Sequential Detection of Regime Changes in Neural Data

Abstract

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.

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