Analysis of Neural Data

Nonfiction, Health & Well Being, Medical, Reference, Biostatistics, Specialties, Internal Medicine, Neuroscience, Science & Nature, Mathematics
Cover of the book Analysis of Neural Data by Robert E. Kass, Uri T. Eden, Emery N. Brown, Springer New York
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Author: Robert E. Kass, Uri T. Eden, Emery N. Brown ISBN: 9781461496021
Publisher: Springer New York Publication: July 8, 2014
Imprint: Springer Language: English
Author: Robert E. Kass, Uri T. Eden, Emery N. Brown
ISBN: 9781461496021
Publisher: Springer New York
Publication: July 8, 2014
Imprint: Springer
Language: English

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.

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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.

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