This proposal is for the renewal of a predoctoral and postdoctoral Quantitative Neuroscience Training Program (QNTP) at Princeton University. Neuroscience research is becoming increasingly quantitative. Formal theoretical techniques are essential for understanding how complex, large-scale interactions between neurons give rise to thought and behavior, and advanced quantitative methods of data analysis are necessary for addressing the increasingly large, multidimensional data sets generated by modern brain imaging techniques (e.g., multiunit recording, fMRI). These methods are also necessary for future progress to be made in understanding, diagnosing, treating and, ultimately, curing brain disturbances that give rise to psychiatric disorders. Unfortunately, the mathematical and computational skills required to address these needs are not a focus of standard neuroscience curricula. Princeton's QNTP is designed to address this need, by providing the next generation of neuroscientists with the necessary mathematical and computational skills for measuring, analyzing, and modeling brain function. The establishment of the QNTP has sparked several developments at Princeton, that (in turn) have accelerated the pace at which the goals of the QNTP are being met. By bringing Princeton's neuroscientists together with faculty in Physics, Mathematics, Computer Science and Engineering, the QNTP helped to spur the formation of the Princeton Neuroscience Institute (PNI) in 2005. The QNTP also helped to inspire the formation (in 2008) of PNI's new free-standing PhD Program in Neuroscience, which incorporates a strong emphasis on classroom and laboratory training in basic quantitative and computational methods during its first two years. These new developments have made it possible for us to refocus the QNTP from its original purpose (providing a foundation in quantitative neuroscience for trainees who are starting out in this area) to providing advanced training in quantitative neuroscience. Specifically, we propose to take the most quantitatively-focused subset of our predoctoral and postdoctoral trainees and provide them with the additional tools and training that they need to excel in computational neuroscience research. This training will be accomplished via advanced quantitative and computational neuroscience elective courses that were developed for the QNTP and are taught by leaders in the field, as well as participation in research seminars, journal clubs, and retreats that are designed to deepen the trainees'knowledge and bolster community among the trainees. PNI faculty have made seminal contributions to quantitative neuroscience, ranging from information-theoretic analyses of neuronal spiking and nonlinear dynamical systems analysis of decision- making to multivariate decoding of human neuroimaging data. The QNTP has been specifically formulated to bring predoctoral and postdoctoral trainees into contact with this expertise and, through this, to catalyze their transformation into full-fledged computational neuroscientists. As with the prior funding period, we are requesting support for four predoctoral trainees and four postdoctoral trainees.

Public Health Relevance

This is an application for support for advanced predoctoral and postdoctoral training in quantitative neuroscience. By instructing our trainees in computational modeling and advanced data analysis techniques, we aim to train scientists that will be able to bridge the key gap in our understanding of how the brain works: how do myriad single molecules and neurons collectively give rise to the mind? This is important for both basic science and clinical science - these modeling and data analysis techniques will give trainees a rich vocabulary and a powerful toolkit for exploring how pathologies of small-scale elements (e.g., deficits in neurotransmission at dopaminergic terminals) can lead to mental disorders (e.g., schizophrenia).

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Institutional National Research Service Award (T32)
Project #
Application #
Study Section
Special Emphasis Panel (ZMH1-ERB-S (01))
Program Officer
Desmond, Nancy L
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Princeton University
Organized Research Units
United States
Zip Code
Yoon, KiJung; Lewallen, Sam; Kinkhabwala, Amina A et al. (2016) Grid Cell Responses in 1D Environments Assessed as Slices through a 2D Lattice. Neuron 89:1086-99
Simony, Erez; Honey, Christopher J; Chen, Janice et al. (2016) Dynamic reconfiguration of the default mode network during narrative comprehension. Nat Commun 7:12141
Chan, Stephanie C Y; Niv, Yael; Norman, Kenneth A (2016) A Probability Distribution over Latent Causes, in the Orbitofrontal Cortex. J Neurosci 36:7817-28
Córdova, Natalia I; Tompary, Alexa; Turk-Browne, Nicholas B (2016) Attentional modulation of background connectivity between ventral visual cortex and the medial temporal lobe. Neurobiol Learn Mem 134 Pt A:115-22
Nguyen, Jeffrey P; Shipley, Frederick B; Linder, Ashley N et al. (2016) Whole-brain calcium imaging with cellular resolution in freely behaving Caenorhabditis elegans. Proc Natl Acad Sci U S A 113:E1074-81
Schwemmer, Michael A; Feng, Samuel F; Holmes, Philip J et al. (2015) A Multi-Area Stochastic Model for a Covert Visual Search Task. PLoS One 10:e0136097
Hanks, Timothy D; Kopec, Charles D; Brunton, Bingni W et al. (2015) Distinct relationships of parietal and prefrontal cortices to evidence accumulation. Nature 520:220-3
Wilson, Robert C; Niv, Yael (2015) Is Model Fitting Necessary for Model-Based fMRI? PLoS Comput Biol 11:e1004237
Schwemmer, Michael A; Lewis, Timothy J (2014) The robustness of phase-locking in neurons with dendro-dendritic electrical coupling. J Math Biol 68:303-40
Wilson, Robert C; Takahashi, Yuji K; Schoenbaum, Geoffrey et al. (2014) Orbitofrontal cortex as a cognitive map of task space. Neuron 81:267-79

Showing the most recent 10 out of 33 publications