The primate visual system is a fertile ground for understanding the neuroscience underlying intelligent behavior. Indeed, much recent progress in machine learning rests upon fundamental concepts learned from the neurophysiology of vision and computational models developed from visual neuroscience. This course uses a combination of lectures, primary literature reading and computer tutorials to develop key concepts underlying computational approaches to intelligent behavior in visual neuroscience. Topics include optimal observer models, heuristics, Fourier analysis, LN models, normalization, signal detection, drift diffusion, efficient coding, Bayesian inference, visual search, metamers, texture models, population coding and recurrent dynamics. Students are expected to have familiarity with Python and linear algebra. Advanced undergraduates may enroll in this course with instructor consent (see pre-requisites in syllabus).
3 units · Letter (ABCD/NP)
The primate visual system is a fertile ground for understanding the neuroscience underlying intelligent behavior. Indeed, much recent progress in machine learning rests upon fundamental concepts learned from the neurophysiology of vision and computational models developed from visual neuroscience. This course uses a combination of lectures, primary literature reading and computer tutorials to develop key concepts underlying computational approaches to intelligent behavior in visual neuroscience. Topics include optimal observer models, heuristics, Fourier analysis, LN models, normalization, signal detection, drift diffusion, efficient coding, Bayesian inference, visual search, metamers, texture models, population coding and recurrent dynamics. Students are expected to have familiarity with Python and linear algebra. Advanced undergraduates may enroll in this course with instructor consent (see pre-requisites in syllabus).
Offered in Spring 2027 at Stanford University.