小柯机器人

美国霍华德·休斯医学研究所Marius Pachitariu等研究人员使用Kilosort4进行脉冲分拣。2024年4月8日，国际知名学术期刊《自然—方法学》在线发表了这一成果。

研究人员表示，脉冲分拣是从局部电场记录中提取单个神经元放电时间的计算过程。这是神经科学中一个重要而又困难的问题，由于记录的非稳态性和附近神经元之间密集的电场重叠，这个问题变得更加复杂。

为了解决脉冲分拣问题，研究人员公开开发了Kilosort框架。研究人员介绍了Kilosort不同版本中引入的各种算法步骤。研究人员还报告了Kilosort4的开发情况，由于采用了基于图的聚类算法，Kilosort4的性能得到了大幅提升。为了测试Kilosort的性能，研究人员开发了一个逼真的模拟框架，该框架使用真实实验中的密集采样电场来生成非稳态脉冲波形和逼真的噪声。

研究人员发现，几乎所有版本的Kilosort在各种模拟条件下的表现都优于其他算法，而Kilosort4在所有情况下的表现都最好，它甚至能在高漂移条件下正确识别低振幅和小空间范围的神经元。

附：英文原文

Title: Spike sorting with Kilosort4

Author: Pachitariu, Marius, Sridhar, Shashwat, Pennington, Jacob, Stringer, Carsen

Issue&Volume: 2024-04-08

Abstract: Spike sorting is the computational process of extracting the firing times of single neurons from recordings of local electrical fields. This is an important but hard problem in neuroscience, made complicated by the nonstationarity of the recordings and the dense overlap in electrical fields between nearby neurons. To address the spike-sorting problem, we have been openly developing the Kilosort framework. Here we describe the various algorithmic steps introduced in different versions of Kilosort. We also report the development of Kilosort4, a version with substantially improved performance due to clustering algorithms inspired by graph-based approaches. To test the performance of Kilosort, we developed a realistic simulation framework that uses densely sampled electrical fields from real experiments to generate nonstationary spike waveforms and realistic noise. We found that nearly all versions of Kilosort outperformed other algorithms on a variety of simulated conditions and that Kilosort4 performed best in all cases, correctly identifying even neurons with low amplitudes and small spatial extents in high drift conditions.

DOI: 10.1038/s41592-024-02232-7

Source: https://www.nature.com/articles/s41592-024-02232-7

Nature Methods：《自然—方法学》，创刊于2004年。隶属于施普林格·自然出版集团，最新IF：47.99
官方网址：https://www.nature.com/nmeth/
投稿链接：https://mts-nmeth.nature.com/cgi-bin/main.plex