Spike sorting for large, dense electrode arrays

Cyrille Rossant, Shabnam N. Kadir, Dan F.M. Goodman, John Schulman, Maximilian L.D. Hunter, Aman B. Saleem, Andres Grosmark, Mariano Belluscio, George H. Denfield, Alexander S. Ecker, Andreas S. Tolias, Samuel Solomon, György Buzski, Matteo Carandini, Kenneth D. Harris

Research output: Contribution to journalArticlepeer-review

289 Citations (Scopus)


Developments in microfabrication technology have enabled the production of neural electrode arrays with hundreds of closely spaced recording sites, and electrodes with thousands of sites are under development. These probes in principle allow the simultaneous recording of very large numbers of neurons. However, use of this technology requires the development of techniques for decoding the spike times of the recorded neurons from the raw data captured from the probes. Here we present a set of tools to solve this problem, implemented in a suite of practical, user-friendly, open-source software. We validate these methods on data from the cortex, hippocampus and thalamus of rat, mouse, macaque and marmoset, demonstrating error rates as low as 5%.

Original languageEnglish
Pages (from-to)634-641
Number of pages8
JournalNature Neuroscience
Issue number4
Publication statusPublished - 29 Mar 2016


Dive into the research topics of 'Spike sorting for large, dense electrode arrays'. Together they form a unique fingerprint.

Cite this