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About this courseSkip 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: 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.
At a glance
- Institution: EPFLx
- Subject: Computer Science
- Level: Advanced
Calculus, differential equations, probabilities.
Recommended textbook: "NEURONAL DYNAMICS - from single neurons to networks and cognition", Cambridge Univ. Press 2014
- Language: English
- Video Transcript: English
- Associated skills: Pulses, Stochastic Process, Heart Rate, Differential Equations, Computational Neuroscience
What you'll learnSkip What you'll 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