Data-driven computational neuroscience: machine learning and statistical models/
xviii, 689 pages diagrams, illustrations 26 cm. Content notes : Part I Introduction -- 1. Computational Neuroscience -- Part II Statistics -- 2. Exploratory data analysis -- 3. Probability theory and random variables -- Part III Supervised classification -- 5. Performance evaluation -- 6.Feature subset selection -- 7. Non- probabilistic classifiers -- 8. Probabilistic classifiers -- 9. Metaclassifiers -- 10. Multidimensional classifiers -- Part IV Unsupervised classification -- 11. Non-probabilistic clustering -- 12. Probabilistic clustering -- Part V Probabilistic graphical models -- 13. Bayesian networks -- 14. Markov networks -- Part VI Spatial Statistics -- 15. Spatial statistics Neuroscience- Computational Models Neuroscience- Statistical Methods
