Cluster tendency assessment in neuronal spike data

English version

Autoři: Sara Mahallati ^aff001; James C. Bezdek ^aff004; Milos R. Popovic ^aff001; Taufik A. Valiante ^aff001
Působiště autorů: Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Canada ^aff001; KITE Research Institute, University Health Network, Toronto, Canada ^aff002; Krembil Research Institute, University Health Network, Toronto, Canada ^aff003; Computer Science and Information Systems Departments, University of Melbourne, Melbourne, Australia ^aff004; Division of Neurosurgery, University of Toronto, Toronto, Canada ^aff005; CRANIA, University Health Network and University of Toronto, Toronto, Canada ^aff006
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0224547

Souhrn

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 methods for providing lower dimensional visualization of the cluster structure and on subsequent spike clustering. We also introduce a visualization technique called improved visual assessment of cluster tendency (iVAT) to estimate possible cluster structures in data without the need for dimensionality reduction. Experimental results are conducted on two datasets with ground truth labels. In data with a relatively small number of clusters, iVAT is beneficial in estimating the number of clusters to inform the initialization of clustering algorithms. With larger numbers of clusters, iVAT gives a useful estimate of the coarse cluster structure but sometimes fails to indicate the presumptive number of clusters. We show that noise associated with recording extracellular neuronal potentials can disrupt computational clustering schemes, highlighting the benefit of probabilistic clustering models. Our results show that t-Distributed Stochastic Neighbor Embedding (t-SNE) provides representations of the data that yield more accurate visualization of potential cluster structure to inform the clustering stage. Moreover, The clusters obtained using t-SNE features were more reliable than the clusters obtained using the other methods, which indicates that t-SNE can potentially be used for both visualization and to extract features to be used by any clustering algorithm.

Klíčová slova:

Action potentials – Algorithms – Clustering algorithms – Data visualization – k means clustering – Neurons – principal component analysis – Vision

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Cluster tendency assessment in neuronal spike data

Souhrn

Klíčová slova:

Zdroje

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