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

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Protein Networks

Protein Networks

pH Scale

pH Scale

Hydronium and hydroxide ions are present both in pure water and in all aqueous solutions, and their concentrations are inversely proportional as determined by the ion product of water (Kw). The concentrations of these ions in a solution are often critical determinants of the solution’s properties and the chemical behaviors of its other solutes. Two different solutions can differ in their hydronium or hydroxide ion concentrations by a million, billion, or even trillion times. A common means of...

Nursing Implementation

Nursing Implementation

Implementation is the execution of the nursing care plan developed during the planning phase.
The five steps to implementing effective nursing care include reassessing the patient, reviewing and revising the existing nursing care plan, organizing the resources and care delivery, anticipating and preventing complications, and implementing nursing interventions.

Network Covalent Solids

Network Covalent Solids

Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...

Scaling

Scaling

In designing and analyzing filters, resonant circuits, or circuit analysis at large, working with standard element values like 1 ohm, 1 henry, or 1 farad can be convenient before scaling these values to more realistic figures. This approach is widely utilized by not employing realistic element values in numerous examples and problems; it simplifies mastering circuit analysis through convenient component values. The complexity of calculations is thereby reduced, with the understanding that...

You might also read

Related Articles

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

Sort by

Same author

Juvenile idiopathic arthritis (JIA) in Korea: epidemiology and biologic treatments.

Journal of rheumatic diseases·2026

Same author

Development of a First-tier Risk Assessment Tool for Occupational Hazards Among Public Officials.

Safety and health at work·2026

Same author

Can classification strategies improve automated cervical vertebral maturation staging? A comparative study.

Scientific reports·2026

Same author

Kawasaki disease shock syndrome in Korean children.

Clinical and experimental rheumatology·2026

Same author

Prognostic implication of tumor grade in patients with node-negative breast cancer aged ≤ 50 years with 21-gene recurrence scores of 11-25.

International journal of surgery (London, England)·2026

Same author

Surgical treatment of papillary thyroid cancer diagnosed during pregnancy: a retrospective, single-center, case-control study.

Annals of surgical treatment and research·2025

Same journal

A Model-Free Reinforcement Learning Implementation of Decision Making Under Uncertainty by Sequential Sampling.

Neural computation·2026

Same journal

DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning.

Neural computation·2026

Same journal

Hierarchical Active Inference Using Successor Representations.

Neural computation·2026

Same journal

W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators.

Neural computation·2026

Same journal

A Hidden Markov Model-Inspired Sequence Classification Method for Hyperdimensional Computing.

Neural computation·2026

Same journal

Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.

Neural computation·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 24, 2026

Designing and Implementing Nervous System Simulations on LEGO Robots

Designing and Implementing Nervous System Simulations on LEGO Robots

Published on: May 25, 2013

TAG: A Neural Network Model for Large-Scale Optical Implementation.

Hyuek-Jae Lee¹, Soo-Young Lee¹, Sang-Yung Shin¹

¹Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, P.O. Box 150 Chongryangni, Seoul, Korea.

Neural Computation

|May 31, 2019

Summary

This summary is machine-generated.

Training by Adaptive Gain (TAG) is a novel algorithm for optical neural networks. TAG offers a scalable solution with fewer adaptive elements, demonstrating comparable performance to perceptron models.

More Related Videos

Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling

Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling

Published on: May 31, 2017

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Related Experiment Videos

Last Updated: Jan 24, 2026

Designing and Implementing Nervous System Simulations on LEGO Robots

Designing and Implementing Nervous System Simulations on LEGO Robots

Published on: May 25, 2013

Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling

Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling

Published on: May 31, 2017

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Area of Science:

Artificial Intelligence
Optical Computing
Machine Learning

Background:

Large-scale artificial neural networks require efficient implementation methods.
Optical systems offer potential for high-speed parallel processing.

Purpose of the Study:

To introduce and evaluate the Training by Adaptive Gain (TAG) algorithm for optical neural networks.
To explore the feasibility of large-scale optical implementation using TAG.

Main Methods:

Developed TAG, an adaptive learning algorithm with fixed and adaptive interconnections.
Utilized multifacet holograms for fixed interconnections and spatial light modulators (SLMs) for adaptive gain controls.
Employed gradient descent and error backpropagation for the training algorithm.
Performed computer simulations to compare TAG performance with perceptron models.

Main Results:

TAG requires fewer adaptive elements compared to perceptron for similar network sizes.
Simulations showed reasonable performance of TAG, comparable to perceptron.
The algorithm is extensible to multilayer architectures.

Conclusions:

TAG presents a viable approach for the optical implementation of large-scale neural networks.
The algorithm balances performance with the practicality of optical hardware.
An electro-optical implementation of TAG is proposed for further development.