PT - JOURNAL ARTICLE AU - Marius Pachitariu AU - Nicholas Steinmetz AU - Shabnam Kadir AU - Matteo Carandini AU - Harris Kenneth D. TI - Kilosort: realtime spike-sorting for extracellular electrophysiology with hundreds of channels AID - 10.1101/061481 DP - 2016 Jan 01 TA - bioRxiv PG - 061481 4099 - http://biorxiv.org/content/early/2016/06/30/061481.short 4100 - http://biorxiv.org/content/early/2016/06/30/061481.full AB - Advances in silicon probe technology mean that in vivo electrophysiological recordings from hundreds of channels will soon become commonplace. To interpret these recordings we need fast, scalable and accurate methods for spike sorting, whose output requires minimal time for manual curation. Here we introduce Kilosort, a spike sorting framework that meets these criteria, and show that it allows rapid and accurate sorting of large-scale in vivo data. Kilosort models the recorded voltage as a sum of template waveforms triggered on the spike times, allowing overlapping spikes to be identified and resolved. Rapid processing is achieved thanks to a novel low-dimensional approximation for the spatiotemporal distribution of each template, and to batch-based optimization on GPUs. A novel post-clustering merging step based on the continuity of the templates substantially reduces the requirement for subsequent manual curation operations. We compare Kilosort to an established algorithm on data obtained from 384-channel electrodes, and show superior performance, at much reduced processing times. Data from 384-channel electrode arrays can be processed in approximately realtime. Kilosort is an important step towards fully automated spike sorting of multichannel electrode recordings, and is freely available (github.com/cortex-lab/Kilosort).