This application requests renewal of support for undergraduate and graduate training programs in computational neuroscience (TPCN) at both Carnegie Mellon University (CMU) and the University of Pittsburgh (Pitt), and for a summer school in computational neuroscience for undergraduates, which will be available to students coming from colleges and universities throughout the United States. The TPCN will administered by the Center for the Neural Basis of Cognition (CNBC), an umbrella organization operated jointly by CMU and Pitt that was established in 1994 to foster interdisciplinary research on the neural mechanisms of brain function, which now comprises 107 faculty having appointments in 20 departments. 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. In this effort, widespread development and adoption of new computational methods has become essential to progress. The primary goal of TPCN programs is to help train a new generation of interdisciplinary neuroscientists with strong quantitative skills. A second goal is the incorporation of computational and data analytic principles into the field of neuroscience through enhanced training at the undergraduate and graduate level. Trainees will work in vertically integrated, cross-disciplinary research teams. Graduate students will take courses in cognitive neuroscience, neurophysiology, and systems neuroscience; they will satisfy a depth requirement in quantitative methodology of their choice (involving computer science, engineering, mathematics, and/or statistics); they will have extended experience in at least one experimental laboratory; and they will take part in journal clubs and seminars within the large Pittsburgh computational neuroscience community. Year-long undergraduates will take courses in mathematics, computer programming, statistics, and neuroscience; they will take an additional course in neuroscience or psychology and a course in computational neuroscience; and they will complete a year-long research project. In addition, they will complete the summer program. Undergraduate trainees in the summer program will sit through a series of lectures on topics in computational neuroscience, including tutorials in Matlab, statistical methods, fundamentals of differential equations, and ideas of neural coding, and will complete a research project. All trainees will receive training in responsible conduct of research. Across 5 years of funding, TPCN will support 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.
| Vinci, Giuseppe; Ventura, Valérie; Smith, Matthew A et al. (2018) Adjusted regularization of cortical covariance. J Comput Neurosci 45:83-101 |
| Rothe, Anselm; Deverett, Ben; Mayrhofer, Ralf et al. (2018) Successful structure learning from observational data. Cognition 179:266-297 |
| Klaus, Andreas; Martins, Gabriela J; Paixao, Vitor B et al. (2017) The Spatiotemporal Organization of the Striatum Encodes Action Space. Neuron 95:1171-1180.e7 |
| Friedrich, Johannes; Zhou, Pengcheng; Paninski, Liam (2017) Fast online deconvolution of calcium imaging data. PLoS Comput Biol 13:e1005423 |
| Cheyette, Samuel J; Plaut, David C (2017) Modeling the N400 ERP component as transient semantic over-activation within a neural network model of word comprehension. Cognition 162:153-166 |
| Li, Yuanning; Richardson, Robert Mark; Ghuman, Avniel Singh (2017) Multi-Connection Pattern Analysis: Decoding the representational content of neural communication. Neuroimage 162:32-44 |
| Aminoff, Elissa M; Li, Yuanning; Pyles, John A et al. (2016) Associative hallucinations result from stimulating left ventromedial temporal cortex. Cortex 83:139-44 |
| Hirshorn, Elizabeth A; Li, Yuanning; Ward, Michael J et al. (2016) Decoding and disrupting left midfusiform gyrus activity during word reading. Proc Natl Acad Sci U S A 113:8162-7 |
| Zhou, Pengcheng; Burton, Shawn D; Snyder, Adam C et al. (2015) Establishing a Statistical Link between Network Oscillations and Neural Synchrony. PLoS Comput Biol 11:e1004549 |
| Tripathy, Shreejoy J; Burton, Shawn D; Geramita, Matthew et al. (2015) Brain-wide analysis of electrophysiological diversity yields novel categorization of mammalian neuron types. J Neurophysiol 113:3474-89 |
Showing the most recent 10 out of 27 publications
