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

Neural Circuits01:25

Neural Circuits

3.0K
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...
3.0K

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

Identification and structure-activity relationship analysis of minimal fusion inhibitors targeting measles virus F protein.

RSC medicinal chemistry·2026
Same author

Manufacture of adeno-associated virus vectors by a novel human-derived cell line HAT and comprehensive evaluation of the vectors.

Molecular therapy. Advances·2026
Same author

Perioperative Complete Blood Count Changes After Endovascular Aneurysm Repair (EVAR) and Fenestrated EVAR (FEVAR): A Retrospective Cohort Study.

Cureus·2026
Same author

Forced degradation analysis of recombinant adeno-associated virus serotype 8 based on analytical anion exchange chromatography coupled to orthogonal characterization.

Journal of pharmaceutical sciences·2026
Same author

Identification of Recombinant Adeno-Associated Virus Serotypes by Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry.

Analytical chemistry·2026
Same author

Synergistic targeting of the ARID2-MYC axis by pomalidomide and panobinostat overcomes intrinsic IMiD resistance in multiple myeloma.

Scientific reports·2026

相关实验视频

Updated: Apr 28, 2026

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol
10:14

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

Published on: May 12, 2019

7.2K

神经网络嵌入功能微连接组.

Arata Shirakami1, Takeshi Hase2,3,4,5,6, Yuki Yamaguchi1

  • 1Graduate Schools of Medicine, Kyoto University, Kyoto, Japan.

Network neuroscience (Cambridge, Mass.)
|March 31, 2025
PubMed
概括

研究人员使用人工神经网络 (ANN) 和新型指标简化了复杂的大脑网络架构. 这种方法减少了87%的神经元数量,并确定了关键的连接模式,有助于未来的神经科学研究.

关键词:
中心的中心性.间接相邻的度是间接相邻的度.一个微型连接组邻居枢纽比率是邻居枢纽比率.网络嵌入 网络嵌入.神经网络的神经网络的神经网络新的指标新指标

更多相关视频

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

963
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.5K

相关实验视频

Last Updated: Apr 28, 2026

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol
10:14

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

Published on: May 12, 2019

7.2K
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

963
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.5K

科学领域:

  • 神经科学是一个神经科学.
  • 计算生物学 计算生物学
  • 网络科学 网络科学

背景情况:

  • 大脑复杂的神经网络架构需要简化才能更深入地理解.
  • 神经网络中的功能连接模式对于破译大脑功能至关重要.

研究的目的:

  • 为了压缩和简化大脑网络架构.
  • 用新型网络指标解释功能连接模式.

主要方法:

  • 使用名为神经网络嵌入 (NNE) 的人工神经网络 (ANN) 进行自动压缩.
  • 网络分析比较压缩特征与15个已确定的网络指标.
  • 引入两个新的指标:间接相邻度和邻近枢纽比率.

主要成果:

  • 神经网络嵌入 (NNE) 将神经元数量表示率降低到原来的13%.
  • 新的指标,间接相邻度和邻近枢纽比率,解释了40%-45%的压缩特征.
  • 非国家企业促进了创新指标的开发,捕捉了现有指标遗漏的特征.

结论:

  • 这项研究成功地简化了复杂的神经元网络架构.
  • 来自NNE压缩特征的新型指标显著提高了功能连接的解释.
  • 这种方法为推进网络神经科学和理解大脑复杂性提供了一个强大的工具.