Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Imaging Biological Samples with Optical Microscopy

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...

You might also read

Related Articles

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

Sort by

Same author

iPad Eye Tracking Reproduces Clinical Grade Oculomotor Differences in Parkinson's Disease.

NPJ digital medicine·2026

Same author

Foundry-Enabled Patterning of Diamond Quantum Microchiplets for Scalable Quantum Photonics.

Nano letters·2026

Same author

Thermal detection of single photons using Dirac fermions.

Nature communications·2026

Same author

Nanophotonic waveguide chip-to-world beam scanning.

Nature·2026

Same author

Single-shot matrix-matrix photonic processor based on spatial-spectral hypermultiplexed parallel diffraction.

Nature communications·2026

Same author

Disaggregated machine learning via in-physics computing at radio frequency.

Science advances·2026

Search research articles

Related Experiment Video

Updated: Nov 18, 2025

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

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

Published on: August 4, 2018

Freely scalable and reconfigurable optical hardware for deep learning.

Liane Bernstein¹, Alexander Sludds², Ryan Hamerly^3,4

¹Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA. lbern@mit.edu.

Scientific Reports

|February 5, 2021

Summary

Digital optical neural networks (DONN) offer a scalable solution to deep neural network (DNN) limitations. Optical interconnects enable efficient data transfer, outperforming electronics at microscale distances.

More Related Videos

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

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

Published on: September 5, 2012

Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution

Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution

Published on: August 16, 2012

Related Experiment Videos

Last Updated: Nov 18, 2025

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

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

Published on: August 4, 2018

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

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

Published on: September 5, 2012

Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution

Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution

Published on: August 16, 2012

Area of Science:

Computer Science
Electrical Engineering
Optics

Background:

Deep neural networks (DNNs) achieve higher accuracy with increased size, but scaling electronic processors faces limitations in communication, thermal management, and power delivery.
Current electronic systems struggle with the communication overhead and power demands of increasingly large DNN models.

Purpose of the Study:

To propose and demonstrate a Digital Optical Neural Network (DONN) that overcomes the scaling limitations of electronic DNNs.
To leverage optical interconnects and reconfigurable inputs for improved DNN scalability and efficiency.

Main Methods:

Developed a Digital Optical Neural Network (DONN) architecture featuring intralayer optical interconnects.
Implemented reconfigurable input values to enhance network flexibility.
Conducted a proof-of-concept experiment using a 3-layer, fully-connected network for MNIST image classification.
Analyzed the energy consumption of the DONN compared to electronic systems.

Main Results:

Demonstrated optical multicast for classifying 500 MNIST images using the DONN.
Identified that digital optical data transfer is more energy-efficient than electronics for computational unit spacing around 100 micrometers.
Showcased the potential of optical interconnects for information locality and architectural flexibility.

Conclusions:

Digital Optical Neural Networks (DONNs) present a viable path for scaling deep learning models beyond current electronic limitations.
Optical interconnects offer significant energy efficiency advantages over electronics in specific microscale applications.
DONNs provide a flexible and scalable architecture for future high-performance computing and artificial intelligence.