Spike sorting based upon machine learning algorithms (SOMA)

• Published in: Journal of neuroscience methods Vol. 160; no. 1; pp. 52 - 68
• Main Authors: Horton, P.M., Nicol, A.U., Kendrick, K.M., Feng, J.F.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 15.02.2007
• Subjects: Action Potentials - physiology; Algorithms; Animals; Artificial Intelligence; Cerebral Cortex - cytology; Computer Simulation; Models, Neurological; Neural networks; Neurons - physiology; Odour; Olfactory bulb; Olfactory Bulb - cytology; Pre-processing; Rats; Self-organising maps; Sheep; Sheep temporal cortex; Signal Processing, Computer-Assisted; Spike sorting; Spike sorting; Neural networks; Sheep temporal cortex; Pre-processing; Olfactory bulb; Self-organising maps; Odour
• ISSN: 0165-0270; 1872-678X
• DOI: 10.1016/j.jneumeth.2006.08.013

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 sofware is available at http://www.sussex.ac.uk/Users/pmh20/spikes.