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

Search research articles

Related Experiment Video

Updated: Sep 11, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Designing robust diffractive neural networks with improved transverse shift tolerance.

Daniil V Soshnikov, Leonid L Doskolovich, Georgy A Motz

Journal of the Optical Society of America. A, Optics, Image Science, and Vision

|August 12, 2025

Summary

This summary is machine-generated.

Related Concept Videos

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

You might also read

Related Articles

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

Sort by

Same author

Surface and Interface Engineering in Integrated Photonic Sensors: Performance Trade-Offs, Stability, and Benchmarking.

Micromachines·2026

Same author

Dispersion Engineering and Sensitivity Enhancement in Photonic Crystal Waveguide Sensors: Current Advances and Emerging Challenges.

Sensors (Basel, Switzerland)·2026

Same author

Design of harmonic diffractive optical elements for generating prescribed intensity distributions.

Applied optics·2026

Same author

Fizeau fringes in wedged resonant gratings.

Optics express·2026

Same author

Optical computation of the divergence of a vector field using a metal-dielectric multilayer.

Optics express·2025

Same author

Supporting quadric method for designing diffractive optical elements in the scalar diffraction theory framework.

Optics express·2025

Same journal

Multi-module collaborative optimization-driven fast speckle correlation imaging in variable environments.

Journal of the Optical Society of America. A, Optics, image science, and vision·2026

Same journal

Secrecy performance analysis of NOMA-UWOC systems over a vertically stratified WGG oceanic turbulence channel.

Journal of the Optical Society of America. A, Optics, image science, and vision·2026

Same journal

Backscattering of plane waves in a composite system containing a rough surface and anisotropic scatterers.

Journal of the Optical Society of America. A, Optics, image science, and vision·2026

Same journal

Aspherical surface construction methods based on extended Jacobi polynomials.

Journal of the Optical Society of America. A, Optics, image science, and vision·2026

Same journal

OCT sidelobe suppression method based on dual-path phase sinusoidal modulation and minimum value fusion.

Journal of the Optical Society of America. A, Optics, image science, and vision·2026

Same journal

Optical design concepts using wavelength-selective diffractive optics to enable miniaturized multimodal endoscopic imaging across separated spectral ranges.

Journal of the Optical Society of America. A, Optics, image science, and vision·2026

See all related articles

We developed a method to design diffractive neural networks (DNNs) robust to positioning errors. This approach ensures reliable image classification even with diffractive optical element (DOE) shifts.

Area of Science:

Optics and photonics
Machine learning
Computational imaging

Background:

Diffractive neural networks (DNNs) offer a promising approach for image classification.
Positioning errors in diffractive optical elements (DOEs) can significantly degrade DNN performance.
Robust DNN design is crucial for practical applications.

Purpose of the Study:

To propose a novel method for designing DNNs that are inherently robust to transverse shifts of their constituent DOEs.
To develop a gradient-based training method that accounts for positioning errors during DNN design.
To demonstrate the effectiveness of the proposed method in classifying handwritten digits.

Main Methods:

Representing classification error as a functional dependent on DOE phase functions and transverse shift vectors.

Related Experiment Videos

Last Updated: Sep 11, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Utilizing the mathematical expectation of this functional as the error functional for gradient-based DNN calculation.

Employing the Monte Carlo method for derivative calculation, equivalent to training with random DOE transverse shifts.

Main Results:

Explicit expressions for the error functional derivatives were derived.
DNNs designed with the proposed method exhibit robustness to DOE transverse shifts.
Successful classification of handwritten digits was achieved at visible wavelengths.

Conclusions:

The proposed gradient method enables the design of DNNs robust to DOE transverse shifts.
The designed DNNs maintain good performance even with significant transverse shifts (up to 17 wavelengths).
This work contributes to the development of reliable and practical diffractive optical computing systems.