This course will introduce a collection of computer simulation techniques useful for investigating a variety of cognitive phenomena involving perception, action, learning, and memory. This course is formatted to support interdisciplinary inquiry, with the backgrounds of students expected to vary broadly across the range of such disciplines as computer science, cognitive science, psychology, and neuroscience, as well as other related fields. The learning of both classic and contemporary methods for cognitive modeling will be facilitated by readings, presentations by class participants as well as by the instructor, homework assignments, a term project, and ample interaction and discussion between attendees.

CILO-1: Students will be able to recognize the basic principles of different computational cognitive neuroscience (CCN) modeling, including extensions to parallel distributed processing (PDP), connectionist, and artificial neural network models. CILO-2: Students will be able to understand the cognitive processes such as perception, attention, memory, and decision-making, and how these processes are represented and integrated in the brain. CILO-3: Students will be able to use basic and advanced statistical methods to analyze large-scale neuroimaging datasets, and interpret the results of these analyses in terms of cognitive and neural mechanisms. CILO-4: Students will be able to communicate complex scientific ideas related to computational cognitive neuroscience to both scientific and lay audiences, through written reports and oral presentations.