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相关概念视频

Neural Control of Respiration01:18

Neural Control of Respiration

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The neural regulation of respiration is a meticulously coordinated process primarily controlled by the respiratory centers located within the brainstem. These centers, composed of specialized neurons, transmit nerve impulses that control the contraction and relaxation of our respiratory muscles.
Respiratory Centers in the Brainstem
Two primary areas comprise the respiratory center: the medullary respiratory center in the medulla oblongata and the pontine respiratory group in the pons. The...
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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...
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用机器学习去解读和解码自发的迷走神经记录.

Mafalda Ribeiro, Ryan G L Koh, Tom Donnelly

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    机器学习模型有效地消除来自迷走神经的神经记录,改善神经调节器件的信号提取. 变异性自编码器在保存相关的呼吸活动信号方面表现出卓越的性能.

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    科学领域:

    • 生物医学工程 生物医学工程
    • 神经科学是一个神经科学.
    • 机器学习 机器学习

    背景情况:

    • 通过神经接口对外围神经进行刺激,有助于治疗和抑郁症等疾病.
    • 有效的神经调制需要有效地提取神经数据,通常受到低信号噪声比 (SNR) 和非静止噪声的挑战.
    • 机器学习 (ML) 是有前途的,但需要适应生物医学信号处理任务.

    研究的目的:

    • 调整和评估 ML 算法,用于无监督地消除神经记录的噪音.
    • 为了比较基于ML的denoising与传统的波段过渡过用于迷走神经活动.
    • 评估ML在保护关闭循环神经调节相关神经特征方面的有效性.

    主要方法:

    • 应用波段过渡过和两个新的ML算法到in-vivo迷走神经记录.
    • 使用一个变化自编码器 (VAE) 进行无监督的无声化.
    • 通过提取呼吸道 afferent 活动来比较 denoising 性能.

    主要成果:

    • 变化自编码器 (VAE) 模型表现出与带程过相比更高的性能.
    • 基于VAE的脱显示出与呼吸活动的更好相关性.
    • 使用交叉相关性,平均平方误差 (MSE) 和确定呼吸速率的准确性来验证性能.

    结论:

    • 机器学习算法,特别是VAE,可以有效地消除神经记录,同时保留必要的生物信号.
    • 这些ML方法为增强可植入神经调节装置的疗效提供了一个有希望的途径.
    • 进一步开发用于神经信号处理的ML对于推进闭环生物医学应用至关重要.