Format:
1 Online-Ressource (xviii, 689 Seiten).
ISBN:
978-1-108-64298-9
Content:
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
Note:
Title from publisher's bibliographic system (viewed on 12 Nov 2020)
Additional Edition:
Erscheint auch als Druck-Ausgabe, Hardcover ISBN 978-1-108-49370-3
Language:
English
Subjects:
Biology
,
Psychology
Keywords:
Maschinelles Lernen
;
Statistik
;
Neurowissenschaften
DOI:
10.1017/9781108642989
URL:
Volltext
(URL des Erstveröffentlichers)
URL:
Volltext
(URL des Erstveröffentlichers)
URL:
Volltext
(URL des Erstveröffentlichers)
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