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相关概念视频

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...
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相关实验视频

Updated: Jul 11, 2025

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
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Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

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构建具有突发活动模式的时间网络.

Anzhi Sheng1,2, Qi Su3,4,5, Aming Li6,7

  • 1Center for Systems and Control, College of Engineering, Peking University, Beijing, 100871, China.

Nature communications
|November 11, 2023
PubMed
概括
此摘要是机器生成的。

研究人员开发了一种新的跨树方法来建模时间变化的社会互动. 这种方法准确地复制了现实世界人类行为中观察到的爆发模式,为动态网络过程提供了洞察力.

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Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
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Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

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Contribution of the Na+/K+ Pump to Rhythmic Bursting, Explored with Modeling and Dynamic Clamp Analyses
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Contribution of the Na+/K+ Pump to Rhythmic Bursting, Explored with Modeling and Dynamic Clamp Analyses

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相关实验视频

Last Updated: Jul 11, 2025

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Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
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Contribution of the Na+/K+ Pump to Rhythmic Bursting, Explored with Modeling and Dynamic Clamp Analyses
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科学领域:

  • 网络科学 网络科学
  • 计算社会科学 计算社会科学
  • 统计物理 统计物理

背景情况:

  • 人类的社会互动在强度上表现出时间的变化,无论是在线还是离线.
  • 描述这些动态交互往往涉及分析具有时间变化的链接和节点活动的网络.
  • 事件间时间分布,测量连续相互作用之间的持续时间,在经验数据中经常是重尾.

研究的目的:

  • 开发一种理论模型,能够复制经验时间网络数据中观察到的爆裂.
  • 构建具有爆发性行为的时间网络和活动模式.
  • 确保模型可以适应节点和链接的任何所需的事件间时间分布.

主要方法:

  • 开发一种新的跨树方法来构建时间网络.
  • 在网络模型中纳入时间变化的活动模式.
  • 确保目标事件间时间分布在一致性条件下是可重现的.

主要成果:

  • 拟议的跨树方法成功生成具有爆发性行为的时间网络.
  • 该模型可以重现实证数据集中观察到的重尾事件间时间分布.
  • 该方法的有效性是独立于底层网络拓是静态还是动态的.

结论:

  • 开发的跨树方法为模拟爆发性相互作用提供了一个强大的框架.
  • 这个模型可以准确地捕捉现实世界人类社交网络中看到的时间动态.
  • 它作为一种有价值的工具,用于研究系统的动态过程,其特点是爆发性相互作用.