Low-latency single channel real-time neural spike sorting system based on template matching


Autoři: Pan Ke Wang aff001;  Sio Hang Pun aff001;  Chang Hao Chen aff001;  Elizabeth A. McCullagh aff003;  Achim Klug aff003;  Anan Li aff004;  Mang I. Vai aff001;  Peng Un Mak aff002;  Tim C. Lei aff001
Působiště autorů: State Key Laboratory of Analog and Mixed-Signal VLSI, Institute of Microelectronics, University of Macau, Macau, China aff001;  Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, China aff002;  Department of Physiology and Biophysics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America aff003;  Jiangsu Key Laboratory of Brain Disease and Bioinformation, Research Center for Biochemistry and Molecular Biology, Xuzhou Medical University, Xuzhou, China aff004;  Department of Electrical Engineering, University of Colorado, Denver, CO, United States of America aff005
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0225138

Souhrn

Recent 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.

Klíčová slova:

Action potentials – Algorithms – Computer hardware – Electrode recording – Electrodes – Gaussian noise – Mice – Neurons


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