Deep-learned spike representations and sorting via an ensemble of auto-encoders

Junsik Eom, In Yong Park, Sewon Kim, Hanbyol Jang, Sanggeon Park, Yeowool Huh, Dosik Hwang

Research output: Contribution to journalArticlepeer-review

Abstract

Spike sorting refers to the technique of detecting signals generated by single neurons from multi-neuron recordings and is a valuable tool for analyzing the relationships between individual neuronal activity patterns and specific behaviors. Since the precision of spike sorting affects all subsequent analyses, sorting accuracy is critical. Many semi-automatic to fully-automatic spike sorting algorithms have been developed. However, due to unsatisfactory classification accuracy, manual sorting is preferred by investigators despite the intensive time and labor costs. Thus, there still is a strong need for fully automatic spike sorting methods with high accuracy. Various machine learning algorithms have been developed for feature extraction but have yet to show sufficient accuracy for spike sorting. Here we describe a deep learning-based method for extracting features from spike signals using an ensemble of auto-encoders, each with a distinct architecture for distinguishing signals at different levels of resolution. By utilizing ensemble of auto-encoder ensemble, where shallow networks better represent overall signal structure and deep networks better represent signal details, extraction of high-dimensional representative features for improved spike sorting performance is achieved. The model was evaluated on publicly available simulated datasets and single-channel and 4-channel tetrode in vivo datasets. Our model not only classified single-channel spikes with varying degrees of feature similarities and signal to noise levels with higher accuracy, but also more precisely determined the number of source neurons compared to other machine learning methods. The model also demonstrated greater overall accuracy for spike sorting 4-channel tetrode recordings compared to single-channel recordings.

Original languageEnglish
Pages (from-to)131-142
Number of pages12
JournalNeural Networks
Volume134
DOIs
Publication statusPublished - 2021 Feb

Bibliographical note

Funding Information:
This research was partially supported by Graduate School of YONSEI University Research Scholarship Grants in 2019 and the Brain Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning, South Korea ( 2018M3C7A1024734 , 2018M3C7A1024736 , 2015M3C7A1028392 ).

All Science Journal Classification (ASJC) codes

  • Cognitive Neuroscience
  • Artificial Intelligence

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