Learning to Sort: Few-shot Spike Sorting with Adversarial Representation Learning

Tong Wu, Aniko Ratkai, Katalin Schlett, Laszlo Grand, Zhi Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Spike sorting has long been used to obtain activities of single neurons from multi-unit recordings by extracting spikes from continuous data and assigning them to putative neurons. A large body of spike sorting algorithms have been developed that typically project spikes into a low-dimensional feature space and cluster them through iterative computations. However, there is no reached consensus on the optimal feature space or the best way of segmenting spikes into clusters, which often leads to the requirement of human intervention. It is hence desirable to effectively and efficiently utilize human knowledge in spike sorting while keeping a minimum level of manual intervention. Furthermore, the iterative computations that are commonly involved during clustering are inherently slow and hinder real-time processing of large-scale recordings. In this paper, we propose a novel few-shot spike sorting paradigm that employs a deep adversarial representation neural network to learn from a handful of annotated spikes and robustly classify unseen spikes sharing similar properties to the labeled ones. Once trained, the deep neural network can implement a parametric function that encodes analytically the categorical distribution of spike clusters, which can be significantly accelerated by GPUs and support processing hundreds of thousands of recording channels in real time. The paradigm also includes a clustering routine termed DidacticSortto aid users for labeling spikes that will be used to train the deep neural network. We have validated the performance of the proposed paradigm with both synthetic and in vitro datasets.

Original languageEnglish (US)
Title of host publication2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages713-716
Number of pages4
ISBN (Electronic)9781538613115
DOIs
StatePublished - Jul 2019
Event41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 - Berlin, Germany
Duration: Jul 23 2019Jul 27 2019

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Conference

Conference41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
CountryGermany
CityBerlin
Period7/23/197/27/19

PubMed: MeSH publication types

  • Journal Article
  • Research Support, Non-U.S. Gov't

Fingerprint Dive into the research topics of 'Learning to Sort: Few-shot Spike Sorting with Adversarial Representation Learning'. Together they form a unique fingerprint.

  • Cite this

    Wu, T., Ratkai, A., Schlett, K., Grand, L., & Yang, Z. (2019). Learning to Sort: Few-shot Spike Sorting with Adversarial Representation Learning. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 (pp. 713-716). [8856938] (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2019.8856938