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 (postprint: europepmc.org/articles/pmc6466618 ).

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 .

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 .

Churchland, P. S., and Sejnowski, T. J. (1990). Neural representation and neural computation. Philosophical Perspectives 4: 343-382. doi: 10.2307/2214198 (preprint: papers.cnl.salk.edu/PDFs/Neural%20Representation%20and%20Neural%20Computation%201990-3325.pdf ).

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

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 .

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 (postprint: europepmc.org/articles/pmc5125387 ).

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 .

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 .

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 .

Kording, K., Blohm, G., Schrater, P., and Kay, K. (2018). Appreciating diversity of goals in computational neuroscience. doi: 10.31219/osf.io/3vy69 .

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/osf.io/dmfhk .

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

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 dspace.mit.edu/handle/1721.1/5782.

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

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 (postprint: archive.org/download/mysticismlogicot00russiala/mysticismlogicot00russiala_bw.pdf ).

Schrater, P., Kording, K., and Blohm, G. (2019). Modeling in Neuroscience as a Decision Process. OSF Preprints. url: osf.io/w56vt

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