Abstract
Sorting of the extracellularly recorded spikes is a basic prerequisite for analysis of the cooperative neural behavior and neural code. Fundamentally the sorting performance is defined by the quality of discriminative features extracted from spike waveforms. Here we discuss two features extraction approaches: principal component analysis (PCA), and wavelet transform (WT). We show that only when properly tuned to the data, the WT technique may outperform PCA. We present a novel method for extraction of spike features based on a combination of PCA and continuous WT. The method automatically tunes its WT part to the data structure making use of knowledge obtained by PCA. We demonstrate the method on simulated and experimental data sets.
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Abbreviations
- PCA:
-
principal component analysis
- WF:
-
wave form
- WSAC:
-
wavelet shape-accounting classifier
- WSPC:
-
wavelet-classifier with superparamagnetic clustering
- WSC:
-
wavelet-based spike classifier
- WT:
-
wavelet transform
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Acknowledgements
This work has been supported in part by the European Union under ROSANA project (EU-IST-2001-34892), by a project from Universidad Complutense (PR1/06-14482-B), by the Spanish Ministry of Education and Science under the program Ramon y Cajal, and by the program BRHE from CRDF and RF Ministry of Education and Science (grant Y1-P-06-06).
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Pavlov, A., Makarov, V.A., Makarova, I. et al. Sorting of neural spikes: When wavelet based methods outperform principal component analysis. Nat Comput 6, 269–281 (2007). https://doi.org/10.1007/s11047-006-9014-8
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DOI: https://doi.org/10.1007/s11047-006-9014-8