Data-Driven Computational Neuroscience - Machine Learning and Statistical Models (Hardcover)

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.

General

Imprint:	Cambridge UniversityPress
Country of origin:	United Kingdom
Release date:	November 2020
Authors:	Concha Bielza • Pedro Larranaga
Dimensions:	259 x 185 x 43mm (L x W x T)
Format:	Hardcover
Pages:	708
ISBN-13:	978-1-108-49370-3
Categories:	Books > Computing & IT > Applications of computing > Signal processing Books > Social sciences > Psychology > Cognition & cognitive psychology > General Books > Science & Mathematics > Biology, life sciences > Life sciences: general issues > Neurosciences Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Books > Computing & IT > Applications of computing > Artificial intelligence > Neural networks
LSN:	1-108-49370-X
Barcode:	9781108493703

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