1 Course description
The course introduces the biophysics of neurons and neuronal networks, learning, as well as neurocomputational modeling. The material is based on the textbook of Thomas Trappenberg: Fundamentals of Computational Neuroscience, chapters 1-5 and on additional scripts that will be distributed during the course.
1. Electrical properties of cells, membranes (Nernst) potential, Goldman equilibrium
2. Action potential, Na- and K-currents, permeability changes, ‘voltage clamp’ experiments
3. Hodgkin-Huxley model of action potential generation
4. Cable theory of dendritic signal processing
5. Simple neuron models: integrate-and-fire-type neurons, binary neurons, rate models
6. The neural code: spikes, spike trains, population coding, time vs. rate code
7. Synapses, LTP, LTD
8. Hebbian learning, neural networks
Written exam at the end of the course. Homework exercises can yield up to one point bonus of the final grade.
4 Prior knowledge
Fourier analysis, differential equations, thermodynamics, electrodynamics
- Thomas Trappenberg, Fundamentals of Computational Neuroscience, Oxford Univ. Press, Oxford (2010).
- Purves D., Augustine GJ., Fitzpatrick D., et.al., Neuroscience, Sinauer, Sunderland (2008).