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Computational Cognitive Neuroscience (and Computational Psychiatry) -- Spring 2020

Instructor: Dr Peggy Seriès
Lectures:on Mondays and Thursdays 11.10 am in Bayes Center room G3
Labs: TBA
TA : Theo Amvrosiadis. Markers: Samuel Rupprechter and Frank Karvelis

This is a 10 points course for MSc level students. No prior knowledge in neuroscience is necessary but some background in statistics, calculus, linear algebra is required, as a well as some knowledge of programming. We will use matlab (or python). The course Neural Computation is recommended as prerequisite (but not compulsory).

Computational Cognitive Neuroscience is a growing research field. The aim of this course is to learn the tools and concepts that can be used to model the relation between the activity of the brain, cognitive processes and dysfunctions such as in mental ilness. This course will appeal to students who are interested in the basic principles of computation in the human and animal brain, in particular how we can relate the activity of the brain to perception, behaviour or decision-making and clinical applications (Computational Psychiatry).

This course differs from / complements NC in focussing on 'higher level' processes and phenomena (e.g. decision making) and more conceptual models (even if we'll try to stay as close as possible to neurophysiology). It is a good complement if you are interested in a PhD in computational neuroscience. On the other hand it is more "neuro" than CCS, but also related and a good complement too.

The topics discussed in the course are mostly of interest for academic research and although they have inspired machine learning solutions, they are of little direct applicability. Tools from machine learning, though, are commonly used in this field. Moreover, apart from learning about the brain, you will also learn about numeral modelling of differential equations, random processes, decoding techniques, dynamical systems, Bayesian modelling and more.

This year, the course material will be posted on Learn.


    We will use the book I have edited: Computational Psychiatry: a Primer (MIT Press, to appear in 2020), as textbook.

    If you want to prepare for the course (and haven't taken NC), 2 things would be beneficial:
    i) read a primer about neuroscience, e.g. Brain Facts .
    ii) make sure you can program in Matlab (see below for tutorials). You can use for e.g. : Chris' handout introduction to matlab; or Matlab primer.


  • Two assignments (25% each) which correspond to short scientific reports that you will write based on the matlab assignments (or Python), and one report (50%) which is an essay assignment on a scientific article of your choice.
  • no exam.
  • The assignments will be posted on Learn and I will help you choosing a good scientific article for the essay.

Lectures and Labs

  • The labs will consist mainly of implementation in matlab of simple models of population codes, perception, learning and plasticity and decision making. Attendance and work on this material will help for the assignments.

  • Slides and videos of the lectures will be posted on Learn. The information posted here relates to 2 years ago but can give you an idea of the content of the course if you can't access Learn (yet).

This page is maintained by Peggy Series.