Low-latency single channel real-time neural spike sorting system based on template matching
Wang,Pan Ke1,2; Pun,Sio Hang1; Chen,Chang Hao1; McCullagh,Elizabeth A.3; Klug,Achim3; Li,Anan4; Vai,Mang I.1,2; Mak,Peng Un2; Lei,Tim C.1,5
Source PublicationPLoS ONE
AbstractRecent technical advancements in neural engineering allow for precise recording and control of neural circuits simultaneously, opening up new opportunities for closed-loop neural control. In this work, a rapid spike sorting system was developed based on template matching to rapidly calculate instantaneous firing rates for each neuron in a multi-unit extracellular recording setting. Cluster templates were first generated by a desktop computer using a non-parameter spike sorting algorithm (Super-paramagnetic clustering) and then transferred to a field-programmable gate array digital circuit for rapid sorting through template matching. Two different matching techniques–Euclidean distance (ED) and correlational matching (CM)–were compared for the accuracy of sorting and the performance of calculating firing rates. The performance of the system was first verified using publicly available artificial data and was further confirmed with pre-recorded neural spikes from an anesthetized Mongolian gerbil. Real-time recording and sorting from an awake mouse were also conducted to confirm the system performance in a typical behavioral neuroscience experimental setting. Experimental results indicated that high sorting accuracies were achieved for both template-matching methods, but CM can better handle spikes with non-Gaussian spike distributions, making it more robust for in vivo recording. The technique was also compared to several other off-line spike sorting algorithms and the results indicated that the sorting accuracy is comparable but sorting time is significantly shorter than these other techniques. A low sorting latency of under 2 ms and a maximum spike sorting rate of 941 spikes/second have been achieved with our hybrid hardware/software system. The low sorting latency and fast sorting rate allow future system developments of neural circuit modulation through analyzing neural activities in real-time.
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Cited Times [WOS]:2   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorPun,Sio Hang
Affiliation1.State Key Laboratory of Analog and Mixed-Signal VLSI,Institute of Microelectronics,University of Macau,Macao
2.Department of Electrical and Computer Engineering,Faculty of Science and Technology,University of Macau,Macao
3.Department of Physiology and Biophysics,University of Colorado Anschutz Medical Campus,Aurora,United States
4.Jiangsu Key Laboratory of Brain Disease and Bioinformation,Research Center for Biochemistry and Molecular Biology,Xuzhou Medical University,Xuzhou,China
5.Department of Electrical Engineering,University of Colorado,Denver,United States
First Author AffilicationUniversity of Macau;  Faculty of Science and Technology
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Wang,Pan Ke,Pun,Sio Hang,Chen,Chang Hao,et al. Low-latency single channel real-time neural spike sorting system based on template matching[J]. PLoS ONE,2019,14(11).
APA Wang,Pan Ke,Pun,Sio Hang,Chen,Chang Hao,McCullagh,Elizabeth A.,Klug,Achim,Li,Anan,Vai,Mang I.,Mak,Peng Un,&Lei,Tim C..(2019).Low-latency single channel real-time neural spike sorting system based on template matching.PLoS ONE,14(11).
MLA Wang,Pan Ke,et al."Low-latency single channel real-time neural spike sorting system based on template matching".PLoS ONE 14.11(2019).
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