Cluster tendency assessment in neuronal spike data.
Sara Mahallati, James C Bezdek, Milos R Popovic, Taufik A Valiante
PLoS One | Public Library of Science (PLoS) | Published : 2019
Sorting spikes from extracellular recording into clusters associated with distinct single units (putative neurons) is a fundamental step in analyzing neuronal populations. Such spike sorting is intrinsically unsupervised, as the number of neurons are not known a priori. Therefor, any spike sorting is an unsupervised learning problem that requires either of the two approaches: specification of a fixed value k for the number of clusters to seek, or generation of candidate partitions for several possible values of c, followed by selection of a best candidate based on various post-clustering validation criteria. In this paper, we investigate the first approach and evaluate the utility of several..View full abstract