Analysis of electrophysiological data from Purkinje cells (P-cells) of the cerebellum presents unique challenges to spike sorting. Complex spikes have waveforms that vary significantly from one event to the next, raising the problem of misidentification. Even when complex spikes are detected correctly, the simple spikes may belong to a different P-cell, raising the danger of misattribution. To address these identification and attribution problems, we wrote an open-source, semi-automated software called P-sort, and then tested it by analyzing data from P-cells recorded in three species: marmosets, macaques, and mice. Like other sorting software, P-sort relies on nonlinear dimensionality reduction to cluster spikes. However, it also uses the statistical relationship between simple and complex spikes to merge disparate clusters and split a single cluster. In comparison with expert manual curation, occasionally P-sort identified significantly more complex spikes, as well as prevented misattribution of clusters. Three existing automatic sorters performed less well, particularly for identification of complex spikes. To improve development of analysis tools for the cerebellum, we provide labeled data for 313 recording sessions, as well as statistical characteristics of waveforms and firing patterns of P-cells in three species.

中文翻译：

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P-sort：小脑神经生理学的开源软件

小脑浦肯野细胞 (P 细胞) 的电生理数据分析对尖峰分选提出了独特的挑战。复杂尖峰的波形从一个事件到下一个事件变化很大，从而引发了错误识别的问题。即使正确检测到复杂的尖峰，简单的尖峰也可能属于不同的 P 细胞，从而增加了错误归因的危险。为了解决这些识别和归因问题，我们编写了一个名为 P-sort 的开源半自动化软件，然后通过分析记录在三个物种（狨猴、猕猴和小鼠）中的 P 细胞的数据来对其进行测试。与其他排序软件一样，P-sort 依赖于非线性降维来聚类尖峰。然而，它还使用简单和复杂尖峰之间的统计关系来合并不同的集群并拆分单个集群。与专家手动管理相比，P-sort 偶尔会识别出更复杂的峰值，并防止集群的错误分配。三个现有的自动分拣机表现不佳，特别是在识别复杂的尖峰时。为了改进小脑分析工具的开发，我们提供了 313 个记录会话的标记数据，以及三个物种中 P 细胞波形和放电模式的统计特征。特别适用于复杂尖峰的识别。为了改进小脑分析工具的开发，我们提供了 313 个记录会话的标记数据，以及三个物种中 P 细胞波形和放电模式的统计特征。特别适用于复杂尖峰的识别。为了改进小脑分析工具的开发，我们提供了 313 个记录会话的标记数据，以及三个物种中 P 细胞波形和放电模式的统计特征。