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An Accurate and Robust Method for Spike Sorting Based on Convolutional Neural Networks

1
School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
2
Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China
3
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Received: 9 October 2020 / Revised: 29 October 2020 / Accepted: 9 November 2020 / Published: 11 November 2020
(This article belongs to the Section Neural Engineering, Neuroergonomics and Neurorobotics)
In the fields of neuroscience and biomedical signal processing, spike sorting is a crucial step to extract the information of single neurons from extracellular recordings. In this paper, we propose a novel deep learning approach based on one-dimensional convolutional neural networks (1D-CNNs) to implement accurate and robust spike sorting. The results of the simulated data demonstrated that the clustering accuracy in most datasets was greater than 99%, despite the multiple levels of noise and various degrees of overlapped spikes. Moreover, the proposed method performed significantly better than the state-of-the-art method named “WMsorting” and a deep-learning-based multilayer perceptron (MLP) model. In addition, the experimental data recorded from the primary visual cortex of a macaque monkey were used to evaluate the proposed method in a practical application. It was shown that the method could successfully isolate most spikes of different neurons (ranging from two to five) by training the 1D-CNN model with a small number of manually labeled spikes. Considering the above, the deep learning method proposed in this paper is of great advantage for spike sorting with high accuracy and strong robustness. It lays the foundation for application in more challenging works, such as distinguishing overlapped spikes and the simultaneous sorting of multichannel recordings. View Full-Text
Keywords: extracellular recording; spike sorting; deep learning; convolutional neural network extracellular recording; spike sorting; deep learning; convolutional neural network
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MDPI and ACS Style

Li, Z.; Wang, Y.; Zhang, N.; Li, X. An Accurate and Robust Method for Spike Sorting Based on Convolutional Neural Networks. Brain Sci. 2020, 10, 835. https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci10110835

AMA Style

Li Z, Wang Y, Zhang N, Li X. An Accurate and Robust Method for Spike Sorting Based on Convolutional Neural Networks. Brain Sciences. 2020; 10(11):835. https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci10110835

Chicago/Turabian Style

Li, Zhaohui, Yongtian Wang, Nan Zhang, and Xiaoli Li. 2020. "An Accurate and Robust Method for Spike Sorting Based on Convolutional Neural Networks" Brain Sciences 10, no. 11: 835. https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci10110835

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