Suggested further readings#

Bassett, D. S., Zurn, P., and Gold, J. I. (2018). On the nature and use of models in network neuroscience. Nature Reviews Neuroscience 19(9):566-578. doi: 10.1038/s41583-018-0038-8 Closed Access publication (postprint: Open Access publication).

Bennett, M. R., and Hacker, P. M. S. (2003). Philosophical Foundations of Neuroscience, Wiley-Blackwell.

Blohm, G., Kording, K. P., and Schrater, P. R. (2020). A how-to-model guide for Neuroscience. Eneuro, 7(1). doi: 10.1523/ENEURO.0352-19.2019 Open Access publication.

Burgess, J. (1998). Occam’s razor and scientific method. In The Philosophy of Mathematics Today (pp. 195–214). Clarendon Press, Oxford.

Chandrasekhar, S. (2013). Truth and beauty. University of Chicago Press.

Chater, N., and Oaksford, M. (1999). Ten years of the rational analysis of cognition. Trends in cognitive sciences 3(2): 57-65. doi: 10.1016/S1364-6613(98)01273-X Closed Access publication.

Churchland, P. S., and Sejnowski, T. J. (1990). Neural representation and neural computation. Philosophical Perspectives 4: 343-382. doi: 10.2307/2214198 Closed Access publication (preprint: Open Access publication).

Churchland, P. S., and Sejnowski, T. J. (1988). Perspectives on cognitive neuroscience. Science 242(4879): 741-745. doi: 10.1126/science.3055294 Closed Access publication.

Cichy, R. M., and Kaiser, D. (2019). Deep neural networks as scientific models. Trends in cognitive sciences, 23(4), 305-317. doi: 10.1016/j.tics.2019.01.009 Open Access publication.

Dayan, P. (2005). Theoretical Neuroscience: Computational And Mathematical Modeling of Neural Systems. MIT Press.

Feldman, J. (2016). The simplicity principle in perception and cognition. Wiley Interdisciplinary Reviews: Cognitive Science 7(5): 330-340. doi: 10.1002/wcs.1406 Closed Access publication (postprint: Open Access publication).

Gillett, C. (2016). Reduction and Emergence in Science and Philosophy. Cambridge University Press.

Goldstein, R. E. (2018). Point of View: Are theoretical results ‘Results’?. Elife 7: e40018. doi: 10.7554/elife.40018 Open Access publication.

Jonas, E., and Kording, K. P. (2017). Could a neuroscientist understand a microprocessor?. PLoS computational biology 13(1): e1005268. doi: 10.1371/journal.pcbi.1005268 Open Access publication.

Josephson, J. R., and Josephson, S. G. (Eds.). (1996). Abductive inference: Computation, philosophy, technology. Cambridge University Press.

Kaplan, D. M. (2011). Explanation and description in computational neuroscience. Synthese 183(3): 339-373. doi: 10.1007/s11229-011-9970-0 Closed Access publication.

Kording, K., Blohm, G., Schrater, P., and Kay, K. (2018). Appreciating diversity of goals in computational neuroscience. doi: 10.31219/ Open Access publication.

Lee, M. D., Criss, A. H., Devezer, B., Donkin, C., Etz, A., Leite, F. P., … and Vandekerckhove, J. (2019). Robust modeling in cognitive science. Computational Brain & Behavior 2(3): 141-153. doi: 10.31234/ Open Access publication.

Lombrozo, T. (2012). Explanation and Abductive Inference. Oxford Handbooks Online. doi: 10.1093/oxfordhb/9780199734689.013.0014 Closed Access publication.

Marr, D., and Poggio, T. (1976). From Understanding Computation to Understanding Neural Circuitry. Artificial Intelligence Laboratory. A.I. Memo. Massachusetts Institute of Technology. AIM-357. Retrieved from

Parker, W. S. (2012). Computer simulation and philosophy of science. Metascience, Vol. 21, pp. 111–114. doi: 10.1007/s11016-011-9567-8 Closed Access publication.

Pearl, J., and Mackenzie, D. (2018). The Book of Why: The New Science of Cause and Effect. Basic Books.

Russell, B. (1917). Mysticism and logic, and other essays. doi: 10.5962/bhl.title.19230 Closed Access publication (postprint: Open Access publication).

Schrater, P., Kording, K., and Blohm, G. (2019). Modeling in Neuroscience as a Decision Process. OSF Preprints. url:

Simon, H. A. (1969). The sciences of the artificial MIT Press. Cambridge, MA.

Trappenberg, T. (2009). Fundamentals of computational neuroscience. OUP Oxford.

Wilson, R. C., and Collins, A. G. (2019). Ten simple rules for the computational modeling of behavioral data. Elife 8: e49547. doi: 10.7554/eLife.49547 Open Access publication