Sequential Detection of Regime Changes in Neural Data

Published

Journal Article

© 2019 IEEE. The problem of detecting changes in firing patterns in neural data is studied. The problem is formulated as a quickest change detection (QCD) problem. Important algorithms from the literature are reviewed. A new algorithmic technique is discussed to detect deviations from learned baseline behavior. The algorithms studied can be applied to both spike and local field potential data. The algorithms are applied to mice spike data to verify the presence of behavioral learning.

Full Text

Duke Authors

Cited Authors

  • Banerjee, T; Allsop, S; Tye, KM; Ba, D; Tarokh, V

Published Date

  • May 16, 2019

Published In

Volume / Issue

  • 2019-March /

Start / End Page

  • 139 - 142

Electronic International Standard Serial Number (EISSN)

  • 1948-3554

International Standard Serial Number (ISSN)

  • 1948-3546

Digital Object Identifier (DOI)

  • 10.1109/NER.2019.8716926

Citation Source

  • Scopus