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Compatibility evaluation of clustering algorithms for contemporary extracellular neural spike sorting

Veerabhadrappa, Rakesh, Ul Hassan, Masood, Zhang, James and Bhatti, Asim 2020, Compatibility evaluation of clustering algorithms for contemporary extracellular neural spike sorting, Frontiers in systems neuroscience, vol. 14, pp. 1-17, doi: 10.3389/fnsys.2020.00034.

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Title Compatibility evaluation of clustering algorithms for contemporary extracellular neural spike sorting
Author(s) Veerabhadrappa, Rakesh
Ul Hassan, Masood
Zhang, JamesORCID iD for Zhang, James orcid.org/0000-0002-8367-9893
Bhatti, AsimORCID iD for Bhatti, Asim orcid.org/0000-0001-6876-1437
Journal name Frontiers in systems neuroscience
Volume number 14
Article ID 34
Start page 1
End page 17
Total pages 17
Publisher Frontiers Media
Place of publication Lausanne, Switzerland
Publication date 2020-06
ISSN 1662-5137
Keyword(s) extracellular
micro-electrode array
spike sorting
clustering
validation indices
Summary 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.
Language eng
DOI 10.3389/fnsys.2020.00034
Indigenous content off
Field of Research 170205 Neurocognitive Patterns and Neural Networks
110902 Cellular Nervous System
080109 Pattern Recognition and Data Mining
1109 Neurosciences
1116 Medical Physiology
0606 Physiology
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30139471

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.