Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Long-term Potentiation01:35

Long-term Potentiation

55.2K
Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
55.2K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Relation-aware pre-trained network with hierarchical aggregation mechanism for cold-start drug recommendation.

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

Enhanced immobilization of cadmium, lead, and antimony with improved soil fertility using sulfate-reducing bacteria@nano zero-valent iron-modified biochar: coupled chemisorption and microbial mechanisms.

Frontiers in microbiology·2026
Same author

CNER-Omni: A unified dynamic modality learning framework for Chinese named entity recognition across text and speech.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Data Augmentation for Few-Shot Biomedical NER Using ChatGPT.

Artificial intelligence in medicine·2025
Same author

Traits improvement of wild rice O. rufipogon via multiplex genome editing.

Journal of integrative plant biology·2025
Same author

ADENER: A syntax-augmented grid-tagging model for Adverse Drug Event extraction in social media.

Journal of biomedical informatics·2025
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
Same journal

Self-Supervised Continuous Dynamic Graph Representation Learning via Hawkes Processes.

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

cPU: Consistent Risk Estimator for Positive-Unlabeled Learning.

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

Tuning-Free Latent Diffusion Models for Ultrahigh-Resolution Image Editing.

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

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026
查看所有相关文章

相关实验视频

Updated: Jun 28, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

529

时间网络嵌入增强了远程动态和自我监督的学习.

Zhizheng Wang, Yuanyuan Sun, Zhihao Yang

    IEEE transactions on neural networks and learning systems
    |April 15, 2024
    PubMed
    概括
    此摘要是机器生成的。

    长TNE通过捕捉远程顶点动态来增强时间网络嵌入,以获得高阶近距离. 这种方法改善了网络挖掘任务,并扩展了现有的时间网络嵌入技术.

    更多相关视频

    Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
    11:52

    Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

    Published on: February 9, 2017

    5.9K
    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    550

    相关实验视频

    Last Updated: Jun 28, 2025

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    529
    Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
    11:52

    Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

    Published on: February 9, 2017

    5.9K
    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    550

    科学领域:

    • 计算机科学 计算机科学
    • 网络科学 网络科学
    • 数据挖掘 数据挖掘

    背景情况:

    • 时间网络嵌入 (TNE) 对于在动态网络上发现知识和推理至关重要.
    • 现有的TNE方法难以捕捉长距离动态,限制了多节点拓协会的探索.
    • 这种局限性阻碍了对不断发展的网络中复杂关系的理解.

    研究的目的:

    • 提出LongTNE,这是一个新的时间网络嵌入方法,可以捕捉远程顶点动态.
    • 为了使TNE方法能够有效地捕捉时间网络中的高阶近距离 (HP).
    • 通过解决捕捉时间网络结构的局限性来提高网络挖掘任务的性能.

    主要方法:

    • LongTNE使用图形自主监督学习 (Graph SSL) 来优化网络快照中的深度链接建立概率.
    • 引入了一个累积前期更新 (AFU) 模块,以分析跨多个网络快照的全球时间演变.
    • 该方法侧重于学习顶点的远程动态,以增强网络表示.

    主要成果:

    • 六个时间网络的实证结果证明了LongTNE在网络挖掘任务中的最先进性能.
    • 长TNE有效地捕捉了高阶近距离,克服了现有的TNE方法的局限性.
    • 拟议的方法在保护网络结构和时间性质方面取得了显著的改进.

    结论:

    • 通过捕捉远程动态和高阶近距离,LongTNE提供了一种强大的时间网络嵌入方法.
    • 该方法在各种网络挖矿任务中实现了卓越的性能.
    • 长TNE具有适应性,并且可以很容易地扩展,以增强现有的时间网络嵌入技术.