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Cluster tendency assessment in neuronal spike data
- Source :
- PLoS ONE, PLoS ONE, Vol 14, Iss 11, p e0224547 (2019)
- Publication Year :
- 2019
- Publisher :
- Public Library of Science, 2019.
-
Abstract
- 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 c 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.
- Subjects :
- Computer science
Physiology
Vision
Initialization
Action Potentials
Social Sciences
Pattern Recognition, Automated
0302 clinical medicine
Mathematical and Statistical Techniques
Animal Cells
Medicine and Health Sciences
Psychology
Cluster Analysis
Neurons
0303 health sciences
Principal Component Analysis
Multidisciplinary
Applied Mathematics
Simulation and Modeling
Statistics
k-means clustering
Sorting
Signal Processing, Computer-Assisted
Electrophysiology
Spike sorting
Feature (computer vision)
Principal component analysis
Physical Sciences
Unsupervised learning
Embedding
Medicine
Sensory Perception
Cellular Types
Algorithms
Research Article
Computer and Information Sciences
Science
Feature extraction
Models, Neurological
Neurophysiology
Research and Analysis Methods
Membrane Potential
03 medical and health sciences
Clustering Algorithms
Cluster (physics)
Computer Simulation
Statistical Methods
Cluster analysis
030304 developmental biology
business.industry
Dimensionality reduction
Data Visualization
Biology and Life Sciences
Pattern recognition
Cell Biology
Visualization
ComputingMethodologies_PATTERNRECOGNITION
Cellular Neuroscience
Multivariate Analysis
Artificial intelligence
K Means Clustering
business
030217 neurology & neurosurgery
Mathematics
Neuroscience
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 14
- Issue :
- 11
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....5605cbd3295c80e6834f4a846ecc8598