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

Neural Circuits

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...

You might also read

Related Articles

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

Sort by

Same author

Multi-mode piezoelectric radiation-based microantennas and miniaturized wireless sensing unit driven by bulk acoustic waves.

Nature communications·2026

Same author

Coherent Perfect Absorption and Amplifying System Enabled Ultrasensitive Ammonia Gas Sensing.

ACS sensors·2025

Same author

Compact and low-crosstalk silicon-based polarization-insensitive multi-channel and multi-mode waveguide crossing.

Optics express·2025

Same author

Prognostic Value of Adding Magnetic Resonance Imaging to Computed Tomography in Acute Ischemic Stroke.

Journal of clinical ultrasound : JCU·2025

Same author

Analytical and experimental study of a valveless piezoelectric micropump with high flowrate and pressure load.

Microsystems & nanoengineering·2023

Same author

Intranasal administration with recombinant Bacillus subtilis induces strong mucosal immune responses against pseudorabies.

Microbial cell factories·2019

Same journal

Denoising algorithm of Φ-OTDR systems based on adaptive fractional wavelet transform denoising.

Optics express·2026

Same journal

Millisecond photon-to-photon latency and high-speed volumetric projection system for optogenetics.

Optics express·2026

Same journal

Polarization-encoded coaxial structured light for high-precision 3D surface profilometry.

Optics express·2026

Same journal

Discrete freeform optical design based on collaborative optimization of point cloud and local normals.

Optics express·2026

Same journal

Ultrafast ghost imaging with 25 GHz speckle switching and wavelength-division multiplexing.

Optics express·2026

Same journal

Atomic vapor cells fabricated by femtosecond laser welding of standard-optical-quality glass.

Optics express·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 13, 2025

Demonstration of Spin-Multiplexed and Direction-Multiplexed All-Dielectric Visible Metaholograms

Demonstration of Spin-Multiplexed and Direction-Multiplexed All-Dielectric Visible Metaholograms

Published on: September 25, 2020

On-chip diffractive optical neural network based on binary metasurfaces.

Kang Yang, Jian Lin, Pengjun Wang

|July 30, 2025

Summary

This summary is machine-generated.

This study introduces an on-chip diffractive optical neural network using binary metasurfaces. This compact device achieves 90% accuracy on the Iris dataset in simulations, demonstrating potential for efficient optical computing.

More Related Videos

Demonstration of Equal-Intensity Beam Generation by Dielectric Metasurfaces

Demonstration of Equal-Intensity Beam Generation by Dielectric Metasurfaces

Published on: June 7, 2019

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip

Published on: November 16, 2019

Related Experiment Videos

Last Updated: Sep 13, 2025

Demonstration of Spin-Multiplexed and Direction-Multiplexed All-Dielectric Visible Metaholograms

Demonstration of Spin-Multiplexed and Direction-Multiplexed All-Dielectric Visible Metaholograms

Published on: September 25, 2020

Demonstration of Equal-Intensity Beam Generation by Dielectric Metasurfaces

Demonstration of Equal-Intensity Beam Generation by Dielectric Metasurfaces

Published on: June 7, 2019

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip

Published on: November 16, 2019

Area of Science:

Optoelectronics
Nanophotonics
Artificial Intelligence

Background:

Diffractive optical neural networks (ONNs) offer a promising avenue for compact and efficient optical computing.
Metasurfaces provide a versatile platform for manipulating light at the nanoscale, enabling novel optical functionalities.
Integrating ONNs with metasurfaces can lead to miniaturized devices for complex computational tasks.

Purpose of the Study:

To propose and investigate an on-chip diffractive optical neural network (d-ONN) implemented using binary metasurfaces.
To optimize binary metasurfaces for compact size and high performance using advanced computational methods.
To demonstrate the d-ONN's capability in executing a real-world classification task.

Main Methods:

Utilized a genetic algorithm (GA) for optimizing the binary metasurface design.
Employed the finite-difference time-domain (FDTD) method for accurate optical simulations.
Designed and fabricated a single-layer d-ONN on a silicon-on-insulator (SOI) platform.

Main Results:

Achieved a compact single-layer d-ONN with an area of 16.5 µm × 23 µm.
Attained a simulation validation accuracy of 90.0% for the Iris dataset classification task.
Fabricated device demonstrated a measurement validation accuracy of 78.3%.

Conclusions:

The proposed on-chip diffractive optical neural network based on binary metasurfaces is effective for classification tasks.
The optimization methods (GA and FDTD) enable the design of compact and high-performance d-ONNs.
While simulation results are promising, further improvements are needed to bridge the gap between simulated and measured performance.