First-hand experience is an essential part of gaining real understanding

Ph.D. Neuroscience students take lecture and laboratory courses; learn to read, understand, and present current scientific literature; develop and carry out substantial original research, and present their research at meetings and conferences, including the annual Neuroscience retreat each Spring.

During the first year, all students participate in a unique year-long Core Course that surveys current neuroscience.

The subjects covered in lectures are accompanied by direct experience in the lab. Students learn through first-hand experience how to run their own fMRI experiments; to design and run their own computer simulations of neural networks; to image neural activity at cellular resolution in behaving animals; and to patch-clamp single cells, to name a few examples. This core course offers students a unique opportunity to learn the practical knowledge essential for successfully developing new experiments and techniques.

Incoming students are encouraged to rotate through up to three different labs to choose the lab that best matches their interests. During this process, students may discover an area of research completely new and fascinating to them. Following their rotations and by mutual agreement with their prospective faculty adviser, students choose a lab in which they will carry out their Ph.D. research.

Ph.D. Timeline Overview

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.