Quiroga, R. Quian and Nadasdy, Z. and Ben-Shaul, Y. (2004) Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering. Neural Computation, 16 (8). pp. 1661-1687. ISSN 0899-7667 http://resolver.caltech.edu/CaltechAUTHORS:QUInc04
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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.
|Additional Information:||© 2004 Massachusetts Institute of Technology. Received December 3, 2002; accepted January 30, 2004; posted online March 13, 2006. We are very thankful to Richard Andersen and Christof Koch for support and advice. We also acknowledge very useful discussions with Noam Shental, Moshe Abeles, Ofer Mazor, Bijan Pesaran, and Gabriel Kreiman. We are in debt to Eytan Domany for providing us the SPC code and to Alon Nevet who provided the original spike data for the simulation. This work was supported by the Sloan-Swartz foundation and DARPA.|
|Subject Keywords:||EVENT-RELATED POTENTIALS; CLASSIFICATION; DECOMPOSITION; ALGORITHMS; TRANSFORM; PACKETS; MODEL|
|Usage Policy:||No commercial reproduction, distribution, display or performance rights in this work are provided.|
|Deposited By:||Jason Perez|
|Deposited On:||17 Jun 2009 21:58|
|Last Modified:||26 Dec 2012 10:54|
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