This proposal is for a training program in Quantitative and Computational Neuroscience (QCN) at Princeton University (R90 undergraduate and non-NRSA predoctoral;T90 NRSA predoctoral). The way neuroscience research is carried out is rapidly changing and becoming much more dependent on, and engaged with, the physical, mathematical and information sciences. New technologies are providing data of unprecedented complexity and scale. fMRI and MEG map activated regions of the human brain with increasing resolution and temporal precision, while multi-electrode recording and optical imaging using voltage and calcium sensors provide detailed information on the spatial patterns of electrochemical activities in neural circuits and single neurons. Increasingly, the questions addressed with these technologies are systems level questions that concern the interactions of many components in networks. How sensory and motor information is represented across the activity of a population of neurons, how working memory and decisions are implemented in neural circuits, and how interacting biochemical pathways in a single synapse can coordinate plasticity and growth are all examples of contemporary network questions in neuroscience. As the focus of neuroscience has evolved to encompass more systems-level functions involving the interplay among large assemblies of interacting elements, the need for more sophisticated mathematical and computational tools has become more acute, both to quantitatively analyze data and to define and test theoretical models. Our training program will address these changes and challenges by providing undergraduate and pre-doctoral instruction in a combined curriculum of formal theoretical techniques and computational methods on the one hand and hands-on inquiry-based project labs and laboratory rotations on the other. Our emphasis is on the interplay of theory and experiment in neuroscience research and how sophisticated analysis techniques can be used to exploit the most advanced instrumentation, in attacking important neuroscience questions. This program will be a cornerstone of Princeton's new Neurosciences Institute emphasizing neural coding and dynamics, and it will bring together faculty from Psychology, Biology, Physics, Chemistry, Engineering and other disciplines to provide a directed curriculum with cutting- edge technology for empowering future neuroscientists with quantitative methods.
| Gershman, Samuel J; Niv, Yael (2015) Novelty and Inductive Generalization in Human Reinforcement Learning. Top Cogn Sci 7:391-415 |
| Schneider, Eve R; Civillico, Eugene F; Wang, Samuel S-H (2013) Calcium-based dendritic excitability and its regulation in the deep cerebellar nuclei. J Neurophysiol 109:2282-92 |
| Arcaro, Michael J; Pinsk, Mark A; Li, Xin et al. (2011) Visuotopic organization of macaque posterior parietal cortex: a functional magnetic resonance imaging study. J Neurosci 31:2064-78 |
| Gershman, Samuel J; Blei, David M; Pereira, Francisco et al. (2011) A topographic latent source model for fMRI data. Neuroimage 57:89-100 |
| Gershman, Samuel J; Blei, David M; Niv, Yael (2010) Context, learning, and extinction. Psychol Rev 117:197-209 |
| Gershman, Samuel J; Niv, Yael (2010) Learning latent structure: carving nature at its joints. Curr Opin Neurobiol 20:251-6 |
| Markowitz, David A; Collman, Forrest; Brody, Carlos D et al. (2008) Rate-specific synchrony: using noisy oscillations to detect equally active neurons. Proc Natl Acad Sci U S A 105:8422-7 |
