optical logic convolutional operators (OLCOs) of varying sizes (1x3, 2x2, 3x3).
  • Validated OLCOs for pattern generation, image edge extraction, and MNIST dataset classification.

    • Main Results:

    • Achieved high-speed optical computing at 20 Gbit/s with a 1x3 OLCO.
    • Successfully performed image edge extraction using a 2x2 OLCO.
    • Attained 95.1% average test accuracy for four-class classification on MNIST using a 3x3 OLCO within an OLCNN.

    Conclusions:

    • The proposed OLCNN offers a high-speed, energy-efficient solution for AI hardware.
    • Synergizing optical logic devices with neural networks creates a new paradigm for optical computing.
    • This logic-driven approach advances the development of optical hardware for artificial intelligence applications.