Introduction and Rationale

Neuroscience is a highly interdisciplinary scientific domain that depends critically on close contact with other fields of inquiry. This is due to the fact that methods for studying the brain often rely on techniques imported from other disciplines, and conversely, insights about the brain are often useful for framing new lines of inquiry in other disciplines.  As a consequence of this interdisciplinarity, there is a large and growing demand for neuroscience coursework and research experience from across the sciences, engineering, and the humanities. Additionally, there is growing recognition that neuroscientific expertise is a useful asset in a wide variety of professional settings, ranging from law and public health to biomedical engineering and artificial intelligence. The graduate certificate in neuroscience is designed to formalize training of graduate students in neuroscience, and to recognize the achievements of students who have undertaken comprehensive training in these topics, both through formal coursework and through research in their respective subject areas. The graduate certificate welcomes participation from across the University in any field that makes contact with neuroscience as part of scholarship. The requirements include two graduate courses (one core course and one approved elective course), attendance at the neuroscience research seminar series, and one chapter of neuroscience research incorporated into the dissertation. Upon completion of the program, the certificate is recorded on the student’s transcript, and the student is also presented with a physical certificate issued by the Princeton Neuroscience Institute Director(s). Actively enrolled Ph.D. students who are not enrolled in the Neuroscience Ph.D. program or Joint Degree Program are eligible to apply.

Administration and Enrollment

The certificate is administered by the Princeton Neuroscience Institute (PNI) under the direction of the Director of Graduate Studies (DGS). The Graduate Program Administrator (GPA) provides administrative support. The DGS and GPA are responsible for advertising the program and ensuring all enrolled students understand the requirements. The certificate program is open to any Princeton University student enrolled in a Ph.D. program who are not enrolled in the Neuroscience Ph.D. program or the Neuroscience Joint Degree Program. Students enroll by completing an online application on the PNI website. The online application requests information including department, research area, advisor, and the (intended) elective course that will be used to fulfill the requirement. The online application also collects information related to the timeline for completion of degree requirements. Students are encouraged to apply to the certificate program as soon as possible to ensure that the requirements are met.

Certificate Requirements

To earn the certificate, students must complete four requirements: (1) take for credit and earn a grade of B or better in one core course; (2) take for credit and earn a grade of B or better in one approved elective course; (3) enroll in the graduate seminar course and attend the seminar journal club; and (4) incorporate one chapter of neuroscience research in the dissertation, as judged by a PNI faculty member who is either a dissertation advisor, thesis committee member, and/or dissertation defense committee member, and who must certify this requirement by writing an email to the Neuroscience DGS.

Core Courses

Students must take one core course listed below. This requirement is designed to ensure that all students who earn the certificate have a solid foundation in the basic principles of neuroscience. A grade of B or better is required in both core courses.

NEU 501A: Cellular and Circuits Neuroscience

A survey of modern neuroscience in lecture format, focusing on brain function from cells and the molecules they express to the function of circuits. The course emphasizes theoretical and computational/quantitative approaches. Topics include cellular neurophysiology, neuroanatomy, neural circuits and dynamics, cell fate decisions, neural development and plasticity, sensory systems, and molecular neuroscience. Students read and discuss primary literature throughout the course.

NEU 501B: Neuroscience: From Molecules to Systems to Behavior

This laboratory course complements NEU 501A and introduces students to the variety of techniques and concepts used in modern neuroscience, from the point of view of experimental and computational/quantitative approaches. Topics include synaptic transmission and plasticity, two-photon imaging, central neuron activity patterns, optogenetic methods to control neural activity and student-designed special projects. In-lab lectures give students the background necessary to understand the scientific content of the labs but the emphasis is on the laboratory work.

NEU 502A: Systems and Cognitive Neuroscience

A survey of modern neuroscience that covers experimental and theoretical approaches to understanding how the brain works. This semester builds on 501, focusing on how the circuits and systems of the brain give rise to cognition. The course covers the neural mechanisms responsible for vision, long-term memory, sleep, motor control, habits, decision making, attention, working memory, and cognitive control. How these functions are disrupted in neurodegenerative and neuropsychiatric disorders are also covered.

NEU 502B: From Molecules to Systems to Behavior

This lab course introduces students to the variety of experimental and computational techniques and concepts used in modern cognitive neuroscience. Topics include functional magnetic resonance imaging, scalp electrophysiological recording, and computational modeling. In-lab lectures provide students with the background necessary to understand the scientific content of the labs, but the emphasis is on the labs themselves, including student-designed experiments using these techniques.

Elective Course

Students are also required to take one neuroscience related elective course. This requirement is designed to give students additional training in the neuroscience field. Elective courses can be selected from any graduate level course on campus as long as the course contains a neuroscience related component. Each elective course must be approved by the Director of Graduate Students. A grade of B or better is required in the elective course.

Note on Overlapping Course Requirements in Home Department

These courses may count towards fulfillment of the student’s home department requirements.

Research Seminar Course and Journal Club

To learn about the current research in neuroscience and interact with researchers across disciplines, students are required to enroll in the neuroscience graduate seminar course, NEU 511 for two semesters and participate in the seminar journal club. The journal club meets the week of an external speaker seminar. The discussion is led by a PNI postdoc and students are expected to attend the journal club meetings and participate by reading the journal paper assigned ahead of time in preparation for discussion.


The final requirement for the certificate is that the student’s dissertation research must include one chapter of neuroscience research. The student’s thesis committee and dissertation defense committee must include at least one core faculty member of the PNI and email the Neuroscience DGS certifying the inclusion of neuroscience research in the dissertation. In all cases, the DGS will review the email and confirm that this requirement has been met. 

List of Approved Courses
Additional elective courses may be approved by the Neuroscience Director of Graduate Studies.

Core Courses

NEU 501A: Cellular and Circuits Neuroscience
NEU 501B: From Molecules to Systems to Behavior
NEU 502A: Systems and Behavior Neuroscience
NEU 502B: From Molecules to Systems to Behavior

Seminar Course
NEU 511: Current Issues in Neuroscience and Behavior

Elective Courses

NEU 537: Computational Neuroscience
NEU 545: Statistics for Neuroscience
NEU 560: Advanced Statistical Methods for Neural Data COS 511: Theoretical Machine Learning
COS 513: Foundations of Probabilistic Modeling
ORF 525: Statistica Learning and Nonparametric Estimation ELE 535: Machine Learning and Pattern Recognition
ELE 571: Digital Neurocomputing
ELE 521: Linear Systems Theory
ELE 523: Nonlinear Systems Theory
MAE 541: Applied Dynamical Systems
MAE 542: Advanced Dynamics
APC 529: Coding Theory and Random Graphs
MAE 546: Optimal Control and Estimation
COS 521: Advanced Algorithm Design
COS 551: Introduction to Computational Molecular Biology COS 598B: Natural Algorithms
APC 524: Software Engineering for Scientific Computing MOL 515: Methods and Logic in Quantitative Biology