Ph.D. Timeline Overview

First Year
During the first year of their Ph.D., all students take the Neuroscience Core Course. The goal of this course is to provide a common foundation so that all students have a strong knowledge base and a common language across the breadth of Neuroscience, which is a highly diverse and multidisciplinary field. To the extent possible, the course aims to teach an overview of all topics through a mix of hands-on laboratory experience, lecture, and computational modeling. Students will also rotate in up to three labs, participate in grant-writing workshops, and attend the Society for Neuroscience Annual Conference.
Second year
By the second year of their Ph.D., students will have joined a research group. Projects that involve collaborations across groups, and thus have students joining more than one research group, are decidedly welcomed. Students also typically teach half-time during their second year, as part of learning to teach and communicate science, and as a part of helping the Neuroscience Institute's educational mission. The other half of their time, students begin to carry out in-depth research and dedicate themselves wholly to this in the summer between their second and third years.
Third year
At the beginning of their third year, Ph.D. students present their thesis proposal at a generals exam, in which they demonstrate their command of their chosen research topic and the existing literature surrounding it, and present a logical plan to address key questions that they have identified.
The third, fourth and fifth years are largely devoted to research. They culminate with the submission of their research papers for publication, and the writing and defense of their Ph.D. thesis. Throughout their time at Princeton, students participate in grant-writing workshops, career workshops, and present their work both locally and in national and international conferences.

Recent Research Examples

Some examples of recently published work in which our students were the first authors are:
  • Findings that one’s own biases and limitations in processing space leak into social cognition, shaping how we imagine other people understand the space around them. (Bio et al., 2018, PNAS)
  • Demonstration that in paradigms involving rapid learning, the computational trade-off between learning episodes and regularities from the environment may be handled by separate anatomical pathways within the hippocampus itself. (Schapiro et al., 2017, Philosophical Transactions of the Royal Society B).
  • Use of fMRI to demonstrate that belief distribution over latent causes is encoded in patterns of brain activity in the orbitofrontal cortex, an area that has been separately implicated in the representations of both states and schemas. (Chan et al., 2016, Journal of Neuroscience).
  • Studies of the function of subpopulations of dopamine neurons that target distinct striatal subregions in the context of an instrumental reversal learning task, and identified key differences in the encoding of reward and choice in dopamine terminals in dorsal versus ventral striatum (Parker et al., 2016, Nature Neuroscience).
  • Collected and analyzed single unit recordings from the dorsomedial striatum of rats performing a spatial working memory task and found that sequential activity was dissociated from stimulus encoding activity. These observations contrast with descriptions of sequential dynamics during similar tasks in other brains areas, and clarify the contribution of the striatum to spatial working memory. (Akhlaghpour et al., 2016, eLife).
  • Findings that the degree of neural pattern change in entorhinal cortex predicts judgments of elapsed time. (Lositsky et al., 2016, eLife).
  • Use of fMRI to examine how visual information is prioritized for processing in the medial temporal lobe (MTL), demonstrating that visual cortex was more coupled with parahippocampal cortex when scenes were attended and more coupled with perirhinal cortex when faces were attended (Cordova et al., 2016, Neurobiology of Learning and Memory).

QCN Track

Across the board, from molecular biology to physics to psychology, Princeton's world-class faculty is particularly strong in quantitative and theoretical investigations. The same is true in Neuroscience. In recognition of this, a Quantitative and Computational Neuroscience track exists within the Neuroscience Ph.D. Students in this track must fulfill all the requirements of the Neuroscience Ph.D. In addition, their electives should be in quantitative courses, and their Ph.D. research should be in quantitative and/or computational neuroscience.  The QCN track is supported by the T32 training grant in Quantitative Neuroscience from the NIMH.

Course listings

View a list of Neuroscience-related courses.