We propose an undergraduate and graduate (NRSA and non-NRSA) training program in computational neuroscience. Our campus has a rich history and an enonnous breadth of active teaching and research in this area, with 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 courseworic 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 our already excellent interdisciplinary exchange and collaboration. Our undergraduate training program will establish a two-year sequence in computational neuroscience, with entry points for up to 12 trainees yearly either from neurobiology or from a computational major (Physics, Computer Science and Engineering, Applied and Computational Mathematics). In fall quarter students attend a series of seminars to introduce them to faculty research. Trainees wiil take a common core cunicuium including both laboratory neurobiology courses and quantitative courses, where the laboratory section is enhanced with a parallel computational course. Choice of additional electives in an individualized curriculum is guided by a mentoring committee. All students will complete at least 1 and preferably 4 quarters of mentored laboratory research. Our graduate training program will support up to 6 students joining either from the Neurobiology &Behavior interdepartmental program or from departmental graduate programs. Students will apply for training grant support at the end of the first year and can7 out a core curriculum consisting of two neurobiology courses and two quantitative courses. Individually tailored curricula including electives selected from offerings in computational neuroscience, mathematics, computer science and physics will be devised in consultation with a mentoring committee. A biweekly joumal club will survey mathematical and systems neuroscience papers and allow student research presentations. Students will have teaching opportunities in new computational courses in the undergraduate Neurobiology program. All trainees will attend a monthly seminar and and present their research at an annual retreat. The program will be directed by Assoc. Prof. Adrienne Falrhall, Physiology and Biophysics, and a leadership team of Prof. Bill Moody, Professor of Biology, Director of the Undergraduate Neurobiology Program;Assoc. Prof. David Peri

Public Health Relevance

This training grant aims to 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 arid ultimately to create assistive technologies that integrate with nervous system function.

Agency
National Institute of Health (NIH)
Institute
National Institute on Drug Abuse (NIDA)
Type
Interdisciplinary Regular Research Training Award (R90)
Project #
5R90DA033461-02
Application #
8312489
Study Section
Special Emphasis Panel (ZDA1-MXL-F (10))
Program Officer
Volman, Susan
Project Start
2011-09-01
Project End
2016-08-31
Budget Start
2012-09-01
Budget End
2013-08-31
Support Year
2
Fiscal Year
2012
Total Cost
$284,410
Indirect Cost
$19,060
Name
University of Washington
Department
Physiology
Type
Schools of Medicine
DUNS #
605799469
City
Seattle
State
WA
Country
United States
Zip Code
98195
Oleskiw, Timothy D; Pasupathy, Anitha; Bair, Wyeth (2014) Spectral receptive fields do not explain tuning for boundary curvature in V4. J Neurophysiol 112:2114-22