This course gives an introduction to the field of theoretical and computational neuroscience with a focus on models of single neurons. Neurons encode information about stimuli in a sequence of short electrical pulses (spikes). Students will learn how mathematical tools such as differential equations, phase plane analysis, separation of time scales, and stochastic processes can be used to understand the dynamics of neurons and the neural code.
Calculus, differential equations, probabilities.
Recommended textbook: "NEURONAL DYNAMICS - from single neurons to networks and cognition", Cambridge Univ. Press 2014
Week 1: A first simple neuron model
Week 2: Hodgkin-Huxley models and biophysical modeling
Week 3: Two-dimensional models and phase plane analysis
Week 4: Two-dimensional models (cont.)/ Dendrites
Week 5: Variability of spike trains and the neural code
Week 6: Noise models, noisy neurons and coding
Week 7: Estimating neuron models for coding and decoding
After studies of Physics in Tübingen and at the Ludwig-Maximilians-University Munich (Master 1989), Wulfram Gerstner spent a year as a visiting researcher at UC Berkeley. He received his PhD in Theoretical Physics from the Technical University Munich in 1…
Free online courses from École polytechnique fédérale de Lausanne
EPFL is the Swiss Federal Institute of Technology in Lausanne. The past decade has seen EPFL ascend to the very top of European institutions of science and technology: it is ranked #1 in E…