CPC Zurich 2019
Reading List


Adams, R. A., Huys, Q. J., & Roiser, J. P. (2016). Computational psychiatry: towards a mathematically informed understanding of mental illness. J Neurol Neurosurg Psychiatry, 87(1), 53-63. [PDF]

Huys, Q. J., Moutoussis, M., & Williams, J. (2011). Are computational models of any use to psychiatry?. Neural Networks, 24(6), 544-551. [PDF]

Huys, Q. J., Maia, T. V., & Frank, M. J. (2016). Computational psychiatry as a bridge from neuroscience to clinical applications. Nature neuroscience, 19(3), 404. [PDF]

Huys, Q. J., Maia, T. V., & Paulus, M. P. (2016). Computational psychiatry: from mechanistic insights to the development of new treatments. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 1(5), 382-385. [PDF]

Friston, K. J., Stephan, K. E., Montague, R., & Dolan, R. J. (2014). Computational psychiatry: the brain as a phantastic organ. The Lancet Psychiatry, 1(2), 148-158. [PDF]

Montague, P. R., Dolan, R. J., Friston, K. J., & Dayan, P. (2012). Computational psychiatry. Trends in cognitive sciences, 16(1), 72-80. [PDF]

Paulus, M. P., Huys, Q. J., & Maia, T. V. (2016). A roadmap for the development of applied computational psychiatry. Biological psychiatry: cognitive neuroscience and neuroimaging, 1(5), 386-392. [PDF]

Petzschner, F. H., Weber, L. A., Gard, T., & Stephan, K. E. (2017). Computational psychosomatics and computational psychiatry: Toward a joint framework for differential diagnosis. Biological Psychiatry, 82(6), 421-430. [PDF]

Stephan, K. E., & Mathys, C. (2014). Computational approaches to psychiatry. Current opinion in neurobiology, 25, 85-92. [PDF]

Stephan, K. E., Iglesias, S., Heinzle, J., & Diaconescu, A. O. (2015). Translational perspectives for computational neuroimaging. Neuron, 87(4), 716-732. [PDF]

Stephan, K. E., Schlagenhauf, F., Huys, Q. J., Raman, S., Aponte, E. A., Brodersen, K. H., ... & Friston, K. J. (2017). Computational neuroimaging strategies for single patient predictions. Neuroimage, 145, 180-199. [PDF]

Schwartenbeck, P., & Friston, K. (2016). Computational phenotyping in psychiatry: a worked example. eneuro, 3(4). [PDF]

Wang, X. J., & Krystal, J. H. (2014). Computational psychiatry. Neuron, 84(3), 638-654. [PDF]




Variational Algorithms for Approximate Bayesian Inference, PhD. Thesis, Gatsby Computational Neuroscience Unit, University College London. [Thesis]

Stephan, K. E., Penny, W. D., Daunizeau, J., Moran, R. J., & Friston, K. J. (2009). Bayesian model selection for group studies. Neuroimage, 46(4), 1004-1017. [PDF]


Friston KJ, Mattout J, Truyillon-Barreto N, Ashburner J, Penny WD (2007) Variational free energy and the
Laplace approximation. NeuroImage 34: 220-234. [PDF]

Knill, D. C., & Richards, W. (Eds.). (1996). Perception as Bayesian inference. Cambridge University Press. [Book]

Petzschner, F. H., Glasauer, S., & Stephan, K. E. (2015). A Bayesian perspective on magnitude estimation. Trends in cognitive sciences, 19(5), 285-293. [PDF]

Rao, R. P., & Ballard, D. H. (1999). Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nature neuroscience, 2(1), 79. [PDF]


Huys, Q. J., Guitart-Masip, M., Dolan, R. J., & Dayan, P. (2015). Decision-theoretic psychiatry. Clinical Psychological Science, 3(3), 400-421. [PDF]

Schwartenbeck, P., Passecker, J., Hauser, T. U., FitzGerald, T. H., Kronbichler, M., & Friston, K. J. (2019). Computational mechanisms of curiosity and goal-directed exploration. eLife, 8, e41703. [PDF]



Wolfers, T., Buitelaar, J. K., Beckmann, C. F., Franke, B., & Marquand, A. F. (2015). From estimating activation locality to predicting disorder: a review of pattern recognition for neuroimaging-based psychiatric diagnostics. Neuroscience & Biobehavioral Reviews, 57, 328-349. [PDF]



A Quick Introduction to Markov Chains and Markov Chain Monte Carlo [PDF]

Kaelbling, L. P., Littman, M. L., & Cassandra, A. R. (1998). Planning and acting in partially observable stochastic domains. Artificial intelligence, 101(1-2), 99-134. [PDF]



Kahan, J., & Foltynie, T. (2013). Understanding DCM: ten simple rules for the clinician. Neuroimage, 83, 542-549. [PDF]

Stephan, K. E., Penny, W. D., Moran, R. J., den Ouden, H. E., Daunizeau, J., & Friston, K. J. (2010). Ten simple rules for dynamic causal modeling. Neuroimage, 49(4), 3099-3109. [PDF]

Stephan, K. E., & Friston, K. J. (2010). Analyzing effective connectivity with functional magnetic resonance imaging. Wiley Interdisciplinary Reviews: Cognitive Science, 1(3), 446-459. [PDF]

Frässle, S., Lomakina, E. I., Razi, A., Friston, K. J., Buhmann, J. M., & Stephan, K. E. (2017). Regression DCM for fMRI. Neuroimage, 155, 406-421. [PDF]



Moran, R. J., Kishida, K. T., Lohrenz, T., Saez, I., Laxton, A. W., Witcher, M. R., ... & Montague, P. R. (2018). The protective action encoding of serotonin transients in the human brain. Neuropsychopharmacology, 43(6), 1425. [PDF]


MacDonald, A. W., & Schulz, S. C. (2009). What we know: findings that every theory of schizophrenia should explain. Schizophrenia bulletin, 35(3), 493-508. [PDF]

Adams, R. A., Stephan, K. E., Brown, H. R., Frith, C. D., & Friston, K. J. (2013). The computational anatomy of psychosis. Frontiers in psychiatry, 4, 47. [PDF]

Sterzer, P., Adams, R. A., Fletcher, P., Frith, C., Lawrie, S. M., Muckli, L., ... & Corlett, P. R. (2018). The predictive coding account of psychosis. Biological psychiatry, 84(9), 634-643. [PDF]


Huys, Q. J., Tobler, P. N., Hasler, G., & Flagel, S. B. (2014). The role of learning-related dopamine signals in addiction vulnerability. In Progress in brain research (Vol. 211, pp. 31-77). Elsevier. [PDF]