Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering

R. Quian Quiroga*, Z. Nadasdy, Y. Ben-Shaul

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1511 Scopus citations

Abstract

This study introduces a new method for detecting and sorting spikes from multiunit recordings. The method combines the wavelet transform, which localizes distinctive spike features, with superparamagnetic clustering, which allows automatic classification of the data without assumptions such as low variance or gaussian distributions. Moreover, an improved method for setting amplitude thresholds for spike detection is proposed. We describe several criteria for implementation that render the algorithm unsupervised and fast. The algorithm is compared to other conventional methods using several simulated data sets whose characteristics closely resemble those of in vivo recordings. For these data sets, we found that the proposed algorithm outperformed conventional methods.

Original languageAmerican English
Pages (from-to)1661-1687
Number of pages27
JournalNeural Computation
Volume16
Issue number8
DOIs
StatePublished - Aug 2004

Fingerprint

Dive into the research topics of 'Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering'. Together they form a unique fingerprint.

Cite this