Neurophysics 1

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.

2 Topics

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

3 Examination

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

5 Literature

Necessary

  • Thomas Trappenberg, Fundamentals of Computational Neuroscience, Oxford Univ. Press, Oxford (2010).

Recommended

  • Purves D., Augustine GJ., Fitzpatrick D., et.al., Neuroscience, Sinauer, Sunderland (2008).

Back to teaching

Advertisements