This course introduces students to applications of computational neuroscience in a workshop format. It is designed for students who are primarily experimentalists and would like to use computational models to understand their data. Each student should come with an experimental research project. By the end of the semester, the goal is to have a fully functioning model of the experimental data that have been collected. The course is structured as follows: (i) the first few classes will be lectures on the basics of modeling; (ii) each student will then present their project and their preliminary modeling ideas; (iii) another series of lectures will focus on teaching material specialized for the student projects; (iv) students will give a final presentation of their modeling results at the end of the semester. Grading is based on class participation, final presentations, and a written report due at the end of the semester. 

Syllabus

Class 1 (9/6): Theoretical foundations (lecture)

Readings:

Gershman, S.J. (2021). Just looking: the innocent eye in neuroscience. Neuron, 109, 2220-2223.

Jonas, E., & Kording, K.P. (2017). Could a neuroscientist understand a microprocessor? PLoS Computational Biology, 13, e1005268.

Class 2 (9/13): Practical foundations (lecture)

This lecture will introduce practical aspects of applied computational neuroscience: data organization, probabilistic programming languages, visualization, model checking, and more.

Readings:

Gelman, A., Vehtari, A., Simpson, D., Margossian, C. C., Carpenter, B., Yao, Y., ... & Modrák, M. (2020). Bayesian workflow. arXiv preprint arXiv:2011.01808.

 Class 3 (9/20): Student presentations introducing research projects + brainstorming

Class 4 (9/27): Student presentations introducing research projects + brainstorming

Class 5 (10/4): Student presentations introducing research projects + brainstorming

Class 6 (10/11): Project-related teaching

Class 7 (10/18): Project-related teaching

Class 8 (10/25): Project-related teaching

Class 9 (11/1): Project-related teaching

Class 10 (11/8): Final project presentations

Class 11 (11/15): Final project presentations

Class 12 (11/29): Final project presentations