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Automatic Classification of Canine Pontine Neuronal Discharge Patterns using K‐means Clustering
Author(s) -
Zuperku Edward,
Prkic Ivana,
Stucke Astrid,
Miller Justin,
Hopp Francis,
Stuth Eckehard
Publication year - 2015
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.29.1_supplement.1032.6
Subject(s) - cluster analysis , pons , pattern recognition (psychology) , computer science , artificial intelligence , neuroscience , biology
Respiratory‐related neurons in the parabrachial‐Kölliker‐Fuse (PB‐KF) region of the pons play a key role in the control of breathing. The neuronal activities of these pontine respiratory group (PRG) neurons exhibit a variety of inspiratory (I), expiratory (E), phase spanning and non‐respiratory related (NRM) discharge patterns. Due to the variety of patterns, it can be difficult to classify them into distinct subgroups according to their discharge contours. This report presents a method that automatically classifies neurons according to their discharge patterns and derives an average subgroup contour of each class. It is based on the K‐means clustering technique and it is implemented via SigmaPlot User‐Defined transform scripts. The discharge patterns of 135 canine PRG neurons were classified into 7 distinct subgroups. Additional methods for choosing the “optimal” number of clusters are described. Analysis of the results suggests that the k‐means clustering method offers a robust objective means of both automatically categorizing neuron patterns and establishing the underlying archetypical contours of subtypes based on the discharge patterns of group of neurons. Supported by VA grant I01BX000721.