Archived Versions

9.29J / 9.912J / 8.261J Introduction to Computational Neuroscience

As taught in: Spring 2004

Voltage modulation versus time in milliseconds.

Data from an experiment on the weakly electric fish Eigenmannia. The frequency of action potential firing increases when the stimulus increases. (Image courtesy of Prof. Sebastian Seung from his notes on neural coding: Linear models.)




Prof. Sebastian Seung

Course Features

Course Description

This course gives a mathematical introduction to neural coding and dynamics. Topics include convolution, correlation, linear systems, game theory, signal detection theory, probability theory, information theory, and reinforcement learning. Applications to neural coding, focusing on the visual system are covered, as well as Hodgkin-Huxley and other related models of neural excitability, stochastic models of ion channels, cable theory, and models of synaptic transmission.

Visit the Seung Lab Web site.

Technical Requirements

Special software is required to use some of the files in this course: .mat, and .m.