Data-Driven Computational Neuroscience: Machine Learning and Statistical Models

Data-driven computational neuroscience is a comprehensive treatment of statistical and machine learning methods for neuroscience, demonstrated through case studies and covering a wide variety of methods. It is designed for researchers and graduate students.

Format: Hardback
Length: 708 pages
Publication date: 26 November 2020
Publisher: Cambridge University Press

Data-driven computational neuroscience plays a pivotal role in converting data into valuable insights into the intricate structure and functions of the brain. This comprehensive introduction, designed specifically for researchers and graduate students, serves as the first in-depth exploration of statistical and machine learning techniques applied to neuroscience. Through a series of case studies showcasing real-world problems, the book empowers readers to develop their own solutions by demonstrating the practical application of these methods.

The book encompasses a diverse range of techniques, including supervised classification with non-probabilistic models such as nearest neighbors, classification trees, rule induction, artificial neural networks, and support vector machines. It also delves into probabilistic models such as discriminant analysis, logistic regression, and Bayesian network classifiers, as well as meta-classifiers, multi-dimensional classifiers, and feature subset selection methods. Additionally, other chapters focus on association discovery with probabilistic graphical models, such as Bayesian networks and Markov networks, and spatial statistics with point processes, encompassing concepts of complete spatial randomness, cluster, regular, and Gibbs processes.

The scope of data-driven computational neuroscience extends across various levels of neuroscience, including cellular, structural, functional, medical, and behavioral neuroscience. By leveraging these cutting-edge methods, researchers can gain a deeper understanding of the complex neural networks underlying cognitive processes, behavior, and disease. This book serves as a valuable resource for scholars and practitioners seeking to advance their knowledge in this rapidly evolving field.

Weight: 1496g
Dimension: 185 x 259 x 45 (mm)
ISBN-13: 9781108493703