Multichannel electrophysiological spike sorting via joint dictionary learning and mixture modeling.


Journal Article

We propose a methodology for joint feature learning and clustering of multichannel extracellular electrophysiological data, across multiple recording periods for action potential detection and classification (sorting). Our methodology improves over the previous state of the art principally in four ways. First, via sharing information across channels, we can better distinguish between single-unit spikes and artifacts. Second, our proposed "focused mixture model" (FMM) deals with units appearing, disappearing, or reappearing over multiple recording days, an important consideration for any chronic experiment. Third, by jointly learning features and clusters, we improve performance over previous attempts that proceeded via a two-stage learning process. Fourth, by directly modeling spike rate, we improve the detection of sparsely firing neurons. Moreover, our Bayesian methodology seamlessly handles missing data. We present the state-of-the-art performance without requiring manually tuning hyperparameters, considering both a public dataset with partial ground truth and a new experimental dataset.

Full Text

Duke Authors

Cited Authors

  • Carlson, DE; Vogelstein, JT; Qisong Wu, ; Wenzhao Lian, ; Mingyuan Zhou, ; Stoetzner, CR; Kipke, D; Weber, D; Dunson, DB; Carin, L

Published Date

  • January 2014

Published In

Volume / Issue

  • 61 / 1

Start / End Page

  • 41 - 54

PubMed ID

  • 23912463

Pubmed Central ID

  • 23912463

Electronic International Standard Serial Number (EISSN)

  • 1558-2531

International Standard Serial Number (ISSN)

  • 0018-9294

Digital Object Identifier (DOI)

  • 10.1109/tbme.2013.2275751


  • eng