Advanced Data Analysis in Neuroscience: Integrating Statistical and Computational Models

Advanced Data Analysis in Neuroscience: Integrating Statistical and Computational Models is intended for use in advanced graduate courses in statistics and machine learning, as well as for all experimental neuroscientists seeking to understand statistical methods at a deeper level, and theoretical neuroscientists with a limited background in statistics. The book reviews almost all areas of applied statistics, from basic statistical estimation and test theory, linear and nonlinear approaches for regression and classification, to model selection and methods for dimensionality reduction, density estimation, and unsupervised clustering. Its focus, however, is linear and nonlinear time series analysis from a dynamical systems perspective, based on which it aims to convey an understanding also of the dynamical mechanisms that could have generated observed time series. It integrates computational modeling of behavioral and neural dynamics with statistical estimation and hypothesis testing.

Interactive examples of most methods are provided through a package of MATLAB routines. The MATLAB code used in the book is also available for download.