An Accurate and Robust Method for Spike Sorting Based on Convolutional Neural Networks
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 imp...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Brain Sciences |
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| Online Access: | https://www.mdpi.com/2076-3425/10/11/835 |
| _version_ | 1797548254194302976 |
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| author | Zhaohui Li Yongtian Wang Nan Zhang Xiaoli Li |
| author_facet | Zhaohui Li Yongtian Wang Nan Zhang Xiaoli Li |
| author_sort | Zhaohui Li |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T14:56:42Z |
| format | Article |
| id | doaj.art-32d1f4c867c547b7ad17085f81e4b15a |
| institution | Directory Open Access Journal |
| issn | 2076-3425 |
| language | English |
| last_indexed | 2024-03-10T14:56:42Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Brain Sciences |
| spelling | doaj.art-32d1f4c867c547b7ad17085f81e4b15a2023-11-20T20:31:37ZengMDPI AGBrain Sciences2076-34252020-11-01101183510.3390/brainsci10110835An Accurate and Robust Method for Spike Sorting Based on Convolutional Neural NetworksZhaohui Li0Yongtian Wang1Nan Zhang2Xiaoli Li3School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, ChinaSchool of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, ChinaSchool of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, ChinaState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, ChinaIn 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.https://www.mdpi.com/2076-3425/10/11/835extracellular recordingspike sortingdeep learningconvolutional neural network |
| spellingShingle | Zhaohui Li Yongtian Wang Nan Zhang Xiaoli Li An Accurate and Robust Method for Spike Sorting Based on Convolutional Neural Networks Brain Sciences extracellular recording spike sorting deep learning convolutional neural network |
| title | An Accurate and Robust Method for Spike Sorting Based on Convolutional Neural Networks |
| title_full | An Accurate and Robust Method for Spike Sorting Based on Convolutional Neural Networks |
| title_fullStr | An Accurate and Robust Method for Spike Sorting Based on Convolutional Neural Networks |
| title_full_unstemmed | An Accurate and Robust Method for Spike Sorting Based on Convolutional Neural Networks |
| title_short | An Accurate and Robust Method for Spike Sorting Based on Convolutional Neural Networks |
| title_sort | accurate and robust method for spike sorting based on convolutional neural networks |
| topic | extracellular recording spike sorting deep learning convolutional neural network |
| url | https://www.mdpi.com/2076-3425/10/11/835 |
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