The activity of neurons in the brain and the code used by these neurons is described by mathematical neuron models at different levels of detail.
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.
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
Before your course starts, try the new edX Demo where you can explore the fun, interactive learning environment and virtual labs. Learn more.
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 1993 with a thesis on associative memory in networks of spiking neurons. After short postdoctoral stays at Brandeis University and the TU Munich, he joined the EPFL in 1996 as Assistant Professor. Promoted to Associate Professor in 2001, he is since 2006 a full professor with double appointment in Computer Science and Life Sciences. Wulfram Gerstner has been invited speaker at numerous international conferences and workshops. He has served on the editorial board of the 'Journal of Computational Neuroscience', and 'Science', as well as other journals. He conducts research in computational neuroscience with special emphasis on models of spiking neurons, spike-timing dependent plasticity, and reward-based learning in spiking neurons.
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