A typical student will take 2-3 courses per term in their first year and complete all coursework by the end of their third year in the program. Because of differences in background and educational goals, course requirements for each student in the program will be adapted to their individual needs.

Year One

Students' first year coursework will be decided by the student in consultation with the faculty steering committee. Typically, students will take 2-3 courses each term of their first year, including at least one computational neuroscience course and two courses covering experimental neuroscience.

Year Two

By two weeks before start of Fall term of a student’s second year, the student must submit a proposed schedule of coursework, along with a statement from his or her advisor recommending approval. This plan will then be considered by the steering committee which may approve the course plan, or ask for modifications. Approval will be based on meeting program expectations in the following three areas.

Computational Neuroscience

Students must take at least three courses in computational neuroscience including mathematical statistical and computational approaches.

Recommended courses fulfilling this requirement include

MATH 3375 Computational Neuroscience ** (PITT)
MATH 3370 Mathematical Neuroscience (PITT)
15-785 Computational Perception and Scene Analysis (CMU)
15-874 Computational Neuroscience of Natural Intelligence (CMU)
15-883 Computational Models of Neural Systems** (CMU)
36-759 Statistical Models of the Brain (CMU)
85-719 Introduction to Parallel Distributed Processing (CMU)
** All students will be required to take one of these two courses as a core course in computational neuroscience.

Experimental Neuroscience

Students must gain graduate level training in the following areas:

i.) cell and molecular neuroscience/neurophysiology
ii.) systems neuroscience
iii.) cognitive neuroscience

Recommended courses fulfilling these requirements include

i.) 03-762 Advanced Cellular Neuroscience (CMU) or NROSCI 2100/2101 Cellular and Molecular Neurobiology (Pitt)
ii.) 03-763 Systems Neuroscience (CMU) or NROSCI 2102 Systems Neuroscience (Pitt)
iii.) Psych 85-765/NROSCI 2005 Cognitive Neuroscience

Quantitative Methods

Students must take at least two graduate level courses in one quantitative subject (e.g. math, computer science or statistics) to ensure depth of knowledge in this area. Under the quantitative methods requirement, we have identified three examples of focus areas:

Dynamical systems focus
Stats 2731 Stochastic Processes (PITT)
MATH 2950 Applied math methods (PITT)
MATH 2921 Dynamical Systems (PITT)

These courses might require the courses below or equivalent previous courses as prerequisites:
MATH 2920 Differential Equations (PITT)
MATH 2370 Linear Algebra (PITT)

Statistics and machine learning focus
36-705 Intermediate Statistics (CMU)
10-702 Statistical Foundations of Machine Learning (CMU)
36-7xx Time Series and Point Processes (course in development) (CMU)

These courses might require the courses below or equivalent previous courses as prerequisites
36-746 Statistical Methods for Neuroscience (CMU)
36-625/626 Probability and Mathematical Statistics (CMU)
10-701 Machine Learning (CMU)

Computation focus
10-701 Machine Learning or advanced AI course (CMU)
15-685 Computer Vision (CMU)
15-451 Algorithms (CMU)

Other foci, including “brain imaging and signal processing” have been discussed and may be added as recommended course sets.

Click here for further information on current CNBC course offerings.

Collaboration with Experimentalists

One critical aspect of training students to be computational neuroscientists is to give them a detailed understanding of how the experimental data they are analyzing or modeling are collected. This will allow students to appreciate the limitations of the experimental data (such as sources of variability), appreciate what kinds of experiments can and cannot be done and aid in their ability to interact with experimentalists.

All students in the PNC will be encouraged to do experimental work or to collaborate closely with experimentalists. We anticipate that students working in different areas will have different needs in terms of the extent of their involvement collecting experimental data. Some students will be in laboratories in which both experimental and computational work are being performed. Such students may gain experience in both approaches throughout their training. Others will typically do a 10 week rotation in an experimental lab. A proposal detailing this cross-training experience must be submitted for approval to the PNC steering committee by the beginning of the second year.

Program Milestones

First Year Research Requirement

By the end of the first calendar year in the program, all students will be required to complete a computational project. This project will be evaluated by a committee consisting of at least three faculty, of whom at least two are PNC training faculty. The purpose of the project is to have the student identify a biological problem, understand the data collection process, articulate the goals of building a model or performing a particular kind of analysis and implement this computational approach. In some cases this project may be a precursor to the student’s eventual thesis project.

Second Year Research Requirement

By the end of the second full year in the program all students will be required to complete a deeper computational project. This project will be evaluated by a committee consisting of at least three faculty of whom at least two are PNC training faculty. The student’s work on the project should demonstrate that the student has 1) the ability to analyze and interpret experimental data in a particular area 2) the ability to develop and implement a computational approach incorporating the relevant level of biological detail and 3) the ability to organize, interpret and present the results of the computational work. This project should be a body of work suitable for publication. It is expected that this work will be written up as a paper to be submitted to a journal in the relevant field. In the second year, students are expected to work on research about 1/3 of their time during the academic year and full time during the summer. In most cases this project will be on an area related to the student’s eventual thesis project

Ph.D. Thesis Proposal

Required coursework should be completed by the end of the third year. By the start of the fourth year a Ph.D. candidate will present a thesis prospectus first to his or her thesis committee and then to the CNBC community. The prospectus should include:

• a clear statement of the proposed research problem
• the significance of the proposed research
• a review of relevant literature relating to the problem
• a review of the candidate’s work leading up to the thesis
• a tentative schedule for completing the work

Advising on scheduling the prospectus, and guiding in the formation of the dissertation committee, is the thesis advisor’s responsibility. The thesis committee should be composed of at least four members, one of whom is an external member and of whom at least two are PNC training faculty. The external member is typically from outside the two participating Universities. All thesis committees are subject to approval by the PNC steering committee.

Ph.D. Thesis Defense

Normally, the dissertation is completed during the student’s fifth year. The final defense is a public presentation, in accord with the College and University requirements for the Ph.D. It is the candidate’s responsibility to ensure that the College and University’s guidelines are followed for publicity of the defense, and the availability of the thesis document at least one week prior to the defense.