Computational Neuroscience

Course Aim

This course introduces basic concepts and methods of computational neuroscience based on theory and a sampling of important scientific papers.

Course Description

Explore topics in computational neuroscience, from single neuron properties to networks of integrate-and-fire neurons. Review the biophysical properties of neurons and extend these findings to cable theory and passive dendrite simulations. Study excitability based on the Hodgkin-Huxley model of the action potential and the contributions of various other ion channels. Review phase space analysis, reaction-diffusion modeling and simulating calcium dynamics. Model single neurons, neuronal populations, and networks using NEURON software. Discuss seminal papers associated with each topic, and produce reports on modeling exercises.

Course Contents

1. Introduction and the NEURON simulator
2. Basic concepts and the membrane equation
3. Linear cable theory and passive dendrites
4. Synapses and passive synaptic integration
5. Ion channels and the Hodgkin-Huxley model
6. Neuronal excitability and phase space analysis
7. Exercise evaluations and discussion of student selected modeling papers
8. Reaction-diffusion modeling and calcium dynamics
9. Active dendrite modeling and parameter searching
10. Integrate-and-fire neurons and network modeling
11. Oscillations and microcircuit modeling
12. Large-scale network modeling
13. Exercise evaluations and discussion of student selected modeling papers


Active participation to textbook discussions in class (40%), reports on modeling papers (40%), written exercises (20%).

Prerequisites or Prior Knowledge

Requires introductory neuroscience course or equivalent with background knowledge in computational methods, programming, mathematics.


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 Books

Computational Modeling Methods for Neuroscientists, edited by Erik De Schutter (MIT Press 2010)

Research Specialties