The University of Chicago has a long tradition of innovative research in the neurosciences. K. C. Cole developed the voltage clamp here, Stephen Polyak and C. J. Herrick did pioneering work on the anatomy of the retina and brain, and Jack Cowan and Hugh Wilson were among the first to develop mathematical analyses of the dynamics of cortical neurons using non-linear dynamics. This tradition is continued by the Ph.D. Program in Computational Neuroscience, which provides an interdepartmental and interdivisional focus for multidisciplinary training in neuroscience.

Computational neuroscience is a relatively new area of inquiry that is concerned with how components of animal and human nervous systems interact to produce behaviors. It relies on quantitative and modeling methods to understand the function of the nervous system, natural behaviors, and cognitive processes, and to design human-made devices that duplicate behaviors.

Coursework in computational neuroscience can prepare students for research in neurobiology, psychology, or in the mathematical or engineering sciences. 

Students in the program rotate through two or three laboratories before choosing one for their thesis work near the end of the first year. About a year later, they qualify for Ph.D. candidacy by presenting and defending a thesis proposal. Most of the remaining time is devoted to thesis research, although students also take electives and can take training in teaching or other professional skills.

The average time to Ph.D. is 5.7 years. Since its inception in 2001, the Program in Computational Neuroscience has awarded 47 Ph.D. degrees. 

Graduates from this program move to traditional academic careers, to careers in biomedical research or engineering, or to opportunities in the corporate world.