在大规模脑干记录中对尖分类器进行比较分析
Caitlynn C De Preter1,2, Elizabeth M Leimer3, Alex Sonneborn1,4
1Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, 97239, USA.
bioRxiv : the preprint server for biology
|November 28, 2024
概括
这项研究评估了五个尖峰分类软件包,用于在面向腹中中枢髓中高密度的神经记录. Kilosort3和IronClust需要最小的修复,有效地识别深层脑干区域中的神经单元.
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.
- 电子生理学 电子生理学
背景情况:
- 高密度的多通道电极可以从以前无法进入的大脑区域的众多神经元进行记录.
- 由于不同的神经特征,尖端分类性能因大脑区域而异,需要对特定区域进行评估.
研究的目的:
- 评估五种常见的尖分类包 (Kilosort3,MountainSort5,Tridesclous,SpyKING CIRCUS,IronClust) 在正面腹中枢髓 (RVM) 中的性能.
- 确定RVM中高密度网络级记录的最有效和高效的尖端分类方法.
主要方法:
- 从RVM使用高密度电极进行录制.
- 对记录的神经数据应用了五种尖端分类算法.
- 手动策划是为了完善单元识别而进行的,优先考虑多个排序器检测到的单元.
主要成果:
- 每个分类包都产生了不同的结果.
- Kilosort3和IronClust需要最少的手工策划,并确定了最多的单位.
- 在SpyKING CIRCUS和MountainSort5中,需要进行大量的策划,而Tridesclous确定了最少的单位.
- 所有测试的分类器都成功识别了已知的RVM生理细胞类型.
结论:
- 选择尖分类软件会影响RVM录音所需的手动策划水平.
- Kilosort3和IronClust为RVM中的高密度记录提供了高效和有效的尖端分类.
- 每个经过测试的分类器都可以从这个深层脑干区域提取有意义的神经数据,尽管治疗需求各不相同.
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