Detecting multiple change points using adaptive regression splines with application to neural recordings

• Verfasst von: Toutounji, Hazem [VerfasserIn]
• Verfasst von: Durstewitz, Daniel [VerfasserIn]
• Titel: Detecting multiple change points using adaptive regression splines with application to neural recordings
• Verf.angabe: Hazem Toutounji and Daniel Durstewitz
• E-Jahr: 2018
• Jahr: 04 October 2018
• Umfang: 17 S.
• Fussnoten: Gesehen am 03.05.2019
• Titel Quelle: Enthalten in: Frontiers in neuroinformatics
• Ort Quelle: Lausanne : Frontiers Research Foundation, 2007
• Jahr Quelle: 2018
• Band/Heft Quelle: 12(2018)
• ISSN Quelle: 1662-5196
• DOI: doi:10.3389/fninf.2018.00067
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1664554270

Abstract

Time series, as frequently the case in neuroscience, are rarely stationary, but often exhibit abrupt changes due to attractor transitions or bifurcations in the dynamical systems producing them. A plethora of methods for detecting such change points in time series statistics have been developed over the years, in addition to test criteria to evaluate their significance. Issues to consider when developing change point analysis methods include computational demands, difficulties arising from either limited amount of data or a large number of covariates, and arriving at statistical tests with sufficient power to detect as many changes as contained in potentially high-dimensional time series. Here, a general method called Paired Adaptive Regressors for Cumulative Sum is developed for detecting multiple change points in the mean of multivariate time series. The method's advantages over alternative approaches are demonstrated through a series of simulation experiments. This is followed by a real data application to neural recordings from rat medial prefrontal cortex during learning. Finally, the method's flexibility to incorporate useful features from state-of-the-art change point detection techniques is discussed, along with potential drawbacks and suggestions to remedy them.