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.

- Introduction and the NEURON simulator
- Basic concepts and the membrane equation
- Linear cable theory
- Passive dendrites
- Modeling exercises 1
- Synapses and passive synaptic integration
- Ion channels and the Hodgkin-Huxley model
- Neuronal excitability and phase space analysis
- Other ion channels
- Modeling exercises 2
- Reaction-diffusion modeling and calcium dynamics
- Nonlinear and adaptive integrate-and-fire neurons
- Neuronal populations and network modeling
- Synaptic plasticity and learning

Requires prior passes in OIST courses B22 Computational Methods and B26 Introduction to Neuroscience, or similar background knowledge in computational methods, programming, mathematics, and neuroscience.