9.29J | Spring 2004 | Undergraduate

Introduction to Computational Neuroscience


Course Meeting Times

Lectures: 2 sessions / week, 1.5 hours / sessions

Course Philosophy

The central assumption of computational neuroscience is that the brain computes. What does that mean? Generally speaking, a computer is a dynamical system whose state variables encode information about the external world. In short, computation equals coding plus dynamics. Some neuroscientists study the way that information is encoded in neural activity and other dynamical variables of the brain. Others try to characterize how these dynamical variables evolve with time. The study of neural dynamics can be further subdivided into two separate strands. One tradition, exemplified by the work of Hodgkin and Huxley, focuses on the biophysics of single neurons. The other focuses on the dynamics of networks, concerning itself with phenomena that emerge from the interactions between neurons. Therefore computational neuroscience can be divided into three subspecialties: neural coding, biophysics of neurons, and neural networks.


  • Basic biology, chemistry, and physics.
  • Differential equations or permission of instructor. Linear algebra is also desirable.
  • Knowledge of MATLAB® or willingness to learn.

Course Requirements

  • Weekly problem sets
  • Midterm project
  • Final project


We will follow the first six chapters of the book very closely, and the later chapters more sketchily.

Dayan, Peter, and L. F. Abbott.Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. Cambridge, MA: MIT Press, 2001. ISBN: 9780262041997.

Course Info

As Taught In
Spring 2004
Learning Resource Types
Lecture Notes
Problem Sets