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

Neural Circuits01:25

Neural Circuits

3.2K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
3.2K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

519
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
519

You might also read

Related Articles

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

Sort by
Same author

Deep semi-supervised learning via dynamic anchor graph embedding in latent space.

Neural networks : the official journal of the International Neural Network Society·2021
Same author

Personalised modelling with spiking neural networks integrating temporal and static information.

Neural networks : the official journal of the International Neural Network Society·2019
Same author

Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: Design methodology and selected applications.

Neural networks : the official journal of the International Neural Network Society·2015
Same author

Deformed graph laplacian for semisupervised learning.

IEEE transactions on neural networks and learning systems·2015
Same author

Exosomal-like vesicles with immune-modulatory features are present in human plasma and can induce CD4+ T-cell apoptosis in vitro.

Transfusion·2010
Same author

Qualitative analysis and simultaneous quantification of phenolic compounds in the aerial parts of Salvia miltiorrhiza by HPLC-DAD and ESI/MS(n).

Phytochemical analysis : PCA·2010

Related Experiment Video

Updated: Mar 24, 2026

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

852

Mapping Temporal Variables Into the NeuCube for Improved Pattern Recognition, Predictive Modeling, and Understanding

Enmei Tu, Nikola Kasabov, Jie Yang

    IEEE Transactions on Neural Networks and Learning Systems
    |March 19, 2016
    PubMed
    Summary

    This study introduces an optimized mapping for temporal data in NeuCube spiking neural networks (SNNs). This method enhances temporal pattern recognition and event prediction for complex stream data.

    Related Experiment Videos

    Last Updated: Mar 24, 2026

    Decoding Natural Behavior from Neuroethological Embedding
    08:00

    Decoding Natural Behavior from Neuroethological Embedding

    Published on: October 3, 2025

    852

    Area of Science:

    • Computational Neuroscience
    • Machine Learning
    • Data Science

    Background:

    • Spiking neural networks (SNNs) like NeuCube are powerful for spatiotemporal data.
    • Existing methods struggle with arbitrary temporal stream data.
    • Accurate temporal pattern recognition and event prediction are crucial in many fields.

    Purpose of the Study:

    • To propose an optimized mapping method for temporal variables into the NeuCube SNN architecture.
    • To extend NeuCube's applicability to arbitrary stream data.
    • To improve temporal pattern recognition, event prediction, and data understanding.

    Main Methods:

    • Developed a novel optimized mapping technique for temporal stream data.
    • Applied the method to the NeuCube SNN architecture.
    • Validated the approach on three diverse benchmark datasets.

    Main Results:

    • Achieved improved accuracy in temporal pattern recognition and event prediction.
    • Demonstrated earlier and more accurate event prediction.
    • Provided better data understanding through NeuCube connectivity visualization.
    • Outperformed traditional machine learning and arbitrary SNN mapping techniques.

    Conclusions:

    • The proposed optimized mapping significantly enhances NeuCube's performance on temporal stream data.
    • This method broadens the application of SNNs for complex temporal data analysis.
    • The approach offers a more effective solution for pattern recognition and prediction tasks.