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

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

544
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...
544
Signal Sequences and Sorting Receptors01:41

Signal Sequences and Sorting Receptors

15.9K
Signal sequences are short amino acid sequences that guide newly synthesized proteins to their proper location within the cell. Classical signal sequences are fifteen to sixty amino acids long and present at the N-terminus of a polypeptide chain. Each signal sequence has a conserved segment of basic residues towards their N terminus, a hydrophobic core, and a C-terminus rich in polar residues. The C-terminus also contains a signal cleavage site and features a -3 -1 sequence motif. The -3-1...
15.9K
Neural Circuits01:25

Neural Circuits

3.3K
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...
3.3K
Neural Regulation01:37

Neural Regulation

45.1K
Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
45.1K

You might also read

Related Articles

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

Sort by
Same author

Dendritic nonlinearities mitigate communication costs.

Patterns (New York, N.Y.)·2026
Same author

Enhancing X-ray Image Classification through Heterogeneous Federated Learning with Natural Image-Augmented Models.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Developing and Testing a Brief Mindfulness Just-in-Time Adaptive Intervention to Reduce Stress Among Caregivers of People With Dementia: Quasi-Experimental Study.

JMIR aging·2026
Same author

Closed-loop correction reprogramming for fine-grained visual prompting.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Artificial plateau neurons with in-situ spike-malleability for rhythmic quadrupedal locomotion.

Nature communications·2026
Same author

Enhancing Generalization and Scalability for Multi-Objective Optimization with Population Pre-Training.

Evolutionary computation·2026
Same journal

Granular Ball-Based Noise-Resistant Fuzzy Multineighborhood Feature Selection via Label Enhancement and Feature Graph.

IEEE transactions on neural networks and learning systems·2026
Same journal

Fighting Evolving Spam With ARTMAP Models: A Noise-Resilient Online Detection Framework.

IEEE transactions on neural networks and learning systems·2026
Same journal

HyperSAT: Unsupervised Hypergraph Neural Networks for Weighted MaxSAT Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

Negation of Basic Belief Assignment in Multisource Information Fusion on Dempster-Shafer Theory With Applications in Pattern Classification.

IEEE transactions on neural networks and learning systems·2026
Same journal

Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Updated: Apr 14, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

11.0K

A Spiking Neural Network System for Robust Sequence Recognition.

Qiang Yu, Rui Yan, Huajin Tang

    IEEE Transactions on Neural Networks and Learning Systems
    |April 17, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel spiking neural network for sequence recognition, demonstrating robust performance against noise and timing variations. This biologically plausible model advances neural computation and cognitive computing applications.

    More Related Videos

    DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
    04:17

    DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

    Published on: May 10, 2024

    1.7K
    Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
    05:19

    Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

    Published on: November 12, 2019

    7.7K

    Related Experiment Videos

    Last Updated: Apr 14, 2026

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
    11:18

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

    Published on: March 2, 2015

    11.0K
    DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
    04:17

    DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

    Published on: May 10, 2024

    1.7K
    Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
    05:19

    Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

    Published on: November 12, 2019

    7.7K

    Area of Science:

    • Computational Neuroscience
    • Artificial Intelligence
    • Biologically Plausible Computing

    Background:

    • Current models often lack a unified framework for sensory processing, learning, and decoding.
    • Understanding neural mechanisms requires considering both upstream and downstream neuronal interactions.
    • Spiking neural networks offer potential for efficient and biologically realistic computation.

    Purpose of the Study:

    • To propose a unified, biologically plausible network architecture using spiking neurons for sequence recognition.
    • To systematically investigate the neural mechanisms underlying sequence recognition by integrating sensory encoding, learning, and decoding.
    • To demonstrate the efficacy of precise spike timing for information processing and cognitive computing.

    Main Methods:

    • Development of a novel network architecture incorporating spiking neurons for sequence recognition.
    • Implementation of a consistent temporal framework utilizing precise spike timing for information processing.
    • Evaluation of a temporal learning rule for classification on benchmark tasks.

    Main Results:

    • The proposed system successfully performs sequence recognition, showing robustness to noisy inputs and invariance to temporal interval variations.
    • The employed temporal learning rule outperformed other common rules in classification tasks.
    • Spiking neurons demonstrated superior computational power for spatiotemporal pattern processing compared to perceptrons.

    Conclusions:

    • The developed system provides a comprehensive approach for encoding stimuli, learning spike patterns, and decoding sequence order using spiking neurons.
    • This architecture offers a general framework for advancements in both neuromorphic hardware and AI software.
    • The findings highlight the potential of biologically inspired computing for complex cognitive tasks.