We propose an undergraduate and graduate (NRSA and non-NRSA) training program in computational neuroscience. Our campus has a rich history and an enormous 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 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 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 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. 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 carry 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 journal 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 present their research at an annual retreat. The program will be directed by Assoc. Prof. Adrienne Fairhall, Physiology and Biophysics, and a leadership team of Prof. Bill Moody, Professor of Biology, Director of the Undergraduate Neurobiology Program;Assoc. Prof. David Perkel, Otolaryngology and Physiology and Biophysics;Prof. Fred Rieke, Physiology and Biophysics, former Director of the Physiology and Biophysics graduate program and Asst. Prof. Eric Shea-Brown, Applied Mathematics.

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 and ultimately to create assistive technologies that integrate with nervous system function.

National Institute of Health (NIH)
National Institute on Drug Abuse (NIDA)
Interdisciplinary Research Training Award (T90)
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Special Emphasis Panel (ZDA1-MXL-F (10))
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Volman, Susan
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University of Washington
Schools of Medicine
United States
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König, Seth D; Buffalo, Elizabeth A (2016) Modeling Visual Exploration in Rhesus Macaques with Bottom-Up Salience and Oculomotor Statistics. Front Integr Neurosci 10:23
Pang, Rich; Fairhall, Adrienne L (2015) Let Music Sound while She Doth Make Her Choice. Neuron 87:1126-8
König, Seth D; Buffalo, Elizabeth A (2014) A nonparametric method for detecting fixations and saccades using cluster analysis: removing the need for arbitrary thresholds. J Neurosci Methods 227:121-31
Wander, Jeremiah D; Rao, Rajesh P N (2014) Brain-computer interfaces: a powerful tool for scientific inquiry. Curr Opin Neurobiol 25:70-5
Johnson, L A; Wander, J D; Sarma, D et al. (2013) Direct electrical stimulation of the somatosensory cortex in humans using electrocorticography electrodes: a qualitative and quantitative report. J Neural Eng 10:036021
Wander, Jeremiah D; Blakely, Timothy; Miller, Kai J et al. (2013) Distributed cortical adaptation during learning of a brain-computer interface task. Proc Natl Acad Sci U S A 110:10818-23