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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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Physiology of Respiration II: Neurogenic Control of Respiration01:22

Physiology of Respiration II: Neurogenic Control of Respiration

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The neurogenic control of respiration coordinates various neural networks and pathways to regulate breathing rate and depth, meeting the body's oxygen and carbon dioxide exchange requirements. This system adapts to physiological and environmental conditions, ensuring optimal breathing patterns.
Central Control
The brainstem is the primary site of central control, hosting respiratory centers:
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Neural Regulation01:37

Neural Regulation

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Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
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Motor Unit Stimulation01:20

Motor Unit Stimulation

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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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Muscles for Facial Expressions01:14

Muscles for Facial Expressions

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The craniofacial muscles are a collection of approximately 20 thin skeletal muscles situated beneath the skin of the face and scalp. These muscles, primarily responsible for the vast array of human facial expressions, originate from the bones or fibrous structures of the skull and extend outwards to connect with the skin. While most skeletal muscles in the body are enveloped in thick fascia, facial muscles generally have a more delicate fascial covering, with the buccinator muscle being a...
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Brainstem01:19

Brainstem

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The brainstem, located inferior to the brain and superior to the spinal cord, serves as a bridge between the cerebrum and the spinal cord. It plays a vital role in relaying information and controlling critical life functions. It comprises three primary regions: the midbrain, pons, and medulla oblongata.
The Midbrain
The midbrain is located beneath the diencephalon and connects the cerebrum with the lower parts of the brain. The cerebral peduncles are prominent midbrain structures that house the...
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Updated: Jun 18, 2025

An Implantable System For Chronic In Vivo Electromyography
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深度学习用于神经肌肉控制声源的声音生产.

Anil Palaparthi1, Rishi K Alluri2, Ingo R Titze1

  • 1Utah Center for Vocology, University of Utah, Salt Lake City, UT 84112, USA.

Applied sciences (Basel, Switzerland)
|July 29, 2024
PubMed
概括

一个深度学习的控制系统准确地管理了发声的肺压力和喉肌肉. 这种计算模型实现了精确的声学和体感目标,这对于语音合成和语音研究至关重要.

科学领域:

  • 计算生物物理学的计算生物物理.
  • 语音科学是一种语言科学.
  • 神经科学是一个神经科学.

背景情况:

  • 开发人类声系统的准确计算模型对于理解语音产生至关重要.
  • 神经肌肉控制系统在调节声学参数方面发挥着至关重要的作用.
  • 以前的模型往往缺乏集成的声学和体感反机制.

研究的目的:

  • 开发和评估基于深度学习的语音生成神经肌肉控制系统.
  • 整合声学和体感反,以精确控制声学参数.
  • 使用LeTalker生物物理模型来模拟和训练控制系统.

主要方法:

  • 采用了一个生物物理计算模型 (LeTalker) 与一个三质声折模型.
  • 设计了一个深度学习的控制系统,配有声学前和反控制器.
  • 该系统是通过LeTalker产生的5万个稳定的语音信号进行训练的.

主要成果:

  • 控制系统准确地实现了四个声学目标 (基本频率,声压水平,光谱中位素,信号与噪声比).
  • 该系统还准确地满足了四个体感官目标 (声长度,纤维应力).
  • 在训练后,反控制器的校正是最小的,除了甲状腺肌肉激活.
关键词:
在 TensorFlow 中,我们可以使用 TensorFlow.人工神经网络的人工神经网络非线性控制系统的非线性控制系统语音声学 语音声学 语音声学语音制作 语音制作

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Author Spotlight: Advancements in the Fabrication of Synthetic Vocal Fold Models for Phonetic and Robotic Applications
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Author Spotlight: Advancements in the Fabrication of Synthetic Vocal Fold Models for Phonetic and Robotic Applications
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结论:

  • 开发的深度学习控制系统有效调节肺压力和喉肌肉激活.
  • 该模型在实现语音控制的声学和体感目标方面表现出很高的准确性.
  • 这种方法为语音生成和声动控制的计算建模提供了一个强大的框架.