评估尖端排序的电生理学数据中的交叉污染
Jack P Vincent1,2, Michael N Economo1,2,3
1Department of Biomedical Engineering, Boston University, Boston, MA.
bioRxiv : the preprint server for biology
|January 8, 2024
概括
准确评估尖峰分类准确性对于细胞外电生理学至关重要. 本研究引入了一种新的分析解决方案,用于预测从尖峰间隔 (ISI) 违规事件中错误发现率 (FDR),改进尖峰分类质量评估.
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:
- 细胞外电生理学可以同时记录成千上万个神经元.
- 准确的尖峰分类对于分析大规模的神经数据至关重要.
- 间尖区间 (ISI) 违规通常用于估计尖分类准确性,但它们与错误发现率 (FDR) 的关系尚不清楚.
结论:
- 分析解决方案为评估尖端分类准确度提供了一个原则方法.
- 虽然预测单个集群的FDR具有挑战性,但人口水平的估计是准确的.
- 这些发现将有助于研究人员可靠地评估大规模神经记录中的尖端分类性能.
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