RT Journal Article
SR Electronic
T1 Kilosort: realtime spike-sorting for extracellular electrophysiology with hundreds of channels
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 061481
DO 10.1101/061481
A1 Marius Pachitariu
A1 Nicholas Steinmetz
A1 Shabnam Kadir
A1 Matteo Carandini
A1 Harris Kenneth D.
YR 2016
UL http://biorxiv.org/content/early/2016/06/30/061481.abstract
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).