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

1.4K
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...
1.4K
Neural Regulation01:37

Neural Regulation

39.6K
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.
39.6K

You might also read

Related Articles

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

Sort by
Same author

Palladium-catalyzed olefination and arylation of 2-substituted 1,2,3-triazole N-oxides.

Organic letters·2013
Same author

One-stop hybrid coronary revascularization versus coronary artery bypass grafting and percutaneous coronary intervention for the treatment of multivessel coronary artery disease: 3-year follow-up results from a single institution.

Journal of the American College of Cardiology·2013
Same author

Relative contributions of the thalamus and the paraventricular nucleus of the hypothalamus to the cardiac sympathetic afferent reflex.

American journal of physiology. Regulatory, integrative and comparative physiology·2013
Same author

Initial light soaking treatment enables hole transport material to outperform spiro-OMeTAD in solid-state dye-sensitized solar cells.

Journal of the American Chemical Society·2013
Same author

The rice GERMINATION DEFECTIVE 1, encoding a B3 domain transcriptional repressor, regulates seed germination and seedling development by integrating GA and carbohydrate metabolism.

The Plant journal : for cell and molecular biology·2013
Same author

Salvage intensity modulated radiotherapy using endorectal balloon after radical prostatectomy: clinical outcomes.

International journal of urology : official journal of the Japanese Urological Association·2013
Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

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

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

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

Adaptive Hardness-Driven Dictionary Distillation for Incomplete Streaming View Clustering.

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

Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation.

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

Task-KV: Task-aware KV Cache Optimization via Semantic Differentiation of Attention Heads.

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

Achieving Text-based Person Retrieval with Any Granularity.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Related Experiment Video

Updated: Aug 4, 2025

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

7.1K

Attention Spiking Neural Networks.

Man Yao, Guangshe Zhao, Hengyu Zhang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |April 6, 2023
    PubMed
    Summary
    This summary is machine-generated.

    Attention mechanisms in spiking neural networks (SNNs) bridge the performance gap with artificial neural networks (ANNs). This approach enhances SNNs for better performance and energy efficiency, paving the way for broader applications.

    More Related Videos

    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

    10.4K
    A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
    07:34

    A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

    Published on: March 25, 2014

    10.0K

    Related Experiment Videos

    Last Updated: Aug 4, 2025

    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

    7.1K
    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

    10.4K
    A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
    07:34

    A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

    Published on: March 25, 2014

    10.0K

    Area of Science:

    • Artificial Intelligence
    • Computational Neuroscience
    • Machine Learning

    Background:

    • Spiking neural networks (SNNs) offer energy efficiency but face performance limitations compared to artificial neural networks (ANNs).
    • Attention mechanisms, crucial for human cognition, can potentially enhance information processing in artificial systems.
    • Bridging the performance gap in SNNs is key to unlocking their full potential for widespread adoption.

    Purpose of the Study:

    • To introduce and investigate the efficacy of attention mechanisms within SNNs.
    • To develop a multi-dimensional attention module for SNNs that operates across temporal, channel, and spatial dimensions.
    • To demonstrate that attention can concurrently improve SNN performance, reduce spiking activity, and increase energy efficiency.

    Main Methods:

    • Proposed a multi-dimensional attention module for SNNs, inferring attention weights across temporal, channel, and spatial dimensions.
    • Integrated attention weights to optimize membrane potentials and regulate spiking responses, guided by neuroscience principles.
    • Conducted extensive experiments on event-based action recognition and image classification tasks, including ImageNet-1K.
    • Theoretically analyzed SNN training challenges like spiking degradation and gradient vanishing using block dynamical isometry theory.

    Main Results:

    • Attention mechanisms significantly improved SNN performance, achieving state-of-the-art top-1 accuracy on ImageNet-1K (75.92%/77.08% with Res-SNN-104).
    • Attention-enhanced SNNs demonstrated reduced performance gaps compared to equivalent ANNs (-0.95%/+0.21%) and substantial energy efficiency gains (31.8×/7.4×).
    • The study confirmed concurrent improvements in spiking sparsity, accuracy, and energy efficiency.
    • Theoretical analysis indicated that attention mechanisms can resolve issues like spiking degradation and gradient vanishing in SNNs.

    Conclusions:

    • Attention mechanisms are effective in enhancing SNNs, offering a compelling balance between performance and energy efficiency.
    • The proposed attention module and optimization strategy provide a viable solution to overcome the performance limitations of traditional SNNs.
    • This research validates SNNs' potential as a general backbone for diverse applications, addressing key challenges in the field.