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相关概念视频

Olfaction01:25

Olfaction

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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...
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Physiology of Smell and Olfactory Pathway01:20

Physiology of Smell and Olfactory Pathway

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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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Olfactory Receptors: Location and Structure01:03

Olfactory Receptors: Location and Structure

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The process of olfaction, also known as the sense of smell, is a sophisticated chemical response system. The specialized sensory neurons that facilitate this process, known as olfactory receptor neurons, are situated in an upper segment of the nasal cavity, known as the olfactory epithelium. Olfactory sensory neurons are bipolar, with their dendrites extending from the epithelium's apex into the mucus that lines the nasal cavity. Airborne molecules, when inhaled, traverse the olfactory...
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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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从神经生理信号中解码嗅觉反应,使用多模式深度学习框架.

Chengxuan Tong1, Yi Ding2, Aung Aung Phyo Wai2

  • 1College of Computing and Data Science, Nanyang Technological University, 50 Nanyang Ave, Singapore, 639798, Singapore; Wilmar International, Singapore, 28 Biopolis Rd, Singapore, 138568, Singapore.

Neural networks : the official journal of the International Neural Network Society
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概括
此摘要是机器生成的。

本研究介绍了标记对齐和交叉注意力融合网络 (TACAF) 来解码EEG信号的嗅觉感知. TACAF显著提高了对气味神经反应的理解,揭示了对嗅觉适应的洞察力.

关键词:
深度学习是一种深度学习.电脑电图 (电脑电图) 是一种脑电图.神经网络的神经网络的神经网络变压器变压器变压器

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科学领域:

  • 神经科学是一个神经科学.
  • 感官感知是一种感官感知.
  • 计算神经科学是一种神经科学.

背景情况:

  • 人类嗅觉系统的时间动态对于感官感知至关重要.
  • 从脑电图 (EEG) 了解嗅觉感知的神经特征需要分析时间动态和整合呼吸等生理信号.
  • 现有的解码EEG嗅觉反应的方法在捕获复杂的时间和空间神经信息方面存在局限性.

研究的目的:

  • 引入一种新的多式联络深度学习框架,即令牌对齐和交叉注意力融合网络 (TACAF),用于增强嗅觉EEG解码.
  • 通过使用EEG和呼吸信号,研究潜在的嗅觉感知和适应的神经机制.
  • 通过有效地融合多式联络数据,提高解码嗅觉反应的准确性和稳定性.

主要方法:

  • 开发代币对齐和交叉注意力融合网络 (TACAF),一个多式联网深度学习框架.
  • 使用波纹特征来选择时间窗口和用于EEG数据表示的光谱分析.
  • 采用空间学习模块用于空间特征提取和多头自我注意力用于时间动态.
  • 实施时间令牌语义对齐 (TTSA) 模块,以同步EEG和呼吸数据进行融合.
  • 收集了20名暴露于愉快和不愉快气味的受试者的EEG和呼吸记录.

主要成果:

  • 在解码嗅觉EEG反应方面,TACAF框架显著优于现有的方法.
  • 分析显示,长时间暴露于气味会诱导嗅觉适应,导致识别性能下降.
  • 该研究成功地解码了不同类型的气味 (愉快与不愉快) 的神经反应.

结论:

  • TACAF网络提供了一种强大的工具来解码EEG的嗅觉感知,其性能优于目前的方法.
  • 嗅觉适应是影响嗅觉识别表现的重要因素,突显了感官处理的动态性质.
  • 这些发现为嗅觉感知和适应的神经机制提供了宝贵的见解,通过空间拓和突出地图可视化.