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 .
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