Analysis of Neural Data

Introduction.- Exploring Data.- Probability and Random Variables.- Random Vectors.- Important Probability Distributions.- Sequences of Random Variables.- Estimation and Uncertainty.- Estimation in Theory and Practice.- Uncertainty and the Bootstrap.- Statistical Significance.- General Methods for Testing Hypotheses.- Linear Regression.- Analysis of Variance.- Generalized Regression.- Nonparametric Regression.- Bayesian Methods.- Multivariate Analysis.- Time Series.- Point Processes.- Appendix: Mathematical Background.- Example Index.- Index.- Bibliography.

Robert E. (Rob) Kass is Professor in the Department of Statistics, the Machine Learning Department, and the Center for the Neural Basis of Cognition at Carnegie Mellon University. Since 2001 his research has been devoted to statistical methods in neuroscience. Together with Emery Brown he has organized the highly successful series of international meetings, Statistical Analysis of Neural Data (SAND).

Uri T. Eden is Associate Professor in the Department of Mathematics and Statistics at Boston University. He received his Ph.D. in the Harvard/MIT Medical Engineering and Medical Physics program in the Health Sciences and Technology Department. His research focuses on developing mathematical and statistical methods to analyze neural spiking activity, using methods related to model identi cation, statistical inference, signal processing, and stochastic estimation and control.

Emery N. Brown is Edward Hood Taplin Professor of Medical Engineering, Professor of Computational Neuroscience, and Associate Director of the Institute of Medical Engineering and Science at MIT; he is also the Warren M. Zapol Professor of Anaesthesia at Harvard Medical School and Massachusetts General Hospital. He is both a statistician and an anesthesiologist. Since 1998 his research has focused on neural information processing, and his experimental work characterizes the way anesthetic drugs act in the brain to create the state of general anesthesia.

"This is an outstanding book, that fills a real need. Assuming no background in statistics, it covers the data analysis methods neuroscientists need to know, from standard material like hypothesis tests, to specialized methods that have recently found use in our field. It has the detail and insight needed for those developing their own statistical methods. And for the working neurobiologist it has plenty of practical tricks, tips, and examples, coming straight from the experts. This book is a must for anyone serious about quantitative analysis in neuroscience." -- Kenneth D. Harris, Professor of Quantitative Neuroscience, University College London

This is an outstanding book, that fills a real need. Assuming no background in statistics, it covers the data analysis methods neuroscientists need to know, from standard material like hypothesis tests, to specialized methods that have recently found use in our field. It has the detail and insight needed for those developing their own statistical methods. And for the working neurobiologist it has plenty of practical tricks, tips, and examples, coming straight from the experts. This book is a must for anyone serious about quantitative analysis in neuroscience.
Kenneth D. Harris , Professor of Quantitative Neuroscience, University College London
Analysis of Neural Data is a thorough, authoritative textbook on the fastest growing statistical field. All relevant topics are covered in depth with examples from the literature and thoughtful comments. Particularly welcome is the discussion of multivariate statistics, time series and Bayesian methods, topics frequently encountered in neuroscience research but infrequently discussed in standard statistics textbooks. A highly readable, useful and commendable textbook!
Apostolos P. Georgopoulos , Regents Professor of Neuroscience, University of Minnesota
This book is a unique and valuable resource for any scientist who wants to approach neural data analysis in a rigorous fashion, or to gain a broad overview of modern statistical concepts and approaches. While the book is an eminently practical guide, it is far from a cookbook. The individual who is willing to invest the time to read it will be deeply rewarded not only with everyday methodological guidance, but also, with a comprehensive understanding of the mathematical foundations of statistics. The first chapter, in which the authors lucidly present a perspective on what statistics has to offer, should be required reading for all neuroscientists or at least, all who care about data. The authors have met the difficult and competing challenges of creating a book that is