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.
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).
| Cope, Elise C; LaMarca, Elizabeth A; Monari, Patrick K et al. (2018) Microglia Play an Active Role in Obesity-Associated Cognitive Decline. J Neurosci 38:8889-8904 |
| Vodrahalli, Kiran; Chen, Po-Hsuan; Liang, Yingyu et al. (2018) Mapping between fMRI responses to movies and their natural language annotations. Neuroimage 180:223-231 |
| Aly, Mariam; Chen, Janice; Turk-Browne, Nicholas B et al. (2018) Learning Naturalistic Temporal Structure in the Posterior Medial Network. J Cogn Neurosci 30:1345-1365 |
| Miller, Kevin J; Botvinick, Matthew M; Brody, Carlos D (2017) Dorsal hippocampus contributes to model-based planning. Nat Neurosci 20:1269-1276 |
| Srivastava, Vaibhav; Feng, Samuel F; Cohen, Jonathan D et al. (2017) A martingale analysis of first passage times of time-dependent Wiener diffusion models. J Math Psychol 77:94-110 |
| Baldassano, Christopher; Chen, Janice; Zadbood, Asieh et al. (2017) Discovering Event Structure in Continuous Narrative Perception and Memory. Neuron 95:709-721.e5 |
| Nguyen, Jeffrey P; Linder, Ashley N; Plummer, George S et al. (2017) Automatically tracking neurons in a moving and deforming brain. PLoS Comput Biol 13:e1005517 |
| Chan, Stephanie C Y; Applegate, Marissa C; Morton, Neal W et al. (2017) Lingering representations of stimuli influence recall organization. Neuropsychologia 97:72-82 |
| Song, Alexander; Charles, Adam S; Koay, Sue Ann et al. (2017) Volumetric two-photon imaging of neurons using stereoscopy (vTwINS). Nat Methods 14:420-426 |
| Chen, Janice; Leong, Yuan Chang; Honey, Christopher J et al. (2017) Shared memories reveal shared structure in neural activity across individuals. Nat Neurosci 20:115-125 |
Showing the most recent 10 out of 55 publications
