Training in Computational Neuroscience

From Biology to Model and Back Again

Four training programs have been funded by the Blueprint for 2011-2016 in the area of Computational Neuroscience. These programs provide training in both experimental neuroscience and in the theories and principles of the physical, computer, mathematical, or engineering sciences that are necessary to develop models and test them experimentally. The programs have both undergraduate and pre-doctoral components, and optional summer courses. By targeting undergraduates, these programs encourage students in quantitative sciences to pursue neuroscience research early in their careers, and conversely, encourage students in the biological or behavioral sciences to become educated in quantitative sciences and computational neuroscience methods.

Brandeis University

Project Director: Eve Marder, Ph.D.
NIDA T90 Grant: Project Information

The goal of the program in Computational Neuroscience at Brandeis University is to identify early career students and provide them with the formal and informal education and training they will need to develop into the next generation of computational/quantitative neuroscientists. The program consists of a full-year research and education program for undergraduates and a  Ph.D. training program. The training faculty has research expertise from human cognition to cellular and molecular neuroscience and was chosen because of their demonstrated commitment to the use of theoretical and computational methods to understand the nervous system in health and disease. Students take courses in computational neuroscience, obtain skills in building models of neurons, synapses, and networks, and employ these in a variety of independent research projects. Two cohorts of prospective trainees are to be targeted: 1) Individuals in degree programs in Physics, Math, and Computer Science who wish to receive training and work in neuroscience; and 2) Individuals in degree programs in Biology, Biochemistry, Neuroscience, or Psychology who wish to learn to employ quantitative and computational methods as part of their ability to tackle important problems in neuroscience. In addition to course work and laboratory research, students and trainees are engaged in a large number of other activities designed to enhance their speaking skills, writing skills, and ability to collaborate with other scientists.


Carnegie Mellon University and University of Pittsburgh

Project Directors: Robert E. Kass, Ph.D. (CMU) and Brent Doiron Ph.D. (University of Pittsburgh)
NIDA T90 Grant: Project Information

Interdisciplinary Training in Computational Neuroscience is a broadly-based computational neuroscience training program that provides students with opportunities for cross-disciplinary research in laboratories at Carnegie Mellon University and the University of Pittsburgh. This training program was launched in 2006.  It includes a full-year research and education program, a summer program for undergraduates and a Ph.D. training program. Training faculty come from a wide range of disciplines across both universities and have research interests ranging from dynamical systems to machine learning to biophysics in the quantitative domain, and ranging from neurophysiology to fMRI imaging to molecular biology in the area of neuroscience. The training program is administered by the Center for the Neural Basis of Cognition, an existing center that involves faculty and students at the two universities. Students in the program will gain expertise both in quantitative and biological approaches to the study of the central nervous system.


University of Washington

Program Director:  Adrienne L. Fairhall, Ph.D., Eric Todd Shea-Brown
NIDA T90 Grant: Project Information

This undergraduate and graduate training program in computational neuroscience draws from faculty mentors distributed through many departments and schools, including Physiology and Biophysics, Biological Structure, Computer Science and Engineering, Applied Math, Biology, Psychology and Bioengineering. Support for undergraduate and graduate education and research will foster the ongoing growth of this area, enhance interaction between theorists and experimentalists, expand and integrate coursework in quantitative approaches in neuroscience, enhance interactions between undergraduate and graduate students, enhance opportunities for undergraduate research and draw together the community across campus to strengthen existing interdisciplinary exchange and collaboration. The undergraduate training program will establish a two-year sequence in computational neuroscience for students from neurobiology or from a computational major who will take a common core curriculum including both laboratory neurobiology courses and quantitative courses, where the laboratory section is enhanced with a parallel computational course. The graduate training program will draw from students in the Neurobiology and Behavior interdepartmental program or from departmental graduate programs. The program will train young neuroscientists to use mathematical and computational tools to understand the highly complex, dynamical processing capabilities of the brain and neural circuitry. It will also give students in physics, mathematics and computer science deeper insight into the biology of the brain in order to devise more appropriate theoretical models. Advancing this collaborative approach to neuroscience will help us to intervene in brain pathologies and ultimately to create assistive technologies that integrate with nervous system function.


New York University

Program Director: Xiao-Jing Wang, Whee Ky Ma
NIDA T90 Grant: Project Information 

This training program in computational neuroscience will support integrated undergraduate and graduate training, hosted by the Center for Neural Science (CNS) at New York University with participation of faculty in the Departments of Psychology, Mathematics, and Computer Science, and the Institute of Neuroscience at the School of Medicine.  Students will have opportunities to acquire machine learning techniques for data analysis and learn about brain-like AI algorithms.  This training program has several unique features including innovative mentorship methods where graduate trainees mentor undergraduate trainees, new courses and research opportunities designed to specifically link cognitive function and the neurobiology of circuits, and innovative education in the nascent field of computational psychiatry that brings theory and circuit modeling to clinical research in mental health.  This Training Program in Computational Neuroscience (TPCN) aims to prepare trainees for jobs not only in academia, but also in medical and industry research.