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Optimal feature subset selection for neuron spike sorting using the genetic algorithm

It is crucial for a neuron spike sorting algorithm to cluster data from different neurons efficiently. In this study, the search capability of the Genetic Algorithm (GA) is exploited for identifying the optimal feature subset for neuron spike sorting with a clustering algorithm. Two important objectives of the optimization process are considered: to reduce the number of features and increase the clustering performance. Specifically, we employ a binary GA with the silhouette evaluation criterion as the fitness function for neuron spike sorting using the Super-Paramagnetic Clustering (SPC) algorithm. The clustering results of SPC with and without the GA-based feature selector are evaluated using benchmark synthetic neuron spike data sets. The outcome indicates the usefulness of the GA in identifying a smaller feature set with improved clustering performance.

History

Event

Neural Information Processing. Conference (22nd : 2015 : Istanbul, Turkey)

Volume

9490

Series

Lecture Notes in Computer Science

Pagination

364 - 370

Publisher

Springer

Location

Istanbul, Turkey

Place of publication

New York, N.Y.

Start date

2015-11-09

End date

2015-11-12

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783319265346

Language

eng

Publication classification

E Conference publication; E1 Full written paper - refereed

Copyright notice

2015, Springer

Title of proceedings

ICONIP 2015 : Neural information processing : 22nd International Conference, ICONIP 2015, Istanbul, Turkey, November 9-12, 2015 : proceedings