Program Lead: John Fedota, NIDA
Council: October 22, 2021
The purpose of this concept is to establish new research training programs in computational neuroscience and behavior and to support the continuation of meritorious existing programs for undergraduate and predoctoral level students. It is intended that these training programs will provide integrated research training and education in both experimental neuroscience and in the theories and principles of the physical, computer, mathematical, or engineering sciences that are necessary to develop models, test them experimentally, and use experimental data to refine the models of normal or disordered neural systems or processes. Programs are further expected to stimulate interactions among training faculty from multiple disciplines and departments and to foster development of an integrated curriculum in computational neuroscience at the applicant institution.
The current computational neuroscience training program has been successful since its inception in 2006 resulting in over 200 publications and the training of a generation of undergraduate and graduate students who have gone on to successfully obtain academic and private industry appointments following training.
That said, the demand for computational training has only increased across NIH ICs and additional programs and positions within programs are needed. In addition to expanding the number of awards given, the next iteration of the program will maintain its focus on computational and mathematical modeling of neural systems and include training on big data methodology including the collection, housing and sharing of large data sets.
Finally, the next iteration of training program in computational neuroscience will enhance diversity at each level of the training program:
Applicant diversity: addressed via an explicit Enhancing Workforce Diversity statement in each application and formal alliances between research intensive institutions and institutions with substantial enrollment of neuroscience majors from diverse backgrounds
Programmatic diversity: addressed via flexible, interdisciplinary training programs that span departments and allow for recruitment of PhD students from across disciplines to extend reach and impact
Mentor diversity: addressed via joint mentorship of trainees by theorists and experimentalists