Data-Driven Computational Neuroscience : Machine Learning and Statistical Models by Concha Bielza and Pedro Larranaga (2020, Hardcover)

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Data-Driven Computational Neuroscience : Machine Learning and Statistical Models, Hardcover by Bielza, Concha; Larrañaga, Pedro, ISBN 110849370X, ISBN-13 9781108493703, Brand New, Free shipping in the US

About this product

Product Identifiers

PublisherCambridge University Press

ISBN-10110849370X

ISBN-139781108493703

eBay Product ID (ePID)5038765419

Product Key Features

Number of Pages708 Pages

Publication NameData-Driven Computational Neuroscience : Machine Learning and Statistical Models

LanguageEnglish

SubjectLife Sciences / Anatomy & Physiology (See Also Life Sciences / Human Anatomy & Physiology), Computer Vision & Pattern Recognition

Publication Year2020

TypeTextbook

AuthorConcha Bielza, Pedro Larranaga

Subject AreaComputers, Science

FormatHardcover

Dimensions

Item Height1.7 in

Item Weight52.6 Oz

Item Length10.2 in

Item Width7.3 in

Additional Product Features

Intended AudienceScholarly & Professional

LCCN2019-060117

Dewey Edition23

Reviews'With admirable zeal, Bielza and Larrañaga have digested and summarized an entire field, the machine learning methods in computational neuroscience. The critical importance of computational tools to analyze neural data and decipher the neural code has been emphasized by the US and international BRAIN Initiatives and this book provides a sure and solid step in this direction.' Rafael Yuste, Columbia University

IllustratedYes

Dewey Decimal612.8

Table Of ContentPart I. Introduction; Section 1. Computational Neuroscience; Part II. Statistics; Section 2. Exploratory Data Analysis; Section 3. Probability Theory and Random Variables; Section 4. Probabilistic Interference; Part III. Supervised pattern recognition; Section 5. Performance Evaluation; Section 6. Feature subset selection; Section 7. Non-probabilistic classifiers; Section 8. Probabilistic classifiers; Section 9. Metaclassifiers; Section 10. Multi-dimensional classifiers; Part IV. Unsupervised pattern recognition; Section 11. Non-probabilistic clustering; Section 12. Probabilistic clustering; Part V. Probabilistic graphical models; Section 13. Bayesian networks; Section 14. Markov networks; Part VI. Spatial statistics; Section 15. Spatial statistics.

SynopsisTrains researchers and graduate students in state-of-the-art statistical and machine learning methods to build models with real-world data., 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., Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This modern treatment of real world cases offers neuroscience researchers and graduate students a comprehensive, in-depth guide to statistical and machine learning methods.

LC Classification NumberQP357.5.B54 2020