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Spike sorting based upon machine learning algorithms (SOMA)
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Horton, P. M., Nicol, A. U., Kendrick, K. M. and Feng, Jianfeng. (2007) Spike sorting based upon machine learning algorithms (SOMA). Journal of Neuroscience Methods, Vol.160 (No.1). pp. 52-68. ISSN 0165-0270
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Official URL: http://dx.doi.org/10.1016/j.jneumeth.2006.08.013
Abstract
We have developed a spike sorting method, using a combination of various machine learning algorithms, to analyse electrophysiological data and automatically determine the number of sampled neurons from an individual electrode, and discriminate their activities. We discuss extensions to a standard unsupervised learning algorithm (Kohonen), as using a simple application of this technique would only identify a known number of clusters. Our extra techniques automatically identify the number of clusters within the dataset, and their sizes, thereby reducing the chance of misclassification. We also discuss a new pre-processing technique, which transforms the data into a higher dimensional feature space revealing separable clusters. Using principal component analysis (PCA) alone may not achieve this. Our new approach appends the features acquired using PCA with features describing the geometric shapes that constitute a spike waveform. To validate our new spike sorting approach, we have applied it to multi-electrode array datasets acquired from the rat olfactory bulb, and from the sheep infero-temporal cortex, and using simulated data. The SOMA solware is available at http://www.sussex.ac.uk/Users/pmh20/spikes. (c) 2006 Elsevier B.V. All rights reserved.
| Item Type: | Journal Article |
|---|---|
| Subjects: | Q Science > QD Chemistry R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry |
| Divisions: | Faculty of Science > Centre for Scientific Computing Faculty of Science > Computer Science |
| Journal or Publication Title: | Journal of Neuroscience Methods |
| Publisher: | Elsevier BV |
| ISSN: | 0165-0270 |
| Date: | 15 February 2007 |
| Volume: | Vol.160 |
| Number: | No.1 |
| Number of Pages: | 17 |
| Page Range: | pp. 52-68 |
| Identification Number: | 10.1016/j.jneumeth.2006.08.013 |
| Status: | Peer Reviewed |
| Publication Status: | Published |
| Access rights to Published version: | Restricted or Subscription Access |
| URI: | http://wrap.warwick.ac.uk/id/eprint/32390 |
Data sourced from Thomson Reuters' Web of Knowledge
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