Introduction to Computational Neuroscience Winter 2024
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Instructor: William L. Kath, Tech Room M460, x1-8784,

Class times: MW 3:30-4:50 pm, Tech M416

Office hours: TBA

Textbook: There is no actual required textbook. Useful references are
  1. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems by Peter Dayan and L. F. Abbott
  2. Mathematical Physiology by James Keener and James Sneyd (access from inside NU's network or use VPN)
  3. Mathematical Foundations of Neuroscience by G. Bard Ermentrout and David Terman (access from inside NU's network or use VPN)

All references are available online through the Northwestern Library

Assignments: Primarily, Matlab projects. (There may be one or two written problems).
A free Matlab student license can be obtained from Mathworks

Lecture materials are available on Canvas

Course outline
Course Description:

The course will cover the basic computational models of neurons - their passive properties, models of ionic conductances, and the effect of a cell's morphology. Models of synapses (excitatory and inhibitory) will be considered, and we will discuss simplified models of neurons, e.g., integrate-and-fire models. We will consider the behavior of networks of neurons, how these can be approximated with firing rate models, and models of plasticity and learning.

Throughout the course, computer demonstrations will be used to explore each concept. These will be performed with Matlab or the NEURON simulation package (http://www.neuron.yale.edu/neuron) participants will have the opportunity to try these packages.