A lecture course that surveys computational neuroscience, which is a branch of neuroscience that employs mathematical models, theoretical analysis, and abstractions of the brain to understand the principles that govern development, structure, physiology, and cognitive abilities of the nervous system. We will cover both classical (e.g., GLM, LIF, …
Overview of computer vision problems and techniques for analyzing the content of images and video. Topics include image formation, alignment and warping; stereo vision; feature extraction, matching, and tracking; deep learning-based object detection, recognition, and segmentation; and case studies of practical vision systems.
This graduate course will introduce students to the multidisciplinary field of bionic vision viewed through the lens of computer science, neuroscience, and human-computer interaction.
An overview of theory and research in visual neuroscience. Topics may include: a focus on mammalian vision considering evidence from behavioral and biological approaches.