To understand the many disorders of the brain it is necessary to grapple with its complexity. Increasingly large and complicated data sets are being collected, but the tools for analyzing and modeling the data are not yet available. More researchers trained in computational neuroscience are desperately needed. This project supports graduate and undergraduate training programs in computational neuroscience (TPCN) at both Carnegie Mellon University (CMU) and the University of Pittsburgh (Pitt), and a summer school in computational neuroscience for undergraduates, which are available to students coming from colleges and universities throughout the United States. The CMU-Pitt TPCN has 16 training faculty in computational neuroscience, 22 training faculty whose laboratories are primarily experimental, and 20 training faculty whose laboratories are both computational and experimental. At the graduate level the TPCN offers a PhD program in Neural Computation (PNC) and joint PhD programs with CMU?s Department of Statistics (PNC-Stat) and its Machine Learning Department (PNC- MLD), all set within a highly collegial, cross-disciplinary environment of our Center for the Neural Basis of Cognition (CNBC), which is operated jointly by CMU and Pitt. The CNBC was established in 1994 to foster interdisciplinary research on the neural mechanisms of brain function, and now comprises 145 faculty having appointments in 22 departments. At the undergraduate level a substantial pool of local students is supplemented during the summer by a cohort of students from across the country. During this renewal funding period the project is strengthening the role of statistics and machine learning throughout the training programs; (2) revising the summer undergraduate program by creating a didactic two-week ?boot camp? at the beginning, which includes a 20-lecture overview of computational neuroscience; (3) creating online materials, in conjunction with the boot camp, that will serve not only our own students but also the greater world of training in computational neuroscience; and (4) enhancing our minority recruitment by (a) taking advantage of the boot camp and online materials, as well as making promotional visits to targeted campuses, and (b) creating and running a one-year ?bridge? program to better prepare under-represented minorities for PhD programs. TPCN trainees work in vertically integrated, cross-disciplinary research teams. Graduate students take a year- long course in computational neuroscience that bridges modeling and modern statistical machine learning approaches to neuroscience. To ensure their competency in core neuroscience principles they also take courses in cognitive neuroscience, neurophysiology, and systems neuroscience. They then pursue depth in a relevant quantitative discipline, such as computer science, engineering, mathematics, or statistics. Graduate students have extended experience in at least one experimental laboratory, and they take part in journal clubs and seminars within the large Pittsburgh neuroscience community. Year-long undergraduates take courses in mathematics, computer programming, statistics, and neuroscience; they take an additional course in neuroscience or psychology and a course in computational neuroscience; and they complete a year-long research project. In addition, they complete the TPCN summer program. Undergraduate trainees in the summer program go through the boot camp on topics in computational neuroscience, including tutorials in Matlab, statistical methods, fundamentals of differential equations, and ideas of neural coding; they then complete a research project under careful guidance. All trainees will receive training in responsible conduct of research. Across 5 years of funding, the TPCN supports 20 NRSA graduate students, 10 non-NRSA graduate students, 30 undergraduate year-long fellows, and 60 undergraduate summer fellows.
Research in neuroscience is crucial for attacking the causes of neurological and mental health disorders. If the field of neuroscience is to continue its rapid advance, neuroscientists must use, understand, and develop new technologies, acquire and analyze ever larger data sets, and grapple more directly with the complexity of neurobiological systems. The primary goal of these training programs will be to help train a new generation of interdisciplinary neuroscientists with strong quantitative skills.
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| Leeds, Daniel D; Tarr, Michael J (2016) A method for real-time visual stimulus selection in the study of cortical object perception. Neuroimage 133:529-548 |
| Williamson, Ryan C; Cowley, Benjamin R; Litwin-Kumar, Ashok et al. (2016) Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models. PLoS Comput Biol 12:e1005141 |
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| Aminoff, Elissa M; Toneva, Mariya; Shrivastava, Abhinav et al. (2015) Applying artificial vision models to human scene understanding. Front Comput Neurosci 9:8 |
| Ghuman, Avniel Singh; Brunet, Nicolas M; Li, Yuanning et al. (2014) Dynamic encoding of face information in the human fusiform gyrus. Nat Commun 5:5672 |
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| Rager, Danielle M; Alvares, Darren; Birznieks, Ingvars et al. (2013) Generating tactile afferent stimulation patterns for slip and touch feedback in neural prosthetics. Conf Proc IEEE Eng Med Biol Soc 2013:5922-5 |
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