Spike Sorting Based On Low-Rank And Sparse Representation

Huang, Libo; Ling, Bingo Wing‐Kuen; Zeng, Yan; Liu, Gan

doi:10.1109/icme46284.2020.9102837

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…Local-features-based methods are those called locality preserving projection (LPP) [15], Laplacian eigenmaps (LE) [16], and graph-Laplacian (GL) [17], etc. On the other hand, the clustering methods such as K-means (KM) [16], [18], spectral clustering (SC) [15], [19], Gaussian mixture model (GMM) [20], [18] are flourishing. Directly combining the spike feature extraction and the clustering, i.e., separately performing them in sequence, has achieved a certain degree of success.…”

Section: Introductionmentioning

confidence: 99%

A Unified Optimization Model of Feature Extraction and Clustering for Spike Sorting

Huang

Liu

Ling

2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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Spike sorting technologies support neuroscientists to access the neural activity with single-neuron or single-actionpotential resolutions. However, conventional spike sorting technologies perform the feature extraction and the clustering separately after the spikes are well detected. It not only induces many redundant processes, but it also yields a lower accuracy and an unstable result especially when noises and/or overlapping spikes exist in the dataset. To address these issues, this paper proposes a unified optimization model integrating the feature extraction and the clustering for spike sorting. Unlike the widely used combination strategies, i.e., performing the principal component analysis (PCA) for spike feature extraction and the K-means (KM) for clustering in sequence, interestingly, this paper finds the solution of the proposed unified model by iteratively performing PCA and KM-like procedures. Subsequently, by embedding the K-means++ strategy in KM-like initializing and a comparison updating rule in the solving process, the proposed model can well handle the noises and overlapping interference as well as enjoy a high accuracy and a low computational complexity. Finally, an automatic spike sorting method is derived after taking the best of the clustering validity indices into the proposed model. The extensive numerical simulation results on both synthetic and realworld datasets confirm that our proposed method outperforms the related state-of-the-art approaches.

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Section: Introductionmentioning

confidence: 99%

A Unified Optimization Model of Feature Extraction and Clustering for Spike Sorting

Huang

Liu

Ling

2021

IEEE Trans. Neural Syst. Rehabil. Eng.

Self Cite

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“…Additional alternative strategies are biogeography-based optimization (Chiarion and Mesin, 2021 ), using blind source separation methods (Leibig et al, 2016 ) or close examination of each spike cluster's center, which is equally close to another two midpoints (Wouters and Kloosterman, 2021 ), by automated template merging (Chen et al, 2021 ). Sparse representation or compressive sensing of neural data performs peculiarly well when spike waveforms are alike; therefore, overlapping spikes can be resolved by this means (Wu et al, 2018a ; Huang et al, 2020 ). Wavelet Packets Decomposition and Mutual Information (WM sorting) is a clustering algorithm specially designed for overlapping spikes outperforming most of the methods presented here; nevertheless, its computational intensity generates doubts about real-time applications (Huang et al, 2019 ).…”

Section: Arising Challengesmentioning

confidence: 99%

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

Bod

Rokai

Meszéna

et al. 2022

Front. Neuroinform.

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The meaning behind neural single unit activity has constantly been a challenge, so it will persist in the foreseeable future. As one of the most sourced strategies, detecting neural activity in high-resolution neural sensor recordings and then attributing them to their corresponding source neurons correctly, namely the process of spike sorting, has been prevailing so far. Support from ever-improving recording techniques and sophisticated algorithms for extracting worthwhile information and abundance in clustering procedures turned spike sorting into an indispensable tool in electrophysiological analysis. This review attempts to illustrate that in all stages of spike sorting algorithms, the past 5 years innovations' brought about concepts, results, and questions worth sharing with even the non-expert user community. By thoroughly inspecting latest innovations in the field of neural sensors, recording procedures, and various spike sorting strategies, a skeletonization of relevant knowledge lays here, with an initiative to get one step closer to the original objective: deciphering and building in the sense of neural transcript.

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“…Local-features-based methods are those called locality preserving projection (LPP) [15], Laplacian eigenmaps (LE) [16], and graph-Laplacian (GL) [17], etc. On the other hand, clustering methods are flourishing, e.g., K-means (KM) [16], [18], spectral clustering (SC) [15], [19], Gaussian mixture model (GMM) [20], [18]. Simply combining spike feature extraction and clustering, i.e., separately performing them in sequence, has achieved certain degrees of success.…”

Section: Introductionmentioning

confidence: 99%

A Unified Model of Feature Extraction and Clustering for Spike Sorting

Huang,

Gan,

Ling

2020

Preprint

Self Cite

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Spike sorting plays an irreplaceable role in understanding brain codes. Traditional spike sorting technologies perform feature extraction and clustering separately after spikes are well detected. However, it may often cause many additional processes and further lead to low-accurate and/or unstable results especially when there are noises and/or overlapping spikes in datasets. To address these issues, in this paper, we proposed a unified optimisation model integrating feature extraction and clustering for spike sorting. Interestingly, instead of the widely used combination strategies, i.e., performing the principal component analysis (PCA) for spike feature extraction and K-means (KM) for clustering in sequence, we unified PCA and KM into one optimisation model, which reduces additional processes with fewer iteration times. Subsequently, by embedding the K-means++ strategy for initialising and a comparison updating rule in the solving process, the proposed model can well handle the noises and/or overlapping interference. Finally, taking the best of the clustering validity indices into the proposed model, we derive an automatic spike sorting method. Plenty of experimental results on both synthetic and real-world datasets confirm that our proposed method outperforms the related state-of-the-art approaches.

show abstract

Spike Sorting Based On Low-Rank And Sparse Representation

Cited by 4 publications

References 15 publications

A Unified Optimization Model of Feature Extraction and Clustering for Spike Sorting

A Unified Optimization Model of Feature Extraction and Clustering for Spike Sorting

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

A Unified Model of Feature Extraction and Clustering for Spike Sorting

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