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Data-Driven Computational Neuroscience

Machine Learning and Statistical Models

Concha Bielza (Universidad Politecnica de Madrid) Pedro Larranaga (Universidad Politecnica de Madrid)

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Cambridge University Press
26 November 2020
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.
By:   Concha Bielza (Universidad Politecnica de Madrid), Pedro Larranaga (Universidad Politecnica de Madrid)
Imprint:   Cambridge University Press
Country of Publication:   United Kingdom
Dimensions:   Height: 259mm,  Width: 185mm,  Spine: 43mm
Weight:   1.490kg
ISBN:   9781108493703
ISBN 10:   110849370X
Pages:   708
Publication Date:   26 November 2020
Audience:   Professional and scholarly ,  Undergraduate
Format:   Hardback
Publisher's Status:   Active
Part 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.

Concha Bielza is a professor in the Department of Artificial Intelligence at Universidad Politecnica de Madrid. She has published more than 120 journal papers and coauthored the book Industrial Applications of Machine Learning (2019). She was awarded the 2014 UPM Research Prize. Pedro Larranaga is a professor in the Department of Artificial Intelligence at Universidad Politecnica de Madrid. He has published more than 150 journal papers and coauthored the book Industrial Applications of Machine Learning (2019). He is fellow of the European Association for Artificial Intelligence and of Academia Europaea.

Reviews for Data-Driven Computational Neuroscience: Machine Learning and Statistical Models

'With admirable zeal, Bielza and Larranaga 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 'Data-Driven Computational Neuroscience is an outstanding treatment of modern statistical data analysis and machine learning for neuroscience. Illustrating each method by real world use-cases, this book is unique as a hands on and comprehensive presentation of technique and analysis. The result is a fine text and resource that treats many important but less well-known aspects of the practice.' Michael Hawrylycz, Allen Institute for Brain Science 'This book provides us with an outstanding text dealing with the multiple applications in modern neuroscience of statistical and computational models learned from data. There is no doubt that new neuroscience technologies and computational neuroscience methods will make it possible to define the structural and functional design of brain circuits and to determine how these designs contribute to the functional organization of the brain. This book contains numerous examples of the current applications of computational neuroscience in various fields of neuroscience, presented in such a way that it is easily accessible to those who are not experts in the field. Therefore, the book also represents an excellent opportunity for neuroscientists from all fields to be introduced to this fascinating world of computational neuroscience, expertly guided by Concha Bielza and Pedro Larranaga - two eminent scientists specializing in computer science and artificial intelligence.' Javier DeFelipe, Instituto Cajal and Centro de Tecnologia Biomedica


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