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The problem of detecting changes in firing patterns in neural data isstudied. The problem is formulated as a quickest change detection problem.Important algorithms from the literature are reviewed. A new algorithmictechnique is discussed to detect deviations from learned baseline behavior. Thealgorithms studied can be applied to both spike and local field potential data.The algorithms are applied to mice spike data to verify the presence ofbehavioral learning.

Sequential Detection of Regime Changes in Neural Data