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Neural Circuits01:25

Neural Circuits

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
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使用对抗性网络来扩展脑计算机接口的解码精度随着时间的推移.

Xuan Ma1, Fabio Rizzoglio1, Kevin L Bodkin1

  • 1Department of Neuroscience, Northwestern University, Chicago, United States.

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概括
此摘要是机器生成的。

循环一致的对抗网络 (Cycle-GAN) 为稳定脑计算机接口 (BCI) 提供了一个强大的解决方案. 这种方法通过调整神经数据分布,随着时间的推移提高解码器的准确性,减少了频繁重新校准的需要.

关键词:
在EMGEMGEMGEMGEMGEMGEMGEMGEM大脑-计算机接口接口发动机控制器的控制器神经科学 神经科学rhesus 子 子 子 子没有监督的学习学习.

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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
  • 机器学习 机器学习

背景情况:

  • 皮层内脑-计算机接口 (iBCI) 通过将神经活动转化为控制信号,恢复患者的运动.
  • 由于记录的神经元的变化,iBCI中的解码器精度随着时间的推移而降低,需要重新校准.
  • 重校准是耗时的,需要用户努力重新学习新的神经动态.

研究的目的:

  • 开发和评估用于稳定iBCI解码器的无监督方法,而不需要经常重新校准.
  • 通过对准神经活动的坐标系统来应对神经表征转移的挑战.
  • 为了比较循环-GAN与现有方法 (如ADAN和Procrustes对齐) 的疗效.

主要方法:

  • 提出了一种使用循环一致对抗网络 (Cycle-GAN) 调整全维神经记录分布的新方法.
  • 将循环-GAN与之前提出的通用对抗网络 (GAN) 方法,对抗域适应网络 (ADAN) 和基于因素分析的Procrustes对齐进行了比较.
  • 对来自多个子和多种行为数据的评估方法,重点是无监督学习,数据要求最小.

主要成果:

  • 与ADAN和Procrustes对齐相比,Cycle-GAN在稳定iBCI解码器方面表现优越.
  • 循环GAN被证明比ADAN更容易训练和更坚固.
  • 所有测试的方法都没有监督,并且需要最小的数据,表明实际可用性.

结论:

  • 循环GAN是一种有效和实用的方法,通过减轻由神经周转引起的解码器漂移来稳定iBCI系统.
  • 这些发现表明,Cycle-GAN可以显著提高脑电脑接口的长期可用性和可靠性.
  • 这种方法减少了对用户的负担,尽量减少需要经常性重新校准会话的需求.