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相关概念视频

Action Potential01:31

Action Potential

8.0K
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...
8.0K
Postsynaptic Potential (PSP)01:32

Postsynaptic Potential (PSP)

2.7K
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...
2.7K
Long-term Potentiation01:35

Long-term Potentiation

55.3K
Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
55.3K
Propagation of Action Potentials01:23

Propagation of Action Potentials

5.9K
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...
5.9K
Action Potentials01:41

Action Potentials

131.3K
Overview
131.3K
Graded Potential01:19

Graded Potential

4.0K
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...
4.0K

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相关实验视频

Updated: Jul 17, 2025

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

Published on: May 29, 2017

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蛋白质的神经潜力超出了训练数据的范围.

Geemi P Wellawatte1, Glen M Hocky2, Andrew D White3

  • 1Department of Chemistry, University of Rochester, Rochester, New York 14627, USA.

The Journal of chemical physics
|August 29, 2023
PubMed
概括

神经网络 (NN) 粗粒度 (CG) 力场可以推断到新的蛋白质区域,即使训练数据有限. 这证明了它们在更高效的分子模拟方面的潜力.

科学领域:

  • 计算化学是一种计算化学.
  • 分子动力学分子动力学
  • 机器学习在生物物理学中的应用

背景情况:

  • 粗粒度 (CG) 分子力学力场对于模拟大型生物分子至关重要.
  • 传统的CG力场往往难以将其推广到看不见的形状状态.
  • 神经网络 (NN) 为开发更强大,更适应的CG力场提供了一个有希望的途径.

研究的目的:

  • 为了比较基于NN的CG力场与传统的CG力场的性能.
  • 为了研究在有限的数据上训练的NN CG力场的推断能力.
  • 评估力匹配误差与自由能量表面重建精度之间的关系.

主要方法:

  • 从四个蛋白质轨迹中训练了88个NN CG力场,使用了来自四个蛋白质轨迹的集群自由能量表面的多种组合.
  • 采用原子模拟来生成自由能量参考表面.
  • 利用总变异相似性,一个统计计计量,来量化参考和NN CG自由能量表面之间的一致性.

主要成果:

  • NN CG力场展示了从自由能量表面的未被访问区域进行推断和采样的能力.
  • 用有限的数据进行训练并没有妨碍NN CG力场的推断能力.
  • 实力匹配错误只与重建的自由能量表面的准确性产生了微弱的相关性.

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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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相关实验视频

Last Updated: Jul 17, 2025

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays
10:45

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

Published on: May 29, 2017

9.9K
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

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Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

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结论:

  • 在有限的数据上训练时,NN CG力场可以泛化到未见的蛋白质构造区域.
  • 这些发现支持了NN CG力场具有显著的外加推算功率的假设.
  • 力量匹配错误不是CG力场能够准确地表示自由能量景观的能力的可靠预测指标.