Compatibility Evaluation of Clustering Algorithms for Contemporary Extracellular Neural Spike Sorting

Abstract

Deciphering useful information from electrophysiological data recorded from the brain, in-vivo or in-vitro, is dependent on the capability to analyse spike patterns efficiently and accurately. The spike analysis mechanisms are heavily reliant on the clustering algorithms that enable separation of spike trends based on their spatio-temporal behaviors. Literature review report several clustering algorithms over decades focused on different applications. Although spike analysis algorithms employ only a small subset of clustering algorithms, however, not much work has been reported on the compliance and suitability of such clustering algorithms for spike analysis. In our study, we have attempted to comment on the suitability of available clustering algorithms and performance capacity when exposed to spike analysis. In this regard, the study reports a compatibility evaluation on algorithms previously employed in spike sorting as well as the algorithms yet to be investigated for application in sorting neural spikes. The performance of the algorithms is compared in terms of their accuracy, confusion matrix and accepted validation indices. Three data sets comprising of easy, difficult, and real spike similarity with known ground-truth are chosen for assessment, ensuring a uniform testbed. The procedure also employs two feature-sets, principal component analysis and wavelets. The report also presents a statistical score scheme to evaluate the performance individually and overall. The open nature of the data sets, the clustering algorithms and the evaluation criteria make the proposed evaluation framework widely accessible to the research community. We believe that the study presents a reference guide for emerging neuroscientists to select the most suitable algorithms for their spike analysis requirements.

Identification and Evaluation of Effective Strategies in a Dynamic Visual Task Using Eye Gaze Dynamics

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Efficient neural spike sorting using data subdivision and unification

“…The unsupervised clustering is more reliable and useful when there is no prior knowledge about clusters [53]. The spike sorting algorithms are mainly used offline and are implemented for behavioural quantification on pre-recorded neural datasets [54]. However,…”

“…researchers have developed online spike sorting algorithms that can quantify spike-clusters on live neural recordings [55]. The latest state of art in spike sorting process is presented in [56].…”

An automatic spike sorting algorithm based on adaptive spike detection and a mixture of skew-t distributions

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