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2008 NIPS NeurIPS 2008

Spike Feature Extraction Using Informative Samples

Abstract

This paper presents a spike feature extraction algorithm that targets real-time spike sorting and facilitates miniaturized microchip implementation. The proposed algorithm has been evaluated on synthesized waveforms and experimentally recorded sequences. When compared with many spike sorting approaches our algorithm demonstrates improved speed, accuracy and allows unsupervised execution. A preliminary hardware implementation has been realized using an integrated microchip interfaced with a personal computer.

🌉 Interdisciplinary Bridge — Healthcare & Medicine and Machine Learning
🧭 Keyword Pioneer — neural signal processing
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Speech & Audio
📈 Trend Setter — Self-Supervised Learning
🐣 Hot Topic Early Bird — feature extraction

Authors

Zhi Yang , Qi Zhao , Wentai Liu

Topics

Machine Learning > Core Methods > Classification Healthcare & Medicine > Research > Biosignal Processing Machine Learning > Core Methods > Feature Learning Machine Learning > Learning Paradigms > Self-Supervised Learning

Keywords

unsupervised learning feature extraction neural signal processing signal processing spike sorting real-time spike sorting waveform analysis real-time processing unsupervised spike detection unsupervised execution
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