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

2.7K
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...
2.7K
Neuron Structure01:30

Neuron Structure

17.8K
Neurons are the main type of cell in the nervous system that generate and transmit electrochemical signals. They primarily communicate with each other using neurotransmitters at specific junctions called synapses. Neurons come in many shapes that often relate to their function, but most share three main structures: an axon and dendrites that extend out from a cell body.
Structure and Function of Neurons
The neuronal cell body—the soma— houses the nucleus and organelles vital to...
17.8K
Neuron Structure01:31

Neuron Structure

230.7K
Overview
230.7K
Classification of Signals01:30

Classification of Signals

1.3K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.3K
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

8.0K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
8.0K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

487
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...
487

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

Graph-Convolutional-Beta-VAE for synthetic abdominal aortic aneurysm generation.

Medical & biological engineering & computing·2025
Same author

What we should learn from pandemic publishing.

Nature human behaviour·2024
Same author

Learning the effective order of a hypergraph dynamical system.

Science advances·2024
Same author

Collaboration and topic switches in science.

Scientific reports·2024
Same author

Fast computation of matrix function-based centrality measures for layer-coupled multiplex networks.

Physical review. E·2022
Same author

A framework for second-order eigenvector centralities and clustering coefficients.

Proceedings. Mathematical, physical, and engineering sciences·2020
Same journal

Erratum: Bacterial Turbulence at Compressible Fluid Interfaces [Phys. Rev. Lett. 136, 138301 (2026)].

Physical review letters·2026
Same journal

Unveiling Light-Quark Yukawa Flavor Structure via Dihadron Fragmentation at Lepton Colliders.

Physical review letters·2026
Same journal

Adaptable Route to Fast Coherent State Transport via Bang-Bang-Bang Protocols.

Physical review letters·2026
Same journal

Topological Transition and Emergence of Elasticity of Dislocation in Skyrmion Lattice: Beyond Kittel's Magnetic-Polar Analogy.

Physical review letters·2026
Same journal

Pound-Drever-Hall Method for Superconducting-Qubit Readout.

Physical review letters·2026
Same journal

Coupling a ^{73}Ge Nuclear Spin to an Electrostatically Defined Quantum Dot in Silicon.

Physical review letters·2026
查看所有相关文章

相关实验视频

Updated: Jan 18, 2026

Examining Local Network Processing using Multi-contact Laminar Electrode Recording
13:40

Examining Local Network Processing using Multi-contact Laminar Electrode Recording

Published on: September 8, 2011

13.2K

在多层网络中的核心-外围检测.

Kai Bergermann1, Francesco Tudisco2

  • 1Technische Universität Chemnitz, Department of Mathematics, 09107 Chemnitz, Germany.

Physical review letters
|September 10, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的模型和非线性光谱方法,用于多层网络中的核心-外围检测. 该方法在节点和层中识别了核心和外围结构,为复杂系统提供了新的见解.

更多相关视频

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

1.0K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.0K

相关实验视频

Last Updated: Jan 18, 2026

Examining Local Network Processing using Multi-contact Laminar Electrode Recording
13:40

Examining Local Network Processing using Multi-contact Laminar Electrode Recording

Published on: September 8, 2011

13.2K
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

1.0K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.0K

科学领域:

  • 网络科学 网络科学
  • 复杂系统分析 复杂系统分析
  • 数据挖掘 数据挖掘

背景情况:

  • 多层网络模型系统具有多种交互类型.
  • 核心-外围检测识别中心 (核心) 和外部 (外围) 网络结构.
  • 现有的方法经常与多层,加权和定向网络的复杂性作斗争.

研究的目的:

  • 提出多层网络中核心-外围结构的新型模型.
  • 开发一种非线性光谱方法,用于同时检测节点和层核心-外围.
  • 分析各种经验多层网络中的结构洞察力.

主要方法:

  • 开发了多层核心-外围结构的新数学模型.
  • 实施了一种非线性光谱分析技术.
  • 将该方法应用于加权和定向的多层网络.

主要成果:

  • 在节点和层中同时成功检测到核心和外围结构.
  • 在三个不同的实证网络中揭示了新的结构洞察力.
  • 证明了该方法对引用,运输和贸易网络的适用性.

结论:

  • 拟议的模型和方法有效地捕捉了核心-外围组织在复杂的多层网络.
  • 非线性光谱方法为不同领域的网络架构提供了有价值的见解.
  • 这项工作促进了对加权和定向多层系统结构的理解.