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

Neuroplasticity01:01

Neuroplasticity

599
Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
599
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

3.2K
Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
3.2K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

126
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...
126
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

12.5K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
12.5K
Network Function of a Circuit01:25

Network Function of a Circuit

328
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
328
Neural Circuits01:25

Neural Circuits

1.3K
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...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Laplacian spectrum constrains collective performance enhancement.

Physical review. E·2026
Same author

Coexistence of many positive invariant sets in several classes of dynamical systems.

Chaos (Woodbury, N.Y.)·2026
Same author

Fuzzy reinforcement learning synchronization of stochastic dynamic networks: An adaptive event-triggered strategy.

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

Query-Efficient Hard-Label Attack: A Prior-Guided Adam Ray Search Optimization.

Sensors (Basel, Switzerland)·2026
Same author

Empirical analysis of AS-level cooperation on the internet considering geopolitical characteristics.

PloS one·2026
Same author

MAEPD: A Foundation Model for Distributed Acoustic Sensing Signal Recognition via Masked Autoencoder Pre-Training and Adapter-Based Prompt Tuning.

Sensors (Basel, Switzerland)·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: Jul 26, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.2K

RGP: Neural Network Pruning Through Regular Graph With Edges Swapping.

Zhuangzhi Chen, Jingyang Xiang, Yao Lu

    IEEE Transactions on Neural Networks and Learning Systems
    |June 13, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces Regular Graph Pruning (RGP), a novel method for lightweight deep learning models. RGP efficiently reduces model parameters and computations by analyzing network topology, achieving over 90% reduction while retaining accuracy.

    More Related Videos

    Rewiring Neuronal Circuits: A New Method for Fast Neurite Extension and Functional Neuronal Connection
    10:26

    Rewiring Neuronal Circuits: A New Method for Fast Neurite Extension and Functional Neuronal Connection

    Published on: June 13, 2017

    8.8K
    Author Spotlight: Exploring Glial Influence in Experience-Dependent Synaptic Pruning During Critical Periods
    07:13

    Author Spotlight: Exploring Glial Influence in Experience-Dependent Synaptic Pruning During Critical Periods

    Published on: March 1, 2024

    720

    Related Experiment Videos

    Last Updated: Jul 26, 2025

    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
    10:44

    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

    Published on: December 7, 2021

    2.2K
    Rewiring Neuronal Circuits: A New Method for Fast Neurite Extension and Functional Neuronal Connection
    10:26

    Rewiring Neuronal Circuits: A New Method for Fast Neurite Extension and Functional Neuronal Connection

    Published on: June 13, 2017

    8.8K
    Author Spotlight: Exploring Glial Influence in Experience-Dependent Synaptic Pruning During Critical Periods
    07:13

    Author Spotlight: Exploring Glial Influence in Experience-Dependent Synaptic Pruning During Critical Periods

    Published on: March 1, 2024

    720

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Computer Science

    Background:

    • Deep learning models require significant computational resources.
    • Existing neural network pruning methods focus on parameter importance, not topology, leading to inefficiencies.
    • Current pruning techniques are often dataset-specific and iterative.

    Purpose of the Study:

    • To develop an efficient, one-shot neural network pruning method.
    • To explore the relationship between network topology and model performance.
    • To reduce model parameters and floating-point operations (FLOPs) significantly.

    Main Methods:

    • Proposed Regular Graph Pruning (RGP) method based on neural network graph structure.
    • Generated regular graphs with node degrees set for a target pruning ratio.
    • Optimized graph edge distribution by minimizing average shortest path length (ASPL).
    • Mapped the pruned graph structure back to the neural network.

    Main Results:

    • Demonstrated a negative correlation between ASPL and neural network classification accuracy.
    • Achieved over 90% reduction in model parameters.
    • Achieved over 90% reduction in FLOPs.
    • RGP showed strong precision retention capabilities.

    Conclusions:

    • Regular Graph Pruning (RGP) offers an efficient and effective approach for lightweight model design.
    • Network topology analysis is crucial for optimizing pruning strategies.
    • RGP enables significant model compression with minimal accuracy loss.