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题名/责任者:

Data-driven computational neuroscience : machine learning and statistical models / Concha Bielza, Universidad Polit茅cnica de Madrid, Pedro Larranaga, Universidad Polit茅cnica de Madrid.

出版发行项:

Cambridge ; New York, NY : Cambridge University Press, 2020.

ISBN:

9781108493703:

ISBN:

9781108642989

载体形态项:

708 pages : illustrations ; 26 cm

个人责任者:

Bielza, Concha, author.

附加个人名称:

Larranaga, Pedro, 1958 author.

论题主题:

Neurosciences-Data processing.

论题主题:

Neurosciences-Statistical methods.

中图法分类号:

Q189

书目附注:

Includes bibliographical references and index.

摘要附注:

"Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This introduction for researchers and graduate students is the first in-depth, comprehensive treatment of statistical and machine learning methods for neuroscience. The methods are demonstrated through case studies of real problems to empower readers to build their own solutions. The book covers a wide variety of methods, including supervised classification with non-probabilistic models (nearest-neighbors, classification trees, rule induction, artificial neural networks and support vector machines) and probabilistic models (discriminant analysis, logistic regression and Bayesian network classifiers), meta-classifiers, multi-dimensional classifiers and feature subset selection methods. Other parts of the book are devoted to association discovery with probabilistic graphical models (Bayesian networks and Markov networks) and spatial statistics with point processes (complete spatial randomness and cluster, regular and Gibbs processes). Cellular, structural, functional, medical and behavioral neuroscience levels are considered"--

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