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.

'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

'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 Larrañaga - two eminent scientists specializing in computer science and artificial intelligence.'

Javier DeFelipe - Instituto Cajal and Centro de Tecnología Biomédica

'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