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Analysis of Neural Data

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概要 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.続きを見る
目次 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.
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本文を見る Full text available from Springer Mathematics and Statistics eBooks 2014 English/International

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登録日 2020.06.27
更新日 2020.06.28