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相关概念视频

Action Potential01:31

Action Potential

7.9K
Neurons communicate by firing action potentials—the electrochemical signal that is propagated along the axon. The signal results in the release of neurotransmitters at axon terminals, thereby transmitting information to the nervous system. An action potential is a specific "all-or-none" change in membrane potential that results in a rapid spike in voltage.
Membrane potential in neurons
Neurons typically have a resting membrane potential of about -70 millivolts (mV). When they...
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Action Potentials01:41

Action Potentials

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Overview
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Generation of Action Potential in Skeletal Muscles01:24

Generation of Action Potential in Skeletal Muscles

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Every cell in the body maintains a membrane potential due to an uneven distribution of positive and negative charges across its plasma membrane. The membrane potential is measured in millivolts and quantifies the difference in charge across the membrane.
Like neurons, muscle cells are also regarded as excitable due to their capacity to change in response to stimuli, primarily due to voltage-gated ion channels embedded in their plasma membranes, which get activated by alterations in the...
4.3K
Action Potential: Phases of Stimulation01:28

Action Potential: Phases of Stimulation

5.4K
The action potential is a complex electrical event that occurs in excitable cells, such as neurons and muscle cells. It consists of several distinct phases, each with specific characteristics.
Resting Phase:
In this phase, the cell's membrane is at its resting potential, typically around -70 millivolts (mV) for neurons. Inside the cell, there is a higher concentration of potassium ions (K+) and a lower concentration of sodium ions (Na+). Voltage-gated sodium channels are closed, and...
5.4K
Postsynaptic Potential (PSP)01:32

Postsynaptic Potential (PSP)

2.5K
Postsynaptic potential (PSP) refers to a change in the electrical potential of a neuron when neurotransmitters released by presynaptic neurons bind to postsynaptic receptors. This potential can either be excitatory, leading to depolarization and ultimately action potential generation, or inhibitory, leading to hyperpolarization and suppression of the postsynaptic neuron.
There are two types of receptors: ionotropic and metabotropic.
The ionotropic receptor is the membrane protein that has an...
2.5K
Graded Potential01:19

Graded Potential

3.8K
Graded potentials are localized fluctuations in the cell membrane's electrical charge, commonly found in the dendrites of neurons. The magnitude of these potential changes depends on the strength of the initiating stimulus. In a membrane at its resting potential, a graded potential signifies a voltage shift either above -70 mV or below -70 mV.
Graded potentials fall into two categories: depolarizing and hyperpolarizing. Depolarizing graded potentials typically occur when sodium (Na+) or...
3.8K

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相关实验视频

Updated: Jun 22, 2025

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
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Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

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机器学习反应潜力的机器学习

Yinuo Yang1, Shuhao Zhang2, Kavindri D Ranasinghe1

  • 1Department of Chemistry, University of Florida, Gainesville, Florida;

Annual review of physical chemistry
|June 28, 2024
PubMed
概括
此摘要是机器生成的。

机器学习潜力 (MLP) 加快了科学中的模拟. 反应式MLP (RMLP) 特别能够在各种尺度上快速,准确地分析化学反应,包括键断裂和形成.

关键词:
化学反应是化学反应.计算化学计算化学机器学习是机器学习.神经网络的神经网络的神经网络潜在的表面能量 表面能量

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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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相关实验视频

Last Updated: Jun 22, 2025

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
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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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科学领域:

  • 计算化学计算化学
  • 材料科学 材料科学 材料科学
  • 生物物理学的生物物理.
  • 机器学习应用 机器学习应用

背景情况:

  • 在过去的20年里,机器学习潜力 (MLP) 彻底改变了化学,生物和材料科学中的模拟.
  • MLP能够快速准确地计算热力学和运动性质,这对于理解复杂系统至关重要.
  • 传统的方法经常与模拟涉及断裂和形成纽带的系统的计算成本作斗争.

研究的目的:

  • 审查MLP的开发和应用,特别是反应性MLP (RMLPs),用于涉及化学反应的系统.
  • 突出RMLPs如何促进各种尺度上的反应动力学,动力学和热力学研究.
  • 讨论构建和培训RMLPs的策略,包括数据采样和罕见事件的积极学习.

主要方法:

  • 专注于神经网络和基于内核的算法来开发MLP模型.
  • 审查反应性MLP (RMLP) 的构建和培训方法.
  • 讨论数据采样策略,包括积极学习,以提高RMLP的性能,特别是对于罕见事件.

主要成果:

  • 与传统方法相比,RMLPs显著加快了反应动态的计算.
  • RMLPs能够有效计算反应轨迹,速率和自由能量景观.
  • 审查表明了RMLPs在不同系统规模和复杂性的多功能性.

结论:

  • 反应式MLP是促进化学,生物和材料科学研究的强大工具,它通过准确模拟反应.
  • 有效的数据采样和积极的学习策略是构建能够处理复杂化学事件的强大的RMLPs的关键.
  • 在依赖于分子模拟的领域,RMLPs为更深入的理解和更快的发现提供了一条途径.