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 wave let transform, which localizes distinctive spike features, with super paramagnetic 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.
Cite
Text
Quiroga et al. "Unsupervised Spike Detection and Sorting with Wavelets and Superparamagnetic Clustering." Neural Computation, 2004. doi:10.1162/089976604774201631Markdown
[Quiroga et al. "Unsupervised Spike Detection and Sorting with Wavelets and Superparamagnetic Clustering." Neural Computation, 2004.](https://mlanthology.org/neco/2004/quiroga2004neco-unsupervised/) doi:10.1162/089976604774201631BibTeX
@article{quiroga2004neco-unsupervised,
title = {{Unsupervised Spike Detection and Sorting with Wavelets and Superparamagnetic Clustering}},
author = {Quiroga, Rodrigo Quian and Nadasdy, Zoltan and Ben-Shaul, Yoram},
journal = {Neural Computation},
year = {2004},
pages = {1661-1687},
doi = {10.1162/089976604774201631},
volume = {16},
url = {https://mlanthology.org/neco/2004/quiroga2004neco-unsupervised/}
}