Suggested further readings

Bassett, D. S., Zurn, P., & Gold, J. I. (2018). On the nature and use of models in network neuroscience. Nature Reviews. Neuroscience. https://doi.org/10.1038/s41583-018-0038-8

Bennett, M. R., & Hacker, P. M. S. (2003). Philosophical Foundations of Neuroscience. Wiley-Blackwell. Blohm, Gunnar, Konrad P. Kording, and Paul R. Schrater. 2020. “A How-to-Model Guide for Neuroscience.” eNeuro 7 (1). https://doi.org/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: Aesthetics and Motivations in Science. University of Chicago Press. Chater, N., & Oaksford, M. (1999). Ten years of the rational analysis of cognition. Trends in Cognitive Sciences, 3(2), 57–65.

Churchland, P. S., & Sejnowski, T. J. (1990). Neural Representation and Neural Computation. Philosophical Perspectives, Vol. 4, p. 343. https://doi.org/10.2307/2214198

Churchland, P. S., & Sejnowski, T. J. (1988). Perspectives on cognitive neuroscience. Science, 242(4879), 741–745. Cichy, Radoslaw M., and Daniel Kaiser. 2019. “Deep Neural Networks as Scientific Models.” Trends in Cognitive Sciences. https://doi.org/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.

Gillett, C. (2016). Reduction and Emergence in Science and Philosophy. Cambridge University Press. Goldstein, R. E. (2018). Are theoretical results “Results”? eLife, Vol. 7. https://doi.org/10.7554/elife.40018

Jonas, E., & Kording, K. P. (2017). Could a Neuroscientist Understand a Microprocessor? PLoS Computational Biology, 13(1), e1005268.

Josephson, J. R., & Josephson, S. G. (Eds.). (1994). Abductive inference. Cambridge: Cambridge University Press.

Kaplan, David Michael. 2011. “Explanation and Description in Computational Neuroscience.” Synthese. https://doi.org/10.1007/s11229-011-9970-0.

Kording, Konrad, Gunnar Blohm, Paul Schrater, and Kendrick Kay. n.d. “Appreciating Diversity of Goals in Computational Neuroscience.” https://doi.org/10.31219/osf.io/3vy69.

Lee, Michael David, Amy H. Criss, Berna Devezer, Chris Donkin, Alexander Etz, Fabio P. Leite, Dora Matzke, et al. n.d. “Robust Modeling in Cognitive Science.” https://doi.org/10.31234/osf.io/dmfhk.

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

Marr, D., & Poggio, T. (1976). From Understanding Computation to Understanding Neural Circuitry. Artificial Intelligence Laboratory. A.I. Memo. Massachusetts Institute of Technology. AIM-357. Retrieved from https://dspace.mit.edu/handle/1721.1/5782

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

Pearl, J., & 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. https://doi.org/10.5962/bhl.title.19230

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

Simon, H. A. (1996). The Sciences of the Artificial. MIT Press.

Trappenberg, Thomas. 2010. Fundamentals of Computational Neuroscience. Oxford University Press.

Wilson, Robert C., and Anne Ge Collins. 2019. “Ten Simple Rules for the Computational Modeling of Behavioral Data.” eLife 8 (November). https://doi.org/10.7554/eLife.49547.