Mail: NW 219.40
52 Oxford St
Cambridge, MA 02138
Cox Lab Website
Members of the Cox Lab
Term: Fall Term 2013-2014. Credit: Half course.
Instructors: David Cox, Jeff Lichtman, Joshua Sanes
Course Level: Primarily for Undergraduates
Description: An introduction to the ways in which the brain controls mental activities. The course covers the cells and signals that process and transmit information, and the ways in which neurons form circuits that change with experience. Topics include the neurobiology of perception, learning, memory, language, emotion, and mental illness.
Note: This course, when taken for a letter grade, meets the General Education requirement for Science of Living Systems or the Core area requirement for Science B. The course is open to students with little formal training in biology.
Meetings: Tu., Th., 10-11:30
Term: Fall Term; Repeated Spring Term 2013-2014. Credit: Half course.
Instructor: David Cox
Course Level: Graduate Course
We recognize visual objects with such ease that it is easy to overlook what an impressive computational feat this represents. Any given object in the world can cast an effectively infinite number of different images onto the retina, depending on its position relative to the viewer, the configuration of light sources, and the presence of other objects in the visual field. In spite of this extreme variation, biological visual systems are able to effortlessly recognize at least hundreds of thousands of distinct object classesa feat that no current artificial system can come close to achieving. My laboratory seeks to understand the underpinnings of visual object recognition through a concerted effort on two fronts. First, we endeavor to understand the workings of biological visual systems using a variety of experimental techniques, ranging from microelectrode recordings to visual psychophysics. Second, we attempt to instantiate what we have learned into artificial object recognition systems, leveraging recent advances in parallel computing to build systems that begin to approach the scale of natural biological systems. By combining reverse- and forward-engineering approaches, we hope to accelerate progress in both domains.