ArticleBrain sciences2020
An Accurate and Robust Method for Spike Sorting Based on Convolutional Neural Networks.
Article in Brain sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Efficient and reliable spike sorting from neural recordings with UMAP-based unsupervised nonlinear dimensionality reduction.PLoS biology · 2025Article
- A Study of Deep Clustering in Spike Sorting.Neuroinformatics · 2025Article
- Advancing Spike Sorting Through Gradient-Based Preprocessing and Nonlinear Reduction With Agglomerative Clustering.Brain and behavior · 2025Article
- PseudoSorter: A self-supervised spike sorting approach applied to reveal Tau-induced reductions in neuronal activity.Science advances · 2025Article
- Multi-material Electrohydrodynamic Printing of Bioelectronics with Sub-Microscale 3D Gold Pillars for In Vitro Extra- and Intra-Cellular Electrophysiological Recordings.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- A new approach for neural decoding by inspiring of hyperdimensional computing for implantable intra-cortical BMIs.Scientific reports · 2024Article
- A fully automatic multichannel neural spike sorting algorithm with spike reduction and positional feature.Journal of neural engineering · 2024Article
- Wireless closed-loop deep brain stimulation using microelectrode array probes.Journal of Zhejiang University. Science. B · 2024Review
- RT-Sort: An action potential propagation-based algorithm for real time spike detection and sorting with millisecond latencies.PloS one · 2024Article
- A Fast and Effective Spike Sorting Method Based on Multi-Frequency Composite Waveform Shapes.Brain sciences · 2023Article
- Data Compression Versus Signal Fidelity Tradeoff in Wired-OR Analog-to-Digital Compressive Arrays for Neural Recording.IEEE transactions on biomedical circuits and systems · 2023Article
- Anti-Disturbance of Scale-Free Spiking Neural Network against Impulse Noise.Brain sciences · 2023Article
- From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings.Frontiers in neuroinformatics · 2022Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
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.
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