---
title: "Suggested further readings"
source: Neuromatch Computational Neuroscience
source_url: https://github.com/NeuromatchAcademy/course-content
licence: CC-BY-4.0
domain: foundations
subdomain: neuromatch-compneuro
date_added: 2026-04-25
---

# 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](https://doi.org/10.1016/j.tics.2017.03.011) {{ closed_access }} (preprint: bioRxiv doi: [10.1101/079798](https://doi.org/10.1101/079798) {{ open_access }}).

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](https://doi.org/10.7554/eLife.49547) {{ open_access }}.

#### 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/ ](https://www.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](https://www.inference.org.uk/itprnn/book.pdf).

### 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](https://doi.org/10.1214/09-SS054) {{ open_access }}.

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](https://www.inference.org.uk/itprnn/book.pdf).

### 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](https://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](https://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](https://doi.org/10.1152/jn.90545.2008) {{ open_access }}.
