A Fully Automated Approach to Spike Sorting

Jason E. Chung, Jeremy F. Magland, Alex H. Barnett, Vanessa M. Tolosa, Angela C. Tooker, Kye Y. Lee, Kedar G. Shah, Sarah H. Felix, Loren M. Frank, Leslie F. Greengard

Research output: Contribution to journalArticlepeer-review


Understanding the detailed dynamics of neuronal networks will require the simultaneous measurement of spike trains from hundreds of neurons (or more). Currently, approaches to extracting spike times and labels from raw data are time consuming, lack standardization, and involve manual intervention, making it difficult to maintain data provenance and assess the quality of scientific results. Here, we describe an automated clustering approach and associated software package that addresses these problems and provides novel cluster quality metrics. We show that our approach has accuracy comparable to or exceeding that achieved using manual or semi-manual techniques with desktop central processing unit (CPU) runtimes faster than acquisition time for up to hundreds of electrodes. Moreover, a single choice of parameters in the algorithm is effective for a variety of electrode geometries and across multiple brain regions. This algorithm has the potential to enable reproducible and automated spike sorting of larger scale recordings than is currently possible. Chung, Magland, et al. present MountainSort, a new fully automatic spike sorting package with a powerful GUI. MountainSort has accuracy comparable to current methods and runtimes faster than real time, enabling automatic and reproducible spike sorting for high-density extracellular recordings.

Original languageEnglish (US)
Pages (from-to)1381-1394.e6
Issue number6
StatePublished - Sep 13 2017


  • automated
  • cluster metrics
  • clustering
  • cortex
  • hippocampus
  • reproducibility
  • spike sorting

ASJC Scopus subject areas

  • General Neuroscience


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