双Sort:在线尖峰排序与一个运行的神经网络
L M Meyer1, F Samann1,2, T Schanze1
1Technische Hochschule Mittelhessen - University of Applied Sciences, Giessen, Germany.
Journal of neural engineering
|October 5, 2023
概括
简单的神经网络 (NN) DualSort 能够有效地在实时情况下,以最少的人为投入,对神经尖端进行排序. 这种方法在尖峰检测和分离方面实现了高性能,即使在噪音条件下也是如此.
科学领域:
- 神经科学是一个神经科学.
- 计算神经科学是一种神经科学.
- 信号处理 信号处理
背景情况:
- 尖端分类,即识别和分离神经元动作潜力的过程,是大脑活动分析中的关键但具有挑战性的步骤.
- 现有的神经网络 (NN) 方法通常集中在尖端分类管道的单个组件上,需要复杂的架构.
- 需要高效,低复杂度的方法来实现实时尖端分类,减少手工干预.
研究的目的:
- 引入DualSort,简单的NN与后处理相结合,可实现高效和实时的尖端分类.
- 为了证明在尖端检测和分类方面可以在没有复杂的NN架构的情况下实现高性能,即使在高噪音下.
- 通过数据增强技术,减少对大量手动标签的需求.
主要方法:
- 简单的神经网络 (NN) DualSort 使用合成和实验单通道细胞外记录进行了训练和评估.
- 该NN通过在信号中代地分类尖峰来检测和分类尖峰波形.
- 下游后处理算法将NN输出精制成精确的尖峰列车,提高整体系统的稳定性.
主要成果:
- DualSort成功地检测,区分和分离了不同的神经元尖峰波形和背景噪声.
- 集成后处理显著提高了模型的性能和稳定性.
- 双排序证明了与针对特定子问题的最先进方法相比具有竞争力的性能.
结论:
- 简单的神经网络,如DualSort,加上后处理,足以实现高性能尖端分类,从而挑战了复杂架构的需求.
- 该框架可以通过数据增强来减少手动标签,并且可以通过无监督的伪标签自主运行.
- 由于 DualSort 的低复杂性,可以在基本硬件上实现高效的实时处理,并显示出分析其他生物信号 (如EEG) 的潜力.
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