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

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 by Prof. Sebastian Seung from his notes on neural coding: Linear models.)

Course Highlights

This course features a selection of downloadable lecture notes, and problem sets in the assignments section.

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.

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Staff

Instructor:
Prof. Sebastian Seung

Course Meeting Times

Lectures:
Two sessions / week
1.5 hours / session

Level

Undergraduate

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