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相关概念视频

Olfaction01:25

Olfaction

44.5K
The sense of smell is achieved through the activities of the olfactory system. It starts when an airborne odorant enters the nasal cavity and reaches olfactory epithelium (OE). The OE is protected by a thin layer of mucus, which also serves the purpose of dissolving more complex compounds into simpler chemical odorants. The size of the OE and the density of sensory neurons varies among species; in humans, the OE is only about 9-10 cm2.
The olfactory receptors are embedded in the cilia of the...
44.5K
Physiology of Smell and Olfactory Pathway01:20

Physiology of Smell and Olfactory Pathway

8.8K
Humans detect odors with the help of specialized cells located in the upper part of the nasal cavity, called olfactory receptor neurons (ORNs). ORNs possess hair-like structures called cilia, which are receptive to sensations from the inhaled air. When an odorant molecule binds to a specific receptor on the cell of the cilia, it leads to a series of events that ultimately cause the ORN to send electrical signals to the olfactory bulb in the brain through the olfactory nerves.
The olfactory...
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相关实验视频

Updated: Jul 28, 2025

A Free-breathing fMRI Method to Study Human Olfactory Function
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广泛的语言表征来预测可解释的气味混合物可辨别性.

Amit Dhurandhar1, Hongyang Li2, Guillermo A Cecchi2

  • 1Foundations of Trusted Artificial Intelligence, T.J. Watson IBM Research Laboratory, 1101 Kitchawan Rd, Yorktown Heights, NY 10598, United States.

Chemical senses
|June 1, 2023
PubMed
概括

这项研究表明,基于语言的语义气味描述器可以预测气味混合物的辨别能力. 这种语义模型通过改进气味可分辨性预测来推进嗅觉科学.

关键词:
歧视性 歧视性机器学习是机器学习.它们是元母体,元母体.计量学学习学习的方法自然语言处理自然语言处理.嗅觉混合物 嗅觉混合物

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科学领域:

  • 嗅觉科学是一种嗅觉科学.
  • 计算化学是一种计算化学.
  • 认知科学是一种认知科学.

背景情况:

  • 传统方法难以准确量化气味和嗅觉属性.
  • 现有的气味剂模型往往侧重于单个分子,而不是混合物.
  • 语言通常被认为不足以详细描述气味.

研究的目的:

  • 开发一种使用语义气味描述器来预测气味混合物的可辨别性模型.
  • 扩展现有的结构感知模型,包括复杂的嗅觉混合物.
  • 为了研究语言在量化嗅觉空间中的实用性.

主要方法:

  • 适应了对气味混合物的结构感知模型.
  • 使用化学描述符来预测语义属性 (例如",鱼"",烧"",甜") 和强度.
  • 在语义表示上使用度量学习来预测气味混合物的可区别性.
  • 使用10个精选的语义描述符来预测可歧视性和相似性.

主要成果:

  • 语义模型成功地预测了气味混合物的可区分性.
  • 这种方法在气味可分辨性预测方面优于现有的最先进的方法.
  • 该模型利用人类感知数据来增强预测的概括性.
  • 证明了快速获得气味混合物的可解释属性的能力.

结论:

  • 来自语言的语义描述符可以有效量化嗅觉属性,如混合物可区分性.
  • 开发的语义模型为气味相似性和可辨别性提供了改进和概括的预测.
  • 这项研究证实了语言的使用作为一种工具,用于在嗅觉空间内建立可区分度量.