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9.29J Introduction to Computational Neuroscience | Brain and Cognitive Sciences

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.

Keywords:: ion channels, neural excitability, Cognitive Science, neural coding, Computational Biology, linear systems, Fourier analysis, Neuroscience, dynamics, stochastic models, convolution, correlation, cable theory, information theory, signal detection theory, probability theory

Disciplines:

Social Sciences / Psychology / Cognitive

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Material Type:: Online Course
Date Added to MERLOT:: May 12, 2016
Date Modified in MERLOT:: April 18, 2020
Author:: Seung, Sebastian
Submitter:: Barbra Bied Sperling

Primary Audience:: College General Ed, College Lower Division, College Upper Division
Technical Format:: Website
Mobile Compatibility:: Not specified at this time
Language:: English
Cost Involved:: No
Source Code Available:: No
Creative Commons:: This work is licensed under a Attribution-NonCommercial-ShareAlike 3.0 United States