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相关概念视频

Multiple Comparison Tests01:13

Multiple Comparison Tests

3.9K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
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Neural Circuits01:25

Neural Circuits

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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...
1.3K
Block Diagram Reduction01:22

Block Diagram Reduction

248
The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
248
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

17.8K
Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
17.8K
Bonferroni Test01:10

Bonferroni Test

2.8K
The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
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相关实验视频

Updated: Jul 24, 2025

Revealing Neural Circuit Topography in Multi-Color
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Revealing Neural Circuit Topography in Multi-Color

Published on: November 14, 2011

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有意义的子图挖掘神经网络推断与多重比较的校正.

Aaron J Gutknecht1,2,3, Michael Wibral1,2

  • 1Department of Data-driven Analysis of Biological Networks, Göttingen Campus Institute for Dynamics of Biological Networks, Georg August Universtiy, Göttingen, Germany.

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|July 3, 2023
PubMed
概括
此摘要是机器生成的。

显著的子图挖掘提供了一种新的方法来比较神经网络,通过识别底层图生成过程中的差异. 这种方法扩展到依赖过程,并在神经科学研究中得到验证,包括自闭症谱系障碍分析.

关键词:
自闭症 自闭症 自闭症图形理论是指图形的理论.多重比较 多重比较网络推断网络推断.统计 统计 统计 统计转移是指转移.

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Basics of Multivariate Analysis in Neuroimaging Data
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相关实验视频

Last Updated: Jul 24, 2025

Revealing Neural Circuit Topography in Multi-Color
09:11

Revealing Neural Circuit Topography in Multi-Color

Published on: November 14, 2011

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Basics of Multivariate Analysis in Neuroimaging Data
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Basics of Multivariate Analysis in Neuroimaging Data

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科学领域:

  • 计算神经科学是一种计算神经科学.
  • 图形理论是指图形的理论.
  • 机器学习 机器学习

背景情况:

  • 神经网络比较对于理解复杂系统至关重要.
  • 现有的方法可能无法完全捕捉图形生成过程中的差异.
  • 显著的子图挖矿提供了一个新的计算方法.

研究的目的:

  • 引入和扩展重要的子图挖掘,用于比较未加权图集.
  • 评估该方法的统计特性,并为神经科学应用提供实际建议.
  • 应用该方法来分析患者群体之间的大脑网络差异.

主要方法:

  • 应用显著的子图挖掘来比较图集.
  • 对依赖图生成过程的方法的扩展.
  • 使用埃尔多斯-雷尼模型的模拟研究和静止状态MEG数据的经验分析.
  • 转移网络的功率分析.

主要成果:

  • 在神经网络比较中显著的子图挖掘的证明实用性.
  • 为依赖图形过程提供了一个扩展,与主题内设计相关.
  • 通过广泛的错误统计调查,在神经科学中应用子图挖掘的实际建议.
  • 经验力量分析表明,该方法在区分自闭症谱系障碍中的网络方面是有效的.

结论:

  • 显著的子图挖掘是比较神经网络和理解生成过程的宝贵工具.
  • 扩展方法和实践指南有助于其在神经科学研究中的应用.
  • 在IDTxl工具箱中的Python实现提高了研究人员的可访问性.