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

Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

2.7K
The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
The relation  between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...
2.7K
The Second Law of Thermodynamics01:14

The Second Law of Thermodynamics

5.1K
In the quest to identify a property that may reliably predict the spontaneity of a process, a promising candidate has been identified: entropy. Scientists refer to the measure of randomness or disorder within a system as entropy. High entropy means high disorder and low energy. To better understand entropy, think of a student’s bedroom. If no energy or work were put into it, the room would quickly become messy. It would exist in a very disordered state, one of high entropy. Energy must be...
5.1K
Entropy02:39

Entropy

28.6K
Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
28.6K
Second Law of Thermodynamics02:49

Second Law of Thermodynamics

22.8K
In the quest to identify a property that may reliably predict the spontaneity of a process, a promising candidate has been identified: entropy. Processes that involve an increase in entropy of the system (ΔS > 0) are very often spontaneous; however, examples to the contrary are plentiful. By expanding consideration of entropy changes to include the surroundings, a significant conclusion regarding the relation between this property and spontaneity may be reached. In thermodynamic...
22.8K
Third Law of Thermodynamics02:38

Third Law of Thermodynamics

17.9K
A pure, perfectly crystalline solid possessing no kinetic energy (that is, at a temperature of absolute zero, 0 K) may be described by a single microstate, as its purity, perfect crystallinity,and complete lack of motion means there is but one possible location for each identical atom or molecule comprising the crystal (W = 1). According to the Boltzmann equation, the entropy of this system is zero.
17.9K
Entropy within the Cell01:22

Entropy within the Cell

10.3K
A living cell's primary tasks of obtaining, transforming, and using energy to do work may seem simple. However, the second law of thermodynamics explains why these tasks are harder than they appear. None of the energy transfers in the universe are completely efficient. In every energy transfer, some amount of energy is lost in a form that is unusable. In most cases, this form is heat energy. Thermodynamically, heat energy is defined as the energy transferred from one system to another that...
10.3K

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

Spatiotemporal spike-centered averaging reveals symmetry of temporal and spatial components of the spike-LFP relationship during human focal seizures.

Communications biology·2023
查看所有相关文章

相关实验视频

Updated: May 21, 2025

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

33.6K

对于时空神经网络特征的第三阶.

Sarita S Deshpande1,2,3, Wim van Drongelen2,3,4

  • 1Medical Scientist Training Program, University of Chicago, Chicago, Illinois, United States.

Journal of neurophysiology
|March 18, 2025
PubMed
概括

我们介绍了第三阶,这是分析神经网络活动的新型指标. 这种方法基于三重关联独特性定理,提供了比对联更完整的脑网络组织特征.

关键词:
三重相关性独特性定理 三重相关性独特性定理进入的过程中,神经网络特征表征 神经网络特征表征

更多相关视频

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.2K
Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
06:40

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

10.1K

相关实验视频

Last Updated: May 21, 2025

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

33.6K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.2K
Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
06:40

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

10.1K

科学领域:

  • 神经科学是一个神经科学.
  • 计算神经科学是一种神经科学.
  • 网络科学 网络科学

背景情况:

  • 神经网络具有复杂的信息处理能力.
  • 了解神经网络结构和相互作用对于破译大脑功能至关重要.
  • 现有的指标可能无法完全捕捉神经活动的复杂的时空动态.

研究的目的:

  • 引入和验证一种新的度量,第三阶,用于表征神经网络活动.
  • 为了证明第三阶在揭示网络组织方面优于双向.
  • 将新指标应用于实验神经数据.

主要方法:

  • 开发了基于三重相关性独特性 (TCU) 定理的第三阶.
  • 通过分析网络活动的时空滞后,计算了三重相关性.
  • 从三重相关数据估计的概率分布函数 (PDF) 来计算.
  • 通过模拟的尖拉斯特验证了该方法,并将其应用于大鼠皮质培养物.

主要成果:

  • 第三阶段提供了神经网络的完整和独特的特征.
  • 该指标在模拟和实验数据中成功识别了底层网络组织.
  • 与双向相比,第三阶在网络活动分析中显示出更深入的深度.
  • 从老鼠皮质培养的结果与双向结一致,但提供了更深入的见解.

结论:

  • 第三阶段是神经网络分析的一个强大而全面的工具.
  • 这种度量提供了对神经元之间的时空相互作用的更完整的理解.
  • 基于TCU的方法增强了复杂神经系统的特征.