Computational Cognitive Neuroscience

About the Program
The brain, and particularly the human/primate brain, is arguably one of the most complex systems in the known universe. Huge progress has been made in the fields of Psychology and Neuroscience to understand the workings of the brain and its relationship to behavior. With the advent of new imaging technologies to record non-invasively and at much lower cost, datasets at huge scales are available to researchers across the world. At the same time, behavioral data from social media, cellphone, and credit cards are accessible at unprecedented temporal and spatial scales with millions and even billions of datapoints. Coupled with these enormous and complex datasets, the analysis tools to analyze these data have also become more complex, such as deep neural networks, Bayesian networks and Boltzmann machines. The Computational Cognitive Neuroscience program provides the requisite skills to become proficient at handling these large and complex data, along with the complex computational analysis tools needed to make progress in our understanding of brain and behavior.

The Computational Cognitive Neuroscience graduate program at the University of Chicago is designed to provide the training and research opportunities for the next generation of computational cognitive neuroscientists. The program will provide students with training in basic neuroscience, cognition and computational techniques to tackle the incredible and daunting challenge in trying to understand such a complex system and complex multidimensional behavior.

Faculty Advisers in Computational Cognitive Neuroscience: Edward S. Awh, Wilma Bainbridge, Akram Bakkour, Marc Berman, Leslie M. Kay, Yuan Chang Leong, Greg Norman, Howard Nusbaum, Brian Prendergast, Monica Rosenberg, Steven Shevell, Edward Vogel, Jai Yu