Introduction to Computational Neuroscience

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.)

Instructor(s)

MIT Course Number

9.29J / 9.912J / 8.261J

As Taught In

Spring 2004

Level

Undergraduate

Cite This Course

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.

Archived Versions

Seung, Sebastian. 9.29J Introduction to Computational Neuroscience, Spring 2004. (MIT OpenCourseWare: Massachusetts Institute of Technology), http://ocw.mit.edu/courses/brain-and-cognitive-sciences/9-29j-introduction-to-computational-neuroscience-spring-2004 (Accessed). License: Creative Commons BY-NC-SA


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