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

Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of the problem,...
The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential.
Properties of the z-Transform I01:17

Properties of the z-Transform I

The z-transform is a fundamental tool in digital signal processing, enabling the analysis of discrete-time systems through its various properties. It is an invaluable tool for analyzing discrete-time systems, offering a range of properties that simplify complex signal manipulations. One fundamental property is linearity. For any two discrete-time signals, the z-transform of their linear combination equals the same linear combination of their individual z-transforms. This property is essential...
Ampere's Law: Problem-Solving01:31

Ampere's Law: Problem-Solving

Ampere's law states that for any closed looped path, the line integral of the magnetic field along the path equals the vacuum permeability times the current enclosed in the loop. If the fingers of the right hand curl along the direction of the integration path, the current in the direction of the thumb is considered positive. The current opposite to the thumb direction is considered negative.
Specific steps need to be considered while calculating the symmetric magnetic field distribution using...

You might also read

Related Articles

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

Sort by
Same author

Evidence for the Collective Nature of Radial Flow in Pb+Pb Collisions with the ATLAS Detector.

Physical review letters·2026
Same author

Evidence for the Dimuon Decay of the Higgs Boson in pp Collisions with the ATLAS Detector.

Physical review letters·2025
Same author

Evidence for Longitudinally Polarized W Bosons in the Electroweak Production of Same-Sign W Boson Pairs in Association with Two Jets in pp Collisions at sqrt[s]=13  TeV with the ATLAS Detector.

Physical review letters·2025
Same author

Observation of tt[over ¯] Production in Pb+Pb Collisions at sqrt[s_{NN}]=5.02  TeV with the ATLAS Detector.

Physical review letters·2025
Same author

Search for Fractionally Charged Particles in Proton-Proton Collisions at sqrt[s]=13  TeV.

Physical review letters·2025
Same author

Search for Magnetic Monopole Pair Production in Ultraperipheral Pb+Pb Collisions at sqrt[s_{NN}]=5.36  TeV with the ATLAS Detector at the LHC.

Physical review letters·2025

Related Experiment Video

Updated: May 25, 2026

Gradient Echo Quantum Memory in Warm Atomic Vapor
10:00

Gradient Echo Quantum Memory in Warm Atomic Vapor

Published on: November 11, 2013

Photonic information processing beyond Turing: an optoelectronic implementation of reservoir computing.

L Larger1, M C Soriano, D Brunner

  • 1UMR CNRS FEMTO-ST 6174/Optics Department, University of Franche-Comté, 16 Route de Gray, 25030 Besançon cedex, France.

Optics Express
|February 15, 2012
PubMed
Summary

Optical Reservoir Computing offers a novel solution for complex information processing challenges. This neuro-inspired approach utilizes nonlinear optoelectronic oscillators for efficient computation, outperforming traditional methods.

More Related Videos

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators
09:23

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

Published on: May 30, 2014

Generation and Coherent Control of Pulsed Quantum Frequency Combs
06:42

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Published on: June 8, 2018

Related Experiment Videos

Last Updated: May 25, 2026

Gradient Echo Quantum Memory in Warm Atomic Vapor
10:00

Gradient Echo Quantum Memory in Warm Atomic Vapor

Published on: November 11, 2013

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators
09:23

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

Published on: May 30, 2014

Generation and Coherent Control of Pulsed Quantum Frequency Combs
06:42

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Published on: June 8, 2018

Area of Science:

  • Optics and Photonics
  • Computational Science
  • Artificial Intelligence

Background:

  • Traditional computational methods face limitations with complex information processing tasks.
  • Optics presents a promising avenue for developing unconventional computational approaches.
  • Reservoir Computing, a neuro-inspired paradigm, offers universal computational capabilities.

Purpose of the Study:

  • To experimentally demonstrate optical information processing using a nonlinear optoelectronic oscillator.
  • To implement Reservoir Computing principles for enhanced computational power.
  • To evaluate the system's performance on benchmark tasks like spoken digit recognition and time series prediction.

Main Methods:

  • Utilizing a nonlinear optoelectronic oscillator with delayed feedback for computation.
  • Exploiting the transient response of a complex dynamical system to input data streams.
  • Implementing a neuro-inspired Reservoir Computing framework.

Main Results:

  • Successful experimental demonstration of optical information processing.
  • Achieved competitive figures of merit in spoken digit recognition.
  • Demonstrated effectiveness in time series prediction tasks.

Conclusions:

  • Nonlinear optoelectronic oscillators are effective platforms for Reservoir Computing.
  • Optical information processing offers a viable alternative to traditional computational methods.
  • This approach shows potential for solving complex information processing challenges.