Sequential Detection of Regime Changes in Neural Data
Signal Processing
2018-09-05 v1 Neurons and Cognition
Applications
Methodology
Abstract
The problem of detecting changes in firing patterns in neural data is studied. The problem is formulated as a quickest change detection 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.
Keywords
Cite
@article{arxiv.1809.00358,
title = {Sequential Detection of Regime Changes in Neural Data},
author = {Taposh Banerjee and Stephen Allsop and Kay M. Tye and Demba Ba and Vahid Tarokh},
journal= {arXiv preprint arXiv:1809.00358},
year = {2018}
}