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

Super-resolution Fluorescence Microscopy

Super-resolution Fluorescence Microscopy

Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...

You might also read

Related Articles

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

Sort by

Same author

Random number generation from a self-chaotic broad-area VCSEL.

Optics letters·2026

Same author

Synchronization of complex spatio-temporal dynamics with lasers.

Light, science & applications·2026

Same author

Femtosecond laser-written mid-infrared waveguides in a heavy oxide germanate glass.

Optics letters·2025

Same author

Polarization and transverse-mode nonlinear dynamics in a multimode VCSEL.

Optics letters·2025

Same author

Passively mode-locked high-frequency dual-VCSEL system: erratum.

Optics express·2025

Same author

Chaos from a free-running broad-area VCSEL.

Optics letters·2025

Same journal

Gaussian-modulated continuous-variable quantum key distribution over 60 km fiber using an integrated silicon photonic receiver.

Optics letters·2026

Same journal

E2E-OCT: end-to-end joint learning model using optical coherence tomography images for vocal cord leukoplakia diagnosis.

Optics letters·2026

Same journal

Holographic generation of panoramic 3D scenes by concave ellipsoidal mirror reflection.

Optics letters·2026

Same journal

Dual-pilot phase recovery with pair-wise maximum-ratio combining for coherent PONs.

Optics letters·2026

Same journal

Mapping the whispering gallery modes of a CaF<sub>2</sub> disk resonator with half-tapered fibers to estimate the fundamental mode volume.

Optics letters·2026

Same journal

Quantitative estimation of deep-subwavelength scale via dark-field scattering axial energy concentration decay profiles.

Optics letters·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 17, 2025

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

Photorefractive reservoir computing.

Sebastian Alveteg, Marc Sciamanna, Alex Fuerbach

|July 1, 2025

Summary

This summary is machine-generated.

Reservoir computing utilizes physical systems for machine learning tasks. A nonlinear photorefractive reservoir computer successfully predicted chaotic time series, achieving a low mean square error.

More Related Videos

Recombination Dynamics in Thin-film Photovoltaic Materials via Time-resolved Microwave Conductivity

Recombination Dynamics in Thin-film Photovoltaic Materials via Time-resolved Microwave Conductivity

Published on: March 6, 2017

$Comparison of Agreement and Accuracy using Binocular Wavefront Optometer with Autorefractor and Phoropter$

Comparison of Agreement and Accuracy using Binocular Wavefront Optometer with Autorefractor and Phoropter

Published on: September 16, 2025

Related Experiment Videos

Last Updated: Sep 17, 2025

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

Recombination Dynamics in Thin-film Photovoltaic Materials via Time-resolved Microwave Conductivity

Recombination Dynamics in Thin-film Photovoltaic Materials via Time-resolved Microwave Conductivity

Published on: March 6, 2017

$Comparison of Agreement and Accuracy using Binocular Wavefront Optometer with Autorefractor and Phoropter$

Comparison of Agreement and Accuracy using Binocular Wavefront Optometer with Autorefractor and Phoropter

Published on: September 16, 2025

Area of Science:

Physics
Computer Science
Machine Learning

Background:

Reservoir computing (RC) is an emerging machine learning (ML) framework.
RC leverages physical systems to perform complex computations.
Interest in alternative computing paradigms is growing.

Purpose of the Study:

Demonstrate the efficacy of a nonlinear photorefractive reservoir computer.
Utilize the reservoir computer for tasks demanding nonlinearity and memory.
Optimize the reservoir for chaotic time series prediction.

Main Methods:

Employed a nonlinear photorefractive reservoir computer.
Adjusted photorefractive response via applied field and laser power to tune reservoir characteristics.
Performed a 10-step Mackey-Glass (MG) time series prediction task.

Main Results:

The photorefractive reservoir computer successfully performed time series prediction.
Achieved a mean square error (MSE) of 5x10^-4 for the 10-step MG prediction.
Demonstrated control over reservoir characteristics by manipulating physical parameters.

Conclusions:

Nonlinear photorefractive systems are viable for reservoir computing.
The demonstrated RC can handle tasks requiring nonlinearity and memory.
Physical parameter tuning allows for optimization of RC performance for specific ML tasks.