CS 395 T - Neural Computation (Fall 2017)

Inferring what algorithms are used by existing computational systems. Using black box system identification to understand the function of real neural/brain systems. Using gradient propagation and other methods to understand the function of artificial neural networks.

By the end of this course, you should come away with an understanding of (1) the basic strategies used for inferring function from computational systems; (2) specific tools and techniques for studying the function of real and simulated neural systems; and (3) when to trust real data acquired from noisy systems.

The syllabus for this course.