Abstract
Recent technological advancements in high-density multi-channel electrodes have made it possible to record large numbers of neurons from previously inaccessible regions. While the performance of automated spike-sorters has been assessed in recordings from cortex, dentate gyrus, and thalamus, the most effective and efficient approach for spike-sorting can depend on the target region due to differing morphological and physiological characteristics. We therefore assessed the performance of five commonly used sorting packages, Kilosort3, MountainSort5, Tridesclous, SpyKING CIRCUS, and IronClust, in recordings from the rostral ventromedial medulla, a region that has been characterized using single-electrode recordings but that is essentially unexplored at the high-density network level. As demonstrated in other brain regions, each sorter produced unique results. Manual curation preferentially eliminated units detected by only one sorter. Kilosort3 and IronClust required the least curation while maintaining the largest number of units, whereas SpyKING CIRCUS and MountainSort5 required substantial curation. Tridesclous consistently identified the smallest number of units. Nonetheless, all sorters successfully identified classically defined RVM physiological cell types. These findings suggest that while the level of manual curation needed may vary across sorters, each can extract meaningful data from this deep brainstem site.