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相关概念视频

Motor Units00:46

Motor Units

59.0K
A motor unit consists of two main components: a single efferent motor neuron (i.e., a neuron that carries impulses away from the central nervous system) and all of the muscle fibers it innervates. The motor neuron may innervate multiple muscle fibers, which are single cells, but only one motor neuron innervates a single muscle fiber.
59.0K
Motor Unit Stimulation01:20

Motor Unit Stimulation

1.9K
When the neuron of a motor unit fires an action potential, it triggers a series of events, leading to a twitch contraction in the muscle fibers. The process of excitation-contraction coupling is crucial in relaying the action potential to the muscle fibers.
The latent period of contraction marks the onset of excitation-contraction coupling, when the action potential propagates across the sarcolemma, preparing the muscle fibers for contraction. As the fibers enter the contraction phase, the...
1.9K
Muscle Stimulation Frequency01:22

Muscle Stimulation Frequency

2.6K
The contraction strength of muscles is regulated by motor neurons, which modulate the frequency of action potentials dispatched to the motor units based on the body's requirements. This process of varying the muscle stimulation frequency allows muscles to contract with a force that is precisely tailored to the needs of the moment, whether lifting a feather or a heavy box.
Wave summation
At low firing rates, motor neurons induce individual twitch contractions in muscle fibers. These twitches...
2.6K

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相关实验视频

Updated: Sep 9, 2025

Electrophysiological Motor Unit Number Estimation MUNE Measuring Compound Muscle Action Potential CMAP in Mouse Hindlimb Muscles
09:07

Electrophysiological Motor Unit Number Estimation MUNE Measuring Compound Muscle Action Potential CMAP in Mouse Hindlimb Muscles

Published on: September 25, 2015

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基于卷积神经网络的运动单元数估计

Chen Junjun1, Zezhou Li2, Linyan Wu1

  • 1School of Rehabilitation Science and Engineering, University of Health and Rehabilitation Sciences, Room 301, Building 10, University of Health and Rehabilitation Sciences, 369 Dengyun Road, Gaoxin District, Qingdao, Shandong, 266113, CHINA.

Journal of neural engineering
|September 2, 2025
PubMed
概括
此摘要是机器生成的。

一个新的神经网络模型,NNEstimation,从复合肌肉动力潜力 (CMAP) 扫描中快速估计运动单元数 (MUNE). 这种人工智能方法比传统方法更准确,更快,

关键词:
CMAP扫描模拟复合肌动力电位 (CMAP) 扫描发动机单位数量估计 (MUNE)神经网络监督学习

更多相关视频

Author Spotlight: Studying Neuromuscular Responses and Motor Neuron Plasticity in Neurodegenerative Diseases
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Author Spotlight: Studying Neuromuscular Responses and Motor Neuron Plasticity in Neurodegenerative Diseases

Published on: April 19, 2024

514
CMAP Scan MUNE MScan - A Novel Motor Unit Number Estimation MUNE Method
08:25

CMAP Scan MUNE MScan - A Novel Motor Unit Number Estimation MUNE Method

Published on: June 7, 2018

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相关实验视频

Last Updated: Sep 9, 2025

Electrophysiological Motor Unit Number Estimation MUNE Measuring Compound Muscle Action Potential CMAP in Mouse Hindlimb Muscles
09:07

Electrophysiological Motor Unit Number Estimation MUNE Measuring Compound Muscle Action Potential CMAP in Mouse Hindlimb Muscles

Published on: September 25, 2015

21.4K
Author Spotlight: Studying Neuromuscular Responses and Motor Neuron Plasticity in Neurodegenerative Diseases
06:08

Author Spotlight: Studying Neuromuscular Responses and Motor Neuron Plasticity in Neurodegenerative Diseases

Published on: April 19, 2024

514
CMAP Scan MUNE MScan - A Novel Motor Unit Number Estimation MUNE Method
08:25

CMAP Scan MUNE MScan - A Novel Motor Unit Number Estimation MUNE Method

Published on: June 7, 2018

12.5K

科学领域:

  • 神经科学
  • 计算生物学
  • 生物医学工程

背景情况:

  • 复合肌动能扫描 (CMAP) 可提供详细的肌动能数据.
  • 使用CMAP扫描的当前机动单元数估计 (MUNE) 方法通常需要耗时的手动数据安装.

研究的目的:

  • 探索基于神经网络的快速MUNE方法的可行性.
  • 建议并评估CMAP扫描中的MUNE端到端卷积神经网络 (CNN) 模型.

主要方法:

  • 开发了NNEstimation,一个使用CNN的监督学习框架.
  • 用于神经网络训练的各种参数生成合成CMAP扫描.
  • 在合成和实验CMAP数据上测试NN估计.

主要成果:

  • 与合成数据的MScanFit数据拟合方法相比,NNEstimation的估计误差较小,执行时间较短.
  • 估计的准确性主要取决于电机单元的数量,而不是噪声或振幅.
  • 从NNEstimation对实验数据的估计显示与MScanFit结果的高度一致.

结论:

  • 在合成数据上训练的NNEstimation提供了与实验数据的传统方法相美的结果.
  • 人工智能驱动的方法显著减少了执行时间,这表明了实际MUNE应用的巨大潜力.