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相关概念视频

Neuronal Communication01:28

Neuronal Communication

1.0K
Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
1.0K
Neural Circuits01:25

Neural Circuits

1.3K
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
Propagation of Action Potentials01:23

Propagation of Action Potentials

6.0K
The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
6.0K
Linear time-invariant Systems01:23

Linear time-invariant Systems

297
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
297
Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

896
The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the...
896
Overview of Synapses01:25

Overview of Synapses

2.4K
A synapse is a specialized structure where two neurons connect, allowing them to pass an electrical or chemical signal to another neuron. It is the point of communication between neurons. The term "synapse" is derived from the Greek word "synapsis," which means "conjunction." The entire process of neural communication revolves around the synapse. When activated, a neuron releases chemicals known as neurotransmitters into the synapse. These neurotransmitters cross the synapse and bind to...
2.4K

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相关实验视频

Updated: Jul 24, 2025

Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice
07:33

Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice

Published on: June 29, 2018

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神经元同步在时间变化的高阶网络中.

Md Sayeed Anwar1, Dibakar Ghosh1

  • 1Physics and Applied Mathematics Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata 700108, India.

Chaos (Woodbury, N.Y.)
|July 6, 2023
PubMed
概括

神经网络中的时间上层交互,以简化复合体的形式建模,比对对交互更有效地促进神经元同步. 这种同步是由群体交互密度增强,甚至可以发生在时间变化的连接.

科学领域:

  • 计算神经科学是一种神经科学.
  • 网络科学 网络科学
  • 复杂的系统复杂的系统.

背景情况:

  • 了解大脑的集体动态,特别是神经元同步,至关重要.
  • 时间和更高阶相互作用对这些动态的影响仍然是一个感兴趣的领域.

研究的目的:

  • 研究通过简化复合体建模的时间上层相互作用如何影响神经元同步.
  • 为了比较时间高阶网络中的同步与时间双向和静态多体相互作用.

主要方法:

  • 通过使用时间上层网络 (简单复合体) 建模具有间隙结相互作用的神经元组合.
  • 采用主稳定函数方法来分析同步解决方案的局部稳定性.
  • 进行数值模拟,观察神经元完全同步的出现.

主要成果:

  • 与时间对联或静态多体相互作用相比,在时间高阶网络中,同步的关键突触强度较低.
  • 神经元同步只能从更高阶的,时间变化的相互作用中出现.
  • 在时间上层结构中增强的同步与群体相互作用的密度相关.

结论:

  • 时间上层相互作用显著促进神经元同步.

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Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice

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Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays
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Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

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Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis
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Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

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  • 群体互动的密度在加强这些网络内的同步方面发挥着关键作用.
  • 来自主稳定性函数的分析条件与数值发现一致,验证了模型.