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

Neuroplasticity01:01

Neuroplasticity

312
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.
312
Neural Circuits01:25

Neural Circuits

1.1K
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...
1.1K
Association Areas of the Cortex01:21

Association Areas of the Cortex

5.2K
Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
5.2K

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

Elevated Red Cell Distribution Width as a Potential Marker of Acute Mountain Sickness in Chinese Young Men Upon Rapidly Ascending to 3,650 m.

International journal of general medicine·2026
Same author

Time-Encoded Geometric Encryption Enabled by Shape-Memory Hydrogel with Photoisomerization-Gated Autonomous Recovery.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Integration of habitat radiomics and 2.5D deep features from <sup>18</sup>F-FDG PET/CT for noninvasive prediction of PD-L1 TPS ≥ 50% in non-small cell lung cancer.

European journal of radiology·2026
Same author

Dual roles of lactylation modification in gastric cancer: Crosstalk between metabolic reprogramming and epigenetic regulation (Review).

Oncology reports·2026
Same author

The Sleep Quality of Han Chinese and Tibetan Firefighters at High Altitude: A Field Study.

Nature and science of sleep·2026
Same author

Efficient spatio-angular reconstruction enables high-fidelity mapping of six-dimensional structures and dynamics with polarized fluorescence microscopy.

Research square·2026

相关实验视频

Updated: Jun 14, 2025

Topographical Estimation of Visual Population Receptive Fields by fMRI
06:02

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

9.2K

适应感受场图形神经网络的自适应神经网络.

Hepeng Gao1, Funing Yang1, Yongjian Yang1

  • 1Jilin University, Changchun, 130012, Jilin, China.

Neural networks : the official journal of the International Neural Network Society
|June 12, 2025
PubMed
概括

图形神经网络 (GNN) 由于过度平滑而面临性能下降. 我们的自适应受体场GNN (ADRP-GNN) 通过自适应扩展受体场来减轻这种情况,从而提高节点分类的准确性.

科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 图形神经网络的神经网络

背景情况:

  • 图形神经网络 (GNN) 是对表示学习的强大工具,但由于过度平滑问题,它们在更深层次的架构中遭受性能退化.
  • 过度平滑导致节点表示变得无法区分,限制了深度GNN的有效性.

研究的目的:

  • 为了解决深度GNN中的过度平滑问题.
  • 提出一种新的GNN架构,以增加深度保持性能.
  • 通过自适应地聚合邻居信息来提高节点分类的准确性.

主要方法:

  • 引入了一个自适应感应场图神经网络 (ADRP-GNN),该网络使用单层图形卷积层.
  • 开发了一种多节点图形卷积网络 (MuGC),以在单一层中捕获多节点邻近信息.
  • 整合了一个MetaLearner用于自适应的接收场生成和一个骨干网络来增强学习能力.

主要成果:

  • 拟议的ADRP-GNN有效地减轻了过度平滑的问题,而不需要更深的网络.
  • 在八个数据集的实验中,与最先进的方法相比,在节点分类任务中,准确度的提高从0.52%到6.88%不等.
  • 适应式接收场机制允许与现有的GNN框架集成,用于各种应用.
关键词:
图形神经网络的神经网络节点的分类 节点的分类过度平滑的问题.

更多相关视频

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

6.9K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.0K

相关实验视频

Last Updated: Jun 14, 2025

Topographical Estimation of Visual Population Receptive Fields by fMRI
06:02

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

9.2K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

6.9K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.0K

结论:

  • ADRP-GNN为GNN中的过度平滑问题提供了一个可行的解决方案.
  • 邻居信息的自适应聚合增强了表示学习和分类性能.
  • 这种架构为各种基于GNN的任务提供了灵活和有效的方法.