Semi-supervised spike sorting using pattern matching and a scaled Mahalanobis distance metric

Douglas M. Schwarz*, Muhammad S.A. Zilany, Melissa Skevington, Nicholas J. Huang, Brian C. Flynn, Laurel H. Carney

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)

Abstract

Sorting action potentials (spikes) from tetrode recordings can be time consuming, labor intensive, and inconsistent, depending on the methods used and the experience of the operator. The techniques presented here were designed to address these issues. A feature related to the slope of the spike during repolarization is computed. A small subsample of the features obtained from the tetrode (ca. 10,000-20,000 events) is clustered using a modified version of k-means that uses Mahalanobis distance and a scaling factor related to the cluster size. The cluster-size-based scaling improves the clustering by increasing the separability of close clusters, especially when they are of disparate size. The full data set is then classified from the statistics of the clusters. The technique yields consistent results for a chosen number of clusters. A M. ATLAB implementation is able to classify more than 5000 spikes per second on a modern workstation.

Original languageEnglish
Pages (from-to)120-131
Number of pages12
JournalJournal of Neuroscience Methods
Volume206
Issue number2
DOIs
Publication statusPublished - 15 May 2012
Externally publishedYes

Keywords

  • Classification
  • Clustering
  • Tetrode recording

Fingerprint

Dive into the research topics of 'Semi-supervised spike sorting using pattern matching and a scaled Mahalanobis distance metric'. Together they form a unique fingerprint.

Cite this