I have here collected various notes. They span my time both as student, instructor and (guest) lecturer.


I did a week of guest lecturing in a graduate course in neurophysics. I dedicated this week to Spike Time Dependent Plasticity, and created a matlab-based exercise. Included is both the pdf, student’s .m-file (so they don’t have to start from scratch) and an m-file containing the solved exercise (teacher’s edition). The object of the exercise is to implement STDP in a small non-recurrent network and show that STDP reduces the influence on noise in the input.
 As to the quality of the assignment, it is possible that the students are either getting too much or too little help. Certainly, it turned out that that a number of them managed to make it all far more complicated than they needed to, giving the impression that the assignment was very advanced.

Mathematical modelling (basic statistics):

A “cheat sheet” including all the formulas (and more) needed at the exam in “Matematisk Modellering 1”, at least in 2007.

Statistics and data analysis (basic statistics)

A note on exactly how, and why, a quantile plot works.
Exercise in neurophysics – spike time dependent plasticity in a feed-forward network
In the spring of 2013, I was asked to teach one week of a neurophysics course offered at my university. For that occasion, I designed an exercise mimicking my own work on spike time dependent plasticity (STDP). It assumes that the students have read part of the scholarpedia article on STDP, and guides them to design a numerical simulation in matlab showing that STDP may be used to filter a noisy signal. The necessary files are:
– The scholarpedia article (if you want to make sure that the students read exactly the same version as I did before designing the exercise):