Suggested further readings

Generic tips on model fitting in neuroscience:

Palminteri, S., Wyart, V., and Koechlin, E. (2017). The importance of falsification in computational cognitive modeling. Trends in cognitive sciences 21(6): 425-433. doi: 10.1016/j.tics.2017.03.011 (preprint: bioRxiv doi: 10.1101/079798 ).

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 .

On linear regression:

Bishop, C. M., and Nasrabadi, N. M. (2006). Pattern recognition and machine learning (Vol. 4, No. 4, p. 738). New York: springer. Section 3.1 provides all mathematical derivations in depth. Freely available at microsoft.com/en-us/research/people/cmbishop/ .

MacKay, D. J. (2003). Information theory, inference and learning algorithms. Cambridge university press. Chapter 22 (Log-likelihood maximization). Freely available at inference.org.uk/itprnn.

On model selection:

Arlot, S., & Celisse, A. (2010). A survey of cross-validation procedures for model selection. Statistics surveys 4: 40-79. doi: 10.1214/09-SS054 .

MacKay, D. J. (2003). Information theory, inference and learning algorithms. Cambridge university press. Chapter 28 (Model selection and Occam’s razor) . Freely available at www.inference.org.uk/itprnn.

On optimization methods (for LLH maximization or MSE minimization):

Acerbi, L., and Ma, W. J. (2017). Practical Bayesian optimization for model fitting with Bayesian adaptive direct search. Advances in neural information processing systems, 30. Algorithm for optimization problems, with Matlab toolbox available at github.com/lacerbi/bads.

Boyd, S., Boyd, S. P., and Vandenberghe, L. (2004). Convex optimization. Cambridge university press. Freely available at web.stanford.edu/~boyd/cvxbook.

Research example developed in outro:

Wei, K., and Kording, K. (2009). Relevance of error: what drives motor adaptation?. Journal of neurophysiology 101(2): 655-664. doi: 10.1152/jn.90545.2008 .