Computational Neuroscience Resources

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:




neuromatch academy logo


The recent Neuromatch Academy provides a complete set of tutorials including videos, assignments, slides and texts on a wide range of computational neuroscience topics:

World Wide Neuro keeps track of all online presentations and workshops in neuroscience!


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.
  • Gerstner, W., Kistler, W. M., Naud, R., & Paninski, L. (2014). Neuronal Dynamics (freely available online) 
  • Izhikevich, E. M. (2007). Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting. MIT Press. (online)

Online tutorials

Linear Algebra and more essential mathematics

In many Dutch universities, BSc curricula in Biology do not include linear algebra (i.e. doing calculations with vectors/matrices) or Fourier analysis (i.e. looking what frequency oscillations are in a signal). Here are some good introductory courses


  • 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!

Machine learning

  • A great ‘normal language’ explanation: vas3k
  • A great starting kit in Python: scikit-learn

Courses in Dutch / in the Netherlands

Physical courses in the Netherlands: Marieke van Vugt, Jorge Mejias and me try to keep track of all computational neuroscience university courses in the Netherlands. Please take a look here!

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).