Data-Driven Computational Neuroscience

Concha Bielza is a professor in the Department of Artificial Intelligence at Universidad Politécnica 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 Larrañaga is a professor in the Department of Artificial Intelligence at Universidad Politécnica 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.

'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

• 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.