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

You might also read

Related Articles

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

Sort by
Same author

HNF4α controls growth, identity, and KRAS inhibitor response in invasive mucinous adenocarcinoma of the lung.

The Journal of clinical investigation·2026
Same author

Temperature-controlled reductive alkylation of single-walled carbon nanotubes for optimizing near-infrared photoluminescence.

Chemical communications (Cambridge, England)·2026
Same author

Role of Second Halogen Atoms of Dihalobenzene in Controlling the Photoluminescence Properties of Single-Walled Carbon Nanotubes by Reductive Arylation.

ACS nanoscience Au·2026
Same author

Who will win the race for the first CFTR gene replacement therapy?

Molecular therapy : the journal of the American Society of Gene Therapy·2026
Same author

PBAE-PEG/Lipid Nanoparticle Delivery of RNA for the Creation of Genetically Engineered Lung Cancer Mouse Models.

Nano letters·2025
Same author

Voices <i>in Molecular Pharmaceutics</i>: Meet Dr. Yutaka Maeda, Who Pioneers New Therapies to Treat Lung Disease.

Molecular pharmaceutics·2025

Related Experiment Video

Updated: Mar 31, 2026

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
08:59

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

7.7K

Improved Learning Performance of Hardware Self-Organizing Map Using a Novel Neighborhood Function.

Hiroomi Hikawa, Yutaka Maeda

    IEEE Transactions on Neural Networks and Learning Systems
    |October 21, 2015
    PubMed
    Summary

    This study introduces a new hardware-friendly neighborhood function for self-organizing maps (SOMs) to enhance vector quantization. The novel function improves SOM performance without increasing hardware costs or reducing speed.

    More Related Videos

    Modeling the Functional Network for Spatial Navigation in the Human Brain
    05:55

    Modeling the Functional Network for Spatial Navigation in the Human Brain

    Published on: October 13, 2023

    1.6K
    Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation
    20:12

    Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation

    Published on: October 8, 2011

    31.2K

    Related Experiment Videos

    Last Updated: Mar 31, 2026

    Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
    08:59

    Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

    Published on: October 28, 2018

    7.7K
    Modeling the Functional Network for Spatial Navigation in the Human Brain
    05:55

    Modeling the Functional Network for Spatial Navigation in the Human Brain

    Published on: October 13, 2023

    1.6K
    Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation
    20:12

    Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation

    Published on: October 8, 2011

    31.2K

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Hardware Acceleration

    Background:

    • Traditional hardware implementations of self-organizing maps (SOMs) often use neighborhood functions limited to negative powers of two.
    • This limitation can impact the vector quantization performance of hardware-based SOMs.

    Purpose of the Study:

    • To propose and evaluate a novel, hardware-friendly neighborhood function for SOMs.
    • The goal is to improve vector quantization performance in hardware SOM implementations.

    Main Methods:

    • The proposed neighborhood function was simulated to assess its vector quantization capabilities.
    • The hardware SOM with the new function was implemented on a field-programmable gate array (FPGA) to evaluate hardware cost and speed.

    Main Results:

    • Simulations confirmed that the proposed neighborhood function enhances the vector quantization performance of hardware SOMs, even with the power-of-two restriction.
    • FPGA implementation showed no increase in hardware cost or decrease in operating speed.

    Conclusions:

    • The novel neighborhood function effectively improves SOM vector quantization performance.
    • It offers a hardware-efficient solution for accelerating SOMs, achieving high performance with parallel processing.