Advanced Data Analysis in Neuroscience: Integrating Statistical and Computational Models

This book is intended for use in advanced graduate courses in statistics / 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. It 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. Further, it integrates computational modeling of behavioral and neural dynamics with statistical estimation and hypothesis testing. This way computational models in neuroscience are not only explanat

ory frameworks, but become powerful, quantitative data-analytical tools in themselves that enable researchers to look beyond the data surface and unravel underlying mechanisms. Interactive examples of most methods are provided through a package of MatLab routines, encouraging a playful approach to the subject, and providing readers with a better feel for the practical aspects of the methods covered.

“Computational neuroscience is essential for integrating and providing a basis for understanding the myriads of remarkable laboratory data on nervous system functions. Daniel Durstewitz has excellently covered the breadth of computational neuroscience from statistical interpretations of data to biophysically based modeling of the neurobiological sources of those data. His presentation is clear, pedagogically sound, and readily useable by experts and beginners alike. It is a pleasure to recommend this very well crafted discussion to experimental neuroscientists as well

as mathematically well versed Physicists. The book acts as a window to the issues, to the questions, and to the tools for finding the answers to interesting inquiries about brains and how they function.”

Henry D. I. Abarbanel

Physics and Scripps Institution of Oceanography, University of California, San Diego

“This book delivers a clear and thorough introduction to sophisticated analysis approaches useful in computational neuroscience.  The models described and the examples provided will help readers develop critical intuitions into what the methods reveal about data.  The overall approach of the book reflects the extensive experience Prof. Durstewitz has developed as a leading practitioner of computational neuroscience. “

Bruno B. Averbeck

Table of Contents

Chapter 1: Statistical Inference
Chapter 2: Regression Problems
Chapter 3: Classification Problems
Chapter 4: Model Complexity and Selection
Chapter 5: Clustering and Density Estimation
Chapter 6: Dimensionality Reduction
Chapter 7: Linear Time Series Analysis
Chapter 8: Nonlinear Concepts in Time Series Analysis
Chapter 9: Time Series from a Nonlinear Dynamical Systems Perspective