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This short book will discuss the crucial role that statistical modelling plays in unravelling patterns arising from recording neuronal cell activities (spikes). The history of modern neuroscience coincides with the rise and popularity of statistical thought process – dating back to the beginning of the 20th century. As such, and from early on, spike trains (incidence of spiking activities, mapped out on a time scale), have been viewed as realizations of point processes. Consequently, the advancement of new means of recording spike trains, such as simultaneous recordings of multiple cells, has radically increased the demand for appropriate statistical analysis, whether through developing theoretical bases for multivariate stochastics processes, or via expanding the tools for capturing the networking of adjacent neurons.

This book has been written for students and researchers who wish to gain a working knowledge of time series analysis as applied in biomedical sciences. While intended as a text for graduate and undergraduate students in statistics and biostatistics, it covers a wide range of parametric, nonparametric and multi-scale methods for the analysis of stochastic processes coming from biology, medicine, epidemiology and neuroscience. The book assumes only a basic knowledge of calculus, matrix algebra and elementary statistics.