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 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.
Highlights
Recording of extracellular signatures of action potentials, referred to as spikes, is a standard tool for revealing the activity of populations of individual neurons
Our results indicate that improved visual assessment of cluster tendency (iVAT) often suggests a most reasonable estimate for the primary cluster structure, while t-Distributed Stochastic Neighbor Embedding (t-SNE) is often capable of displaying finer cluster structure
We provide an objective measure of comparison between t-SNE and the other methods, we evaluate the quality of partitions obtained by clustering in the upspace and in the five two-dimensional representations
Summary
Recording of extracellular signatures of action potentials, referred to as spikes, is a standard tool for revealing the activity of populations of individual neurons (single units). Poor sorting quality results in biased cross-correlation estimates of the spiking activity of the different identified units [3]. It has been estimated that single or tetrode type electrodes (i.e. impedance< 100KO) can record neuronal activity within a spherical volume of 50 μm radius with amplitudes large enough to be detected (> 60μV). This volume of brain tissue constitutes about 100 neurons. Other methods using high density electrode arrays reported simulations with no more than 10 units [8, 9]
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