Computational Neuroscience

Spring 1998

Bard Ermentrout

Walt Schneider

Coordinates:

Instructors:

Bard Ermentrout

Walt Schneider


Abstract

The course offers an introduction to modeling methods in neuroscience. It illustrates how models can extend and evaluate neuroscience concepts. Basic techniques of modeling biophysics, excitable membranes, small network and large scale network systems will be introduced. The course begins with a consideration of mathematical models of excitable membranes, including the Hodgkin-Huxley model and simplifications such as the Morris-Lecar and FitzHugh-Nagumo models. It will provide hands-on laboratory experience in modeling membranes, neurons, and neural networks. The course explores the use of differential equations, numerical simulation, and graphical techniques for modeling compartmental and connectionist neural systems. The range of topics include simulations of electrical properties of membrane channels, single cells, neuronal networks and connectionist simulation. Students will be afforded laboratory experience in computer modeling, and they will develop computational neuroscience models in the course. Prerequisites for the course include basic knowledge of calculus, neuroscience, and some computer programming.

Required texts: None

Recommended Texts:

Syllabus