Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering
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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The reduction in TNF alpha validates the design capability to activate the cholinergic anti-inflammatory pathway.
“validating the design capability to activate the cholinergic anti-inflammatory pathway [19]”
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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
- ORCID
- Quiroga, R. Quian
- 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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Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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