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Neuronal Dynamics


About this course

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: Adding detail: dendrites and synapses

Week 4: Reducing detail: two-dimensional models

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.


Calculus, differential equations, probabilities .

Course Staff

Wulfram Gerstner


Hesam Setareh


Mohammadjavad Faraji


Laureline Logiaco


William Podlaski


Suggested Readings

W. Gerstner, W.M. Kistler, R. Naud and L. Paninski, Neuronal Dynamics: from single neurons to networks and models of cognition. Cambridge Univ. Press, 2014

W. Gerstner and W.M. Kistler, Spiking neuron models: Single neurons, populations, plasticity. Cambridge Univ. Press, 2002

P. Dayan and L.F. Abbott, Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. MIT Press, 2001