Analysis of Neural Data

Continual improvements in data collection and processing have had a huge impact on brain research, producing data sets that are often large and complicated. By emphasizing a few fundamental principles, and a handful of ubiquitous techniques, Analysis of Neural Data provides a unified treatment of analytical methods that have become essential for contemporary researchers. Throughout the book ideas are illustrated with more than 100 examples drawn from the literature, ranging from electrophysiology, to neuroimaging, to behavior. By demonstrating the commonality among various statistical approaches the authors provide the crucial tools for gaining knowledge from diverse types of data. Aimed at experimentalists with only high-school level mathematics, as well as computationally-oriented neuroscientists who have limited familiarity with statistics, Analysis of Neural Data serves as both a self-contained introduction and a reference work.

Table of Contents

1 Introduction

2 Exploring Data

3 Probability and Random Variables

4 Random Vectors

5 Important Probability Distributions

6 Sequences of Random Variables

7 Estimation and Uncertainty

8 Estimation in Theory and Practice

9 Propagation of Uncertainty and the Bootstrap

10 Models, Hypotheses, and Statistical Significance

11 General Methods for Testing Hypotheses

12 Linear Regression

13 Analysis of Variance

14 Generalized Linear and Nonlinear Regression

15 Nonparametric Regression

16 Bayesian Methods

17 Multivariate Analysis

18 Time Series

19 Point Processes

About the Authors

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.