1
|
Huang L, Gan L, Zeng Y, Ling BWK. Automatical Spike Sorting With Low-Rank and Sparse Representation. IEEE Trans Biomed Eng 2024; 71:1677-1686. [PMID: 38147418 DOI: 10.1109/tbme.2023.3347137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2023]
Abstract
Spikesorting is crucial in studying neural individually and synergistically encoding and decoding behaviors. However, existent spike sorting algorithms perform unsatisfactorily in real scenarios where heavy noises and overlapping samples are commonly in the spikes, and the spikes from different neurons are similar. To address such challenging scenarios, we propose an automatic spike sporting method in this paper, which integrally combines low-rank and sparse representation (LRSR) into a unified model. In particular, LRSR models spikes through low-rank optimization, uncovering global data structure for handling similar and overlapped samples. To eliminate the influence of the embedded noises, LRSR uses a sparse constraint, effectively separating spikes from noise. The optimization is solved using alternate augmented Lagrange multipliers methods. Moreover, we conclude with an automatic spike-sorting framework that employs the spectral clustering theorem to estimate the number of neurons. Extensive experiments over various simulated and real-world datasets demonstrate that our proposed method, LRSR, can handle spike sorting effectively and efficiently.
Collapse
|
2
|
Li S, Tang Z, Yang L, Li M, Shang Z. Application of deep reinforcement learning for spike sorting under multi-class imbalance. Comput Biol Med 2023; 164:107253. [PMID: 37536094 DOI: 10.1016/j.compbiomed.2023.107253] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 06/27/2023] [Accepted: 07/07/2023] [Indexed: 08/05/2023]
Abstract
Spike sorting is the basis for analyzing spike firing patterns encoded in high-dimensional information spaces. With the fact that high-density microelectrode arrays record multiple neurons simultaneously, the data collected often suffers from two problems: a few overlapping spikes and different neuronal firing rates, which both belong to the multi-class imbalance problem. Since deep reinforcement learning (DRL) assign targeted attention to categories through reward functions, we propose ImbSorter to implement spike sorting under multi-class imbalance. We describe spike sorting as a Markov sequence decision and construct a dynamic reward function (DRF) to improve the sensitivity of the agent to minor classes based on the inter-class imbalance ratios. The agent is eventually guided by the optimal strategy to classify spikes. We consider the Wave_Clus dataset, which contains overlapping spikes and diverse noise levels, and the macaque dataset, which has a multi-scale imbalance. ImbSorter is compared with classical DRL architectures, traditional machine learning algorithms, and advanced overlapping spike sorting techniques on these two above datasets. ImbSorter obtained improved results on the Macro_F1. The results show ImbSorter has a promising ability to resist overlapping and noise interference. It has high stability and promising performance in processing spikes with different degrees of skewed distribution.
Collapse
Affiliation(s)
- Suchen Li
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, 450001, China; Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, 450001, China
| | - Zhuo Tang
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, 450001, China; Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, 450001, China
| | - Lifang Yang
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, 450001, China; Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, 450001, China
| | - Mengmeng Li
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, 450001, China; Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, 450001, China.
| | - Zhigang Shang
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, 450001, China; Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, 450001, China.
| |
Collapse
|
3
|
Classification of overlapping spikes using convolutional neural networks and long short term memory. Comput Biol Med 2022; 148:105888. [PMID: 35872414 DOI: 10.1016/j.compbiomed.2022.105888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 06/29/2022] [Accepted: 07/16/2022] [Indexed: 11/21/2022]
Abstract
Spike sorting is one of the key techniques to understand brain activity. In this paper, we propose a novel deep learning approach based on convolutional neural networks (CNN) and long short term memory (LSTM) to implement overlapping spike sorting. The results of the simulated data demonstrated that the clustering accuracy was greater than 99.9% and 99.0% for non-overlapping spikes and overlapping spikes, respectively. Moreover, the proposed method performed better than our previous deep learning approach named "1D-CNN". 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 overlapping spikes of different neurons (ranging from two to five). In summary, the CNN + LSTM method proposed in this paper is of great advantage for overlapping spike sorting with high accuracy. It lays the foundation for application in more challenging works, such as distinguishing the simultaneous recordings of multichannel neuronal activities.
