Course Coordinator: 
Erik De Schutter
Computational Neuroscience

Computational neuroscience has a rich history going back to the original Hodgkin-Huxley model of the action potential and the work of Wilfrid Rall on cable theory and passive dendrites. More recently networks consisting of simple integrate-and-fire neurons have become popular. Nowadays standard simulator software exists to apply these modeling methods, which can then be used to interpret and predict experimental findings.

This course introduces some standard modeling methods with an emphasis on simulation of single neurons and synapses and an introduction to integrate-and-fire networks. Each theoretical topic is linked to one or more seminal papers that will be discussed in class. A number of simple exercises using the NEURON simulator will demonstrate single neuron and synapse modeling.

This course introduces basic concepts and methods of computational neuroscience based on theory and a sampling of important scientific papers.
Course Content: 
  1. Introduction and the NEURON simulator
  2. Basic concepts and the membrane equation
  3. Linear cable theory
  4. Passive dendrites
  5. Modeling exercises 1
  6. Synapses and passive synaptic integration
  7. Ion channels and the Hodgkin-Huxley model
  8. Neuronal excitability and phase space analysis
  9. Other ion channels
  10. Modeling exercises 2
  11. Reaction-diffusion modeling and calcium dynamics
  12. Nonlinear and adaptive integrate-and-fire neurons
  13. Neuronal populations and network modeling
  14. Synaptic plasticity and learning
Course Type: 
Active participation to textbook discussions in class (40%), reports on modeling papers (40%), written exercises (20%).
Text Book: 
Biophysics of Computation, by Christof Koch (1999) Oxford Press
Neural Dynamics: From Single Neurons to Networks and Models of Cognition, by Wulram Gerstner, Werner M. Kistler, Richard Naud and Liam Paninski (Cambridge University Press 2014)
Reference Book: 
Computational Modeling Methods for Neuroscientists, edited by Erik De Schutter (MIT Press 2010)
Prior Knowledge: 

Requires background knowledge in computational methods, programming, mathematics, and neuroscience.