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Article

Deep Learning-Based Template Matching Spike Classification for Extracellular Recordings

by 1,†, 1,†, 1, 1, 2,3,4,5, 2,3 and 1,*
1
School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea
2
Department of Medical Science, College of Medicine, Catholic Kwandong University, Gangneung 25601, Korea
3
Translational Brain Research Center, Catholic Kwandong University, International St. Mary’s Hospital, Incheon 22711, Korea
4
Department of Neuroscience, University of Science & Technology, Daejeon 34113, Korea
5
Center for Neuroscience, Korea Institute of Science and Technology, Seoul 02792, Korea
*
Author to whom correspondence should be addressed.
Equally contributing authors.
Appl. Sci. 2020, 10(1), 301; https://doi.org/10.3390/app10010301
Received: 12 November 2019 / Revised: 20 December 2019 / Accepted: 30 December 2019 / Published: 31 December 2019
(This article belongs to the Section Applied Biosciences and Bioengineering)
We propose a deep learning-based spike sorting method for extracellular recordings. For analysis of extracellular single unit activity, the process of detecting and classifying action potentials called “spike sorting” has become essential. This is achieved through distinguishing the morphological differences of the spikes from each neuron, which arises from the differences of the surrounding environment and characteristics of the neurons. However, cases of high structural similarity and noise make the task difficult. And for manual spike sorting, it requires professional knowledge along with extensive time cost and suffers from human bias. We propose a deep learning-based spike sorting method on extracellular recordings from a single electrode that is efficient, robust to noise, and accurate. In circumstances where labelled data does not exist, we created pseudo-labels through principal component analysis and K-means clustering to be used for multi-layer perceptron training and built high performing spike classification model. When tested, our model outperformed conventional methods by 2.1% on simulation data of various noise levels, by 6.0% on simulation data of various clusters count, and by 1.7% on in-vivo data. As a result, we showed that the deep learning-based classification can classify spikes from extracellular recordings, even showing high classification accuracy on spikes that are difficult even for manual classification. View Full-Text
Keywords: spike classification; extracellular recordings; deep learning; multi-layer perceptron; template matching; clustering spike classification; extracellular recordings; deep learning; multi-layer perceptron; template matching; clustering
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MDPI and ACS Style

Park, I.Y.; Eom, J.; Jang, H.; Kim, S.; Park, S.; Huh, Y.; Hwang, D. Deep Learning-Based Template Matching Spike Classification for Extracellular Recordings. Appl. Sci. 2020, 10, 301. https://doi.org/10.3390/app10010301

AMA Style

Park IY, Eom J, Jang H, Kim S, Park S, Huh Y, Hwang D. Deep Learning-Based Template Matching Spike Classification for Extracellular Recordings. Applied Sciences. 2020; 10(1):301. https://doi.org/10.3390/app10010301

Chicago/Turabian Style

Park, In Y., Junsik Eom, Hanbyol Jang, Sewon Kim, Sanggeon Park, Yeowool Huh, and Dosik Hwang. 2020. "Deep Learning-Based Template Matching Spike Classification for Extracellular Recordings" Applied Sciences 10, no. 1: 301. https://doi.org/10.3390/app10010301

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