Almost no-one coming to computational neuroscience has a degree in computational neuroscience: most have degrees in biology, physics, mathematics or AI. Therefore, I often receive the question what good introductory material is. Here, I compiled a list of some introductory material I like.
General introduction into computational neuroscience by the Bernstein Centers in Germany:
Explanation of Aeon Magazine about the importance of the definition of information by Claude Shannon:
The recent Neuromatch Academy provides a complete set of tutorials including videos, assignments, slides and texts on a wide range of computational neuroscience topics:
Lists made by others
- Dan Goodman makes a nice list of freely available resources.
- The Open Source Brain project also keeps track of the recent and available books
My personal preferences
Scholarpedia: peer reviewed short explanations about a plethora of topics, often by the original authors themselves!Dayan, P., & Abbott, L. F. (2001). Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. MIT Press.
- Brian is a great resource for starting making models, and it also has some tutorials available. Great to learn both brain simulations and programming in Python!
- The Brain Dynamics Toolbox is a great tool for dynamical systems
- For an introduction in programming, there are many resources, Datacamp is a good one.
- Kaggle is another good resource.
- This Coursera course by Adrienne Fairhall and Rajesh Rao is a great introduction to computational neuroscience!
Courses in Dutch / in the Netherlands
For some introductory slides in Dutch, here are some lectures slides about modelling of neurons and networks (1, 2, 3) an introduction to decoding, some introductory slides about (un)supervised learning (1, 2) and reinforcement learning (1 ,2).