Recommended or required reading:
Abeles, M.: Corticonics: Neural Circuits of the Cerebral Cortex, Cambridge University Press, 1991
Alberts, B.; Bray, D.; Lewis, J.: Molecular Biology of the Cell, Garland Science, 2002
Andreassi, John L.: Psychophysiology: Human Behavior and Physiological Response, Taylor & Francis, 2006, ISBN 978-0805849516
Bear, M.F.; Connors, B.W.; Paradiso, M.A.: Neuroscience, Lippincott Williams and Wilkins, 2001
Churchland, P.S:; Sejnowski, T.J.: The Computational Brain, MIT Press, 1992
Dayan, P.; Abbott, L.F.: Theoretical Neuroscience, MIT Press, 1992
Duda, Richard O.; Hart, Peter E.; Stock, David G.: Pattern Classification, Wiley, 2001, 2. Aufl., ISBN 978-0471056690
Eliasmith, Chris; Anderson, Charles H.: Neural Engineering - Computation, Representation, and Dynamics in Neurobiological Systems, MIT Press, 2003, ISBN 0-262-05071-4
Fletcher, R.: Practical Methods of Optimization, John Wiley & Sons, 1987
Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron: Deep Learning, MIT Press, 2016
Levine, Daniel S.: Introduction to Neural and Cognitive Monitoring, Lawrence Erlbaum Associates, 2000
Malmivuo, Jaakko; Plonsey, Robert: Bioelectromagnetism, Oxford University Press, 1995
Pesenson, Misha Z.: Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain, Wiley VCH, 2013, ISBN 978-3527411986
Ripley, Brian D.: Pattern Recognition and Neural Networks, Cambridge University Press, 1996
Rosner, Jorge: Peeling the Onion: Gestalt Theory and Methodology, Gestalt-Institute of Toronto, 1990
Schölkopf, B.; Smola, A.J.: Learning with Kernels: Support vector Machinces, Regularization, Optimization and Beyond, MIT Press, 2002
Sheppard, Clinton: Tree-based Machine Learning Algorithms: Decision Trees, Random Forests, and Boosting, CreateSpace Independent Publishing Platform, 2017, ISBN 978-1975860974
Vapnik, V.N.: Statistical Learning Theory, John Wiley & Sons, 1998
Wahba, G.: Spline Models for Observational Data, SIAM, 1990
[updated 18.07.2019]
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