- Kandel, Schwartz, Jessell. Principles of Neural Science. McGraw Hill. Parts I – IV.
[cell and molecular biology of the neuron, synaptic transmission, neural basis of cognition]
- Bear, Connors, Paradiso. Neuroscience. Exploring the Brain. LWW.
- Bühlmann, P. Computational Statistics. ETH lectures. Website / Script
[introduction to classical multiple linear regression, hypothesis tests, nonparametric regression, classification, shrinkage]
Bayesian statistics and machine learning
- Bishop, C. M. (2007). Pattern Recognition and Machine Learning. Springer. Chapters 1-4.
[Bayesian linear regression and classification]
- Barber, D. (2012). Bayesian Reasoning and Machine Learning. Cambridge University Press.
Bayesian models of neural information processing
- Kenji Doya (Editor), Shin Ishii (Editor), Alexandre Pouget (Editor), Rajesh P. N. Rao (Editor) (2007), Bayesian Brain: Probabilistic Approaches to Neural Coding. MIT Press.
Probabilistic Models of Cognition
- Griffiths, T. L., & Yuille, A. (2006). A primer on probabilistic inference. Trends in Cognitive Sciences, 10, (online supplement to issue 7). (pdf)
- Probabilistic Models of Cognition [Special Issue]. Trends in Cognitive Sciences, 10(7).
- Tom Griffiths’s reading list on Bayesian methods for cognitive science
- Graduate Summer School Probabilistic Models of Cognition 2011
More Bayesian inference
- Bishop, C. M. (2007). Pattern Recognition and Machine Learning. Springer. Chapters 6-11.
[kernel methods, Gaussian processes, graphical models, approximate inference]
- Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003). Bayesian Data Analysis (2nd ed.). Chapman and Hall/CRC.
[hierarchical models, shrinkage, priors, mixture models, Laplace approximation, Bayesian vs. classical inference]
- Jaynes, E.T. (2003). Probability Theory. The Logic of Science. Cambridge University Press.
- Petris, G., Petrone, S., Campagnoli, P. Dynamic Linear Models with R (2009). Springer.
[state-space models, dynamic linear models, filtering, smoothing, Kalman]
- Range, H. P., Dale, M. M., Ritter, J. M., Flower, R. J. Pharmacology. Section 4.
[drug action in the central nervous system, neurotransmitters, drugs, addiction, abuse]
- Huettel, Scott A., Allen W. Song, and Gregory McCarthy. Functional Magnetic Resonance Imaging. Sinauer Associates, 2009.
[Chapters 1 to 5 describe the general physical principles of MRI in an accessible way to non-physicists, while chapters 6 and 7 explain the physiological and physical basis of the BOLD contrast mechanism comprehensively.]
- Haacke, E. Mark, Robert W. Brown, Michael R. Thompson, Ramesh Venkatesan, and Norman Cheng. Magnetic Resonance Imaging: Physical Principles and Sequence Design. Wiley John + Sons, 2013.
[The reference book on MR physics, also known as the ‘green Bible’, as it used to be green in the most recent 1999 edition. Parts of it need a lot of physical intuition, and it does not focus particularly on fMRI. However, it is well-written and also covers the mathematical prerequisites needed to understand MRI.]
- Bernstein, Matt A., Kevin F. King, and Xiaohong Joe Zhou. Handbook of MRI Pulse Sequences. Elsevier, 2004.
[With mathematical rigour, fundamental concepts of image generation and reconstruction, including artefacts, are explained in this book. Very clearly written, but the main audience are researchers interested in writing pulse sequences for the MR scanner themselves.]
- Luck, Steve. An Introduction To The Event-Related Potential Technique. MIT Press, 2005.
[The book provides detailed, practical advice about how to design, conduct, and interpret EEG/ERP experiments.]