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Data-Driven Computational Neuroscience: Machine Learning and Statistical Models



1st Edition 

by Concha Bielza (Author) 

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.


Year: 2021
Pages: 746
Language: English
Format: PDF
Size: 43 MB
Publisher: Cambridge University Press
ISBN-10: 110849370X
ISBN-13: 978-1108493703
Tag: Download Book Data-Driven Computational Neuroscience: Machine Learning and Statistical Models