The human brain is a biological and computational marvel. It can learn, talk, see, touch, smell, taste, think, feel, and listen, while using less energy than a modern laptop. Our brains accomplish these feats through specialization, where each part of the brain focuses only on one or a few tasks. In this course we will take a tour through the human brain in an effort to learn at least a little bit about every single area in the cortex. Because the human cortex is involved in nearly every aspect of human life, we will touch on a broad set of topics, including vision, language, audition, touch, decision making, and social cognition. We will also discuss methods for mapping the brain and organizing principles that may be at play.
From basic Python programming to probability theory and statistical models. This class will cover topics related to data analysis and modeling and teach students how to perform these procedures using Python. (Formerly NEU 337.)
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.
From basic Python programming to probability theory and statistical models. This class will cover topics related to data analysis and modeling and teach students how to perform these procedures using Python.