Abstract
Having the ability to study the activity of single neurons will facilitate studies in many areas including cognitive sciences and brain computer interface applications. Due to the fact that every neuron has it’s own unique spike waveform, by applying spike-sorting methods, one can separate neurons based on their associated spike. Spike sorting is an unsupervised learning problem in the realm of data mining and machine learning. In this study, a new method that will improve the accuracy of spike sorting in comparison to existing methods has been introduced. This method, which is named Multi Cluster Feature Selection (MCFS), will designate a reduced number of features from the original data set that will best differentiate the existing clusters through solving a Lasso optimization problem. MCFS, was also applied to data obtained from multi-channel recordings on a rat’s brain. With MCFS, each channel was studied and neurons in each channel were sorted with an improved rate in comparison to conventional methods such as PCA.
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