Data-Driven Computational Neuroscience: Machine Learning and Statistical Models

ISBN-13: 9781108493703 / Angielski / Twarda / 2020 / 708 str.

Concha Bielza (Universidad Politécnica de Madrid);Pedro Larrañaga (Universidad Politécnica de Madrid)

Opis

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.

Recenzje

'In our world of Big Brain Initiatives and Big Data, this encompassing book provides the much-needed bridge between these two 'Bigs'. Data-driven computational and statistical methods are admirably presented and exemplified, providing new insights on fundamental challenges such as classifying neurons into types, uncovering the neuronal code and unveiling principles of brain-connectivity. This book is a must.' Idan Segev, The Edmond and Lily Safra Centre for Brain Sciences, The Hebrew University of Jerusalem

Spis treści

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.