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相关概念视频

Brain Imaging01:14

Brain Imaging

225
Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
225

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Evaluation of Volatile Organic Compounds and Carbonyl Compounds Present in the Cabins of Newly Produced, Medium- and Large-Size Coaches in China.

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[Comparison and optimization of total ionic strength adjustment buffer during detecting fluoride in trace serum sample by fluoride ion selective electrode method].

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Flexible Transparent Electronic Gas Sensors.

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Magnetoresistance in Co/2D MoS2/Co and Ni/2D MoS2/Ni junctions.

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Novel Parent Survey Measures Sensory Behaviors Incorporating Sensory Modality and Stimulus Intensity.

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相关实验视频

Updated: Jun 21, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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大脑机器交互式神经调节研究工具与边缘AI计算

Yan Li1,2, Yingnan Nie1,2, Zhaoyu Quan3,4

  • 1Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.

Heliyon
|July 8, 2024
PubMed
概括
此摘要是机器生成的。

研究人员开发了一种用于实时神经调节的脑机界面工具. 这种智能系统集成了神经传感,人工智能计算和电刺激,加速了神经学和精神病治疗的研究.

关键词:
人工智能的人工智能是人工智能.闭环神经调节的神经调节.边缘AI计算 边缘AI计算机器学习 机器学习实时实时的时间.扣押检测检测 扣押检测 扣押检测

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Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
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相关实验视频

Last Updated: Jun 21, 2025

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科学领域:

  • 神经科学是一个神经科学.
  • 生物医学工程 生物医学工程
  • 人工智能的人工智能

背景情况:

  • 使用人工智能的闭环神经调节为神经和精神疾病提供了新的治疗方法.
  • 脑机接口对于推进精确和个性化的电子医学至关重要.
  • 加快闭环神经调节的发展需要实时神经传感和刺激的综合研究工具.

研究的目的:

  • 开发一种新的脑机交互神经调节研究工具 (BMINT).
  • 为了实现实时神经生理信号传感,人工智能驱动的计算和电刺激.
  • 促进人工智能生态系统的集成,以实现先进的神经调制策略.

主要方法:

  • 开发BMINT系统,整合神经传感,边缘人工智能计算和电刺激.
  • 实现主流机器学习算法用于实时数据处理.
  • 实时脉冲对脉冲的电刺激传递.

主要成果:

  • BMINT实现了系统时间延迟不到3毫秒.
  • 证明可行的计算成本和机器学习算法的高效部署.
  • 启用智能计算框架,使用人工智能生态系统资源实时闭环神经调节.

结论:

  • BMINT研究工具显著加速了智能闭环神经调节的发展.
  • 通过整合神经传感,边缘人工智能和刺激来促进翻译研究.
  • 为推进神经病学和精神病学领域的精确和个性化电子医学提供了一个平台.