Robust detection of neural spikes using sparse coding based features

Zuozhi Liu; Xiaotian Wang; Quan Yuan; Zuozhi Liu; Xiaotian Wang; Quan Yuan

doi:10.3934/mbe.2020235

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 4: 4257-4270. doi: 10.3934/mbe.2020235

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Robust detection of neural spikes using sparse coding based features

1.
School of Mathematics and Statistics, Guizhou University of Finance and Economics, Guiyang, Guizhou, 550025, China
2.
School of Artificial Intelligence, Xidian University, Xi'an, Shaanxi, 710071, China
3.
Finance Department, Guizhou University of Finance and Economics, Guiyang, Guizhou, 550025, China

Received: 26 March 2020 Accepted: 02 June 2020 Published: 15 June 2020

The detection of neural spikes plays an important role in studying and processing extracellular recording signals, which promises to be able to extract the necessary spike data for all subsequent analyses. The existing algorithms for spike detection have achieved great progress but there still remains much room for improvement in terms of the robustness to noise and the flexibility in the spike shape. To address this issue, this paper presents a novel method for spike detection based on the theory of sparse representation. By analyzing the characteristics of extracellular neural recordings, a targetdriven sparse representation framework is firstly constructed, with which the neural spike signals can be effectively separated from background noise. In addition, considering the fact that the spikes emitted by different neurons have different shapes, we then learn a universal dictionary to give a sparse representation of various spike signals. Finally, the information (location and number) of spikes in the recorded signal are achieved by comprehensively analyzing the sparse features. Experimental results demonstrate that the proposed method outperforms the existing methods in the spike detection problem.
- neural spike detection,
- sparse representation,
- dictionary learning,
- sparse feature
Citation: Zuozhi Liu, Xiaotian Wang, Quan Yuan. Robust detection of neural spikes using sparse coding based features[J]. Mathematical Biosciences and Engineering, 2020, 17(4): 4257-4270. doi: 10.3934/mbe.2020235

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Abstract

The detection of neural spikes plays an important role in studying and processing extracellular recording signals, which promises to be able to extract the necessary spike data for all subsequent analyses. The existing algorithms for spike detection have achieved great progress but there still remains much room for improvement in terms of the robustness to noise and the flexibility in the spike shape. To address this issue, this paper presents a novel method for spike detection based on the theory of sparse representation. By analyzing the characteristics of extracellular neural recordings, a targetdriven sparse representation framework is firstly constructed, with which the neural spike signals can be effectively separated from background noise. In addition, considering the fact that the spikes emitted by different neurons have different shapes, we then learn a universal dictionary to give a sparse representation of various spike signals. Finally, the information (location and number) of spikes in the recorded signal are achieved by comprehensively analyzing the sparse features. Experimental results demonstrate that the proposed method outperforms the existing methods in the spike detection problem.

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