Materials for CBU Methods Lecture

The following references are the papers mentioned in the slides or material that I generally recommend students to look into.

References

• Alon, 2009, How To Choose a Good Scientific Problem, https://www.cell.com/molecular-cell/fulltext/S1097-2765(09)00641-8

• Blohm et al., 2020, A How-to-Model Guide for Neuroscience https://www.eneuro.org/content/7/1/ENEURO.0352-19.2019  

• Brown, 2015, On unifiers, diversifiers, and the nature of pattern recognition https://www.sciencedirect.com/science/article/pii/S0167865515001312  

• Doerig et al., 2023, The neuroconnectionist research programme https://www.nature.com/articles/s41583-023-00705-w

• Levenstein et al., 2021, On the Role of Theory and Modeling in Neuroscience https://www.jneurosci.org/content/43/7/1074 

• Mehrer et al., 2020, Individual differences among deep neural network model https://www.nature.com/articles/s41467-020-19632-w 

• Richards et al., 2019, A deep learning framework for neuroscience https://www.nature.com/articles/s41593-019-0520-2 

• Wang, 2025, Theoretical Neuroscience: Understanding Cognition https://www.routledge.com/Theoretical-Neuroscience-Understanding-Cognition/Wang/p/book/9781032604817 

• Wilson et al., 2017, Good enough practices in scientific computing https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005510 

• Yang & Wang, 2020, Artificial Neural Networks for Neuroscientists: A Primer https://www.sciencedirect.com/science/article/pii/S0896627320307054 

Example research projects

• Achterberg, Akarca et al., 2023, Spatially embedded recurrent neural networks reveal widespread links between structural and functional neuroscience findings https://www.nature.com/articles/s42256-023-00748-9 

• Ali et al., 2022, Predictive coding is a consequence of energy efficiency in recurrent neural networks https://www.sciencedirect.com/science/article/pii/S2666389922002719 

• Francl & McDermott, 2022, Deep neural network models of sound localization reveal how perception is adapted to real-world environments https://www.nature.com/articles/s41562-021-01244-z 

Additional resources

• In context of a recent paper we have made the following list of relevant research institutes and online resources: https://github.com/8erberg/NeuroAI_Trainee_Resources 

• More generally, I discuss additional papers and research on my subpage on NeuroAI: https://www.jachterberg.com/neuroai 

• Generally speaking, writing code is of course a key part of modelling. In the future I want to make a more specific overview of relevant content for students, but until then, these three resources on scientific computing might be helpful:

  • Wilson et al., 2017, Good enough practices in scientific computing https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005510 

  • The Good Research Code Handbook https://goodresearch.dev/index.html   

  • Friends Don't Let Friends Make Bad Graphs https://github.com/cxli233/FriendsDontLetFriends