ArticlePloS one2023
A study of autoencoders as a feature extraction technique for spike sorting.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Hybrid knowledge- and data-driven modelling for robust spike detection and sorting in human C-fiber microneurography.Scientific reports · 2026Article
- Fraudulent account detection in social media using hybrid deep transformer model and hyperparameter optimization.Scientific reports · 2025Article
- A Study of Deep Clustering in Spike Sorting.Neuroinformatics · 2025Article
- A Study of Non-Linear Manifold Feature Extraction in Spike Sorting.Neuroinformatics · 2025Article
- AECuration: automated event curation for spike sorting.Journal of neural engineering · 2025Article
- Fully-Gated Denoising Auto-Encoder for Artifact Reduction in ECG Signals.Sensors (Basel, Switzerland) · 2025Article
- Towards robust and generalizable representations of extracellular data using contrastive learning.Advances in neural information processing systems · 2023Article
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Authors and funding
5 authors.
Funding
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Abstract
Spike sorting is the process of grouping spikes of distinct neurons into their respective clusters. Most frequently, this grouping is performed by relying on the similarity of features extracted from spike shapes. In spite of recent developments, current methods have yet to achieve satisfactory performance and many investigators favour sorting manually, even though it is an intensive undertaking that requires prolonged allotments of time. To automate the process, a diverse array of machine learning techniques has been applied. The performance of these techniques depends however critically on the feature extraction step. Here, we propose deep learning using autoencoders as a feature extraction method and evaluate extensively the performance of multiple designs. The models presented are evaluated on publicly available synthetic and real "in vivo" datasets, with various numbers of clusters. The proposed methods indicate a higher performance for the process of spike sorting when compared to other state-of-the-art techniques.
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Registered trials
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