Collapse
|
4
|
Bod RB, Rokai J, Meszéna D, Fiáth R, Ulbert I, Márton G. From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings. Front Neuroinform 2022; 16:851024. [PMID: 35769832 PMCID: PMC9236662 DOI: 10.3389/fninf.2022.851024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 05/06/2022] [Indexed: 11/15/2022] Open
Abstract
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.
Collapse
Affiliation(s)
- Réka Barbara Bod
- Laboratory of Experimental Neurophysiology, Department of Physiology, Faculty of Medicine, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Târgu Mureş, Târgu Mureş, Romania
| | - János Rokai
- Integrative Neuroscience Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- School of PhD Studies, Semmelweis University, Budapest, Hungary
| | - Domokos Meszéna
- Integrative Neuroscience Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Richárd Fiáth
- Integrative Neuroscience Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - István Ulbert
- Integrative Neuroscience Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Gergely Márton
- Integrative Neuroscience Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| |
Collapse
|
5
|
Chen K, Jiang Y, Wu Z, Zheng N, Wang H, Hong H. HTsort: Enabling Fast and Accurate Spike Sorting on Multi-Electrode Arrays. Front Comput Neurosci 2021; 15:657151. [PMID: 34234663 PMCID: PMC8255361 DOI: 10.3389/fncom.2021.657151] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 05/28/2021] [Indexed: 11/13/2022] Open
Abstract
Spike sorting is used to classify the spikes (action potentials acquired by physiological electrodes), aiming to identify their respective firing units. Now it has been developed to classify the spikes recorded by multi-electrode arrays (MEAs), with the improvement of micro-electrode technology. However, how to improve classification accuracy and maintain low time complexity simultaneously becomes a difficulty. A fast and accurate spike sorting approach named HTsort is proposed for high-density multi-electrode arrays in this paper. Several improvements have been introduced to the traditional pipeline that is composed of threshold detection and clustering method. First, the divide-and-conquer method is employed to utilize electrode spatial information to achieve pre-clustering. Second, the clustering method HDBSCAN (hierarchical density-based spatial clustering of applications with noise) is used to classify spikes and detect overlapping events (multiple spikes firing simultaneously). Third, the template merging method is used to merge redundant exported templates according to the template similarity and the spatial distribution of electrodes. Finally, the template matching method is used to resolve overlapping events. Our approach is validated on simulation data constructed by ourselves and publicly available data and compared to other state-of-the-art spike sorters. We found that the proposed HTsort has a more favorable trade-off between accuracy and time consumption. Compared with MountainSort and SpykingCircus, the time consumption is reduced by at least 40% when the number of electrodes is 64 and below. Compared with HerdingSpikes, the classification accuracy can typically improve by more than 10%. Meanwhile, HTsort exhibits stronger robustness against background noise than other sorters. Our more sophisticated spike sorter would facilitate neurophysiologists to complete spike sorting more quickly and accurately.
Collapse
Affiliation(s)
- Keming Chen
- Key Laboratory of Radio Frequency Circuit and System, Hangzhou Dianzi University, Hangzhou, China
| | - Yangtao Jiang
- Key Laboratory of Radio Frequency Circuit and System, Hangzhou Dianzi University, Hangzhou, China
| | - Zhanxiong Wu
- Key Laboratory of Radio Frequency Circuit and System, Hangzhou Dianzi University, Hangzhou, China
| | - Nenggan Zheng
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
| | - Haochuan Wang
- Key Laboratory of Radio Frequency Circuit and System, Hangzhou Dianzi University, Hangzhou, China
| | - Hui Hong
- Key Laboratory of Radio Frequency Circuit and System, Hangzhou Dianzi University, Hangzhou, China
| |
Collapse
|
6
|
Moghaddasi M, Aliyari Shoorehdeli M, Fatahi Z, Haghparast A. Unsupervised automatic online spike sorting using reward-based online clustering. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101701] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|