  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.
