Data-driven computational neuroscience : machine learning and statistical models
By: Bielza, Concha.
Contributor(s): Larranaga, Pedro.
Publisher: Cambridge Cambridge University Press 2021Description: xviii, 689p.ISBN: 9781108493703.Subject(s): Neurosciences -- Data processingDDC classification: 612.8 | B476d Summary: 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.| Item type | Current location | Collection | Call number | Status | Date due | Barcode | Item holds |
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PK Kelkar Library, IIT Kanpur | General Stacks | 612.8 B476d (Browse shelf) | Available | A185444 |
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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