Cited by 2,119Neuroscience and Neural EngineeringNeural dynamics and brain functionEEG and Brain-Computer Interfaces

Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering

Quiroga, R. Quian, Nadasdy, Z., Ben-Shaul, Y.·2004·Neural Computation

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.

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Crossref · 10.1162/089976604774201631

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23 of 23 references verified0 citation errors · 0 not checkable

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Authors
Quiroga, R. Q., Nadasdy, Z., & Ben-Shaul, Y.
Affiliations
  • California Institute of Technology (US)
  • Division of Biology, California Institute of Technology, Pasadena, CA 91125, U.S.A.
  • ICNC, Hebrew University, Jerusalem, Israel
Venue
Neural Computation
Publisher
MIT Press
Published
2004-08-01
Volume
16
Issue
8
Pages
1661-1687
ISSN
  • 0899-7667 (ISSN-L)
  • 1530-888X
OpenAlex
W2136104104
Cited by
2,119
References
23
Citations by year
2026: 81 · 2025: 100 · 2024: 97 · 2023: 98 · 2022: 129 · 2021: 119 · 2020: 129 · 2019: 151 · 2018: 168 · 2017: 150 · 2016: 143 · 2015: 149 · 2014: 133 · 2013: 98 · 2012: 81
FWCI
8.20
Type
journal article
Language
en
Open access
closed
Locations
  • Neural Computation (publishedVersion)
  • PubMed (publishedVersion)
  • CaltechAUTHORS (California Institute of Technology) (submittedVersion)
  • Leicester Research Archive (University of Leicester) (submittedVersion)
Topics
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
Keywords
spike detection, spike sorting, multi-unit recording, wavelet transform, unsupervised clustering, amplitude thresholding, neural signal processing
MeSH terms
Action Potentials, Algorithms, Computer Simulation, Models, Neurological, Neurons

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