Data-Driven Computational Neuroscience: Machine Learning and Statistical Models

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Concha Bielza, Pedro LarrañagaCambridge University Press, 11/26/2020EAN 9781108493703, ISBN10: 110849370XHardcover, 708 pages, 25.9 x 18.5 x 4.3 cmLanguage: EnglishOriginally publi shed in EnglishData-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.Part I. IntroductionSection 1. Computational NeurosciencePart II. StatisticsSection 2. Exploratory Data AnalysisSection 3. Probability Theory and Random VariablesSection 4. Probabilistic InterferencePart III. Supervised pattern recognitionSection 5. Performance EvaluationSection 6. Feature subset selectionSection 7. Non-probabilistic classifiersSection 8. Probabilistic classifiersSection 9. MetaclassifiersSection 10. Multi-dimensional classifiersPart IV. Unsupervised pattern recognitionSection 11. Non-probabilistic clusteringSection 12. Probabilistic clusteringPart V. Probabilistic graphical modelsSection 13. Bayesian networksSection 14. Markov networksPart VI. Spatial statisticsSection 15. Spatial statistics. Show more