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Computational Intelligence and Neuroscience - Volume 2014 2014, Article ID 643059, 12 pages -

Research ArticleSchool of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA

Received 16 September 2013; Revised 20 December 2013; Accepted 7 February 2014; Published 16 April 2014

Academic Editor: Wei Wu

Copyright © 2014 Brett A. Matthews and Mark A. Clements. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper details a novel probabilistic method for automatic neural spike sorting which uses stochastic point process models of neural spike trains and parameterized action potential waveforms. A novel likelihood model forobserved firing times as the aggregation of hidden neural spike trains is derived, as well as an iterative procedure for clustering the data and finding the parameters that maximize the likelihood. The method is executed and evaluated on both a fully labeled semiartificial dataset and a partially labeled real dataset of extracellular electric traces from rat hippocampus. In conditions of relatively high difficulty i.e., with additive noise and with similar action potential waveform shapes for distinct neurons the method achieves significant improvements in clustering performance over a baseline waveform-only Gaussian mixture model GMM clustering on the semiartificial set 1.98% reduction in error rate and outperforms both the GMM and a state-of-the-art method on the real dataset 5.04% reduction in false positive + false negative errors. Finally, an empirical study of two free parameters for our method is performed on the semiartificial dataset.





Autor: Brett A. Matthews and Mark A. Clements

Fuente: https://www.hindawi.com/



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