Quantification of inter-trial non-stationarity in spike trains from periodically stimulated neural cultures
In neuroscience, non-stationarity detection of spike trains is useful for ensuring stability of experimental condition, and detecting plasticity. A novel method for estimating point process divergence and its application for non-stationarity detection in spike trains is proposed. The method for measuring divergence is based on decomposition of finite point process and Hilbertian metrics. The method is demonstrated by detecting short-term and long-term plasticity in neural culture probed with periodic stimulations.