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Graduate study in QCN

The Quantitative Neuroscience Training Program (QNTP) within the PNI Ph.D. Program

  ABOUT THE QNTP
 
The PNI Ph.D. program is designed to provide all of our students with a strong foundation in quantitative approaches to neuroscience. The QNTP then builds on this foundation by giving our most quantitatively-focused predoctoral trainees the additional tools and training they need to function fully as computational neuroscientists.
 
The QNTP is supported by a T32 training grant from the NIMH that was just renewed in 2013. All PNI graduate students who receive support from this T32 training grant are members of the QNTP and must complete the QNTP requirements listed below. In addition, PNI graduate students who are not receiving support from the training grant can opt to join the QNTP. All students who successfully complete the QNTP requirements listed below will receive a credential in “Quantitative and Computational Neuroscience” from the PNI upon receiving their Ph.D.


QNTP REQUIREMENTS

 
All QNTP trainees are required to complete the requirements of the PNI Ph.D. program (e.g., 501 and 502, attendance at the PNI retreat and neuro seminar). In addition to these core requirements, QNTP trainees also need to complete the following requirements:
 
Coursework. QNTP trainees are required to take two electives in total (in contrast to non-QNTP trainees, who only need to take one elective). These electives provide in-depth coverage of mathematical and computational methods for formal theory development and data analysis in neuroscience. For QNTP trainees, the two electives must be taken from a set of six Computational Neuroscience electives or from a broader set of elective courses that provide advanced training in relevant quantitative methods (see List of Electives at the end of this document). Note: other courses may satisfy the coursework requirement so long as they have sufficient quantitative/computational content and they are pre-approved by the PNI Curriculum Committee.
 

Quantitative and Computational Neuroscience (QCN) Journal Club. To keep QNTP trainees informed about relevant developments in the field, all predoctoral members of the QNTP are required to attend a biweekly QCN journal club and present once a year. The journal club organizers (trainees in the program) will choose a broad theme for each meeting, always with a quantitative focus, and then solicit volunteers to present a background and a focus paper on the subject. The journal club focuses on recent articles in the literature, but occasional informal presentations of recent findings from the trainee’s laboratory will also be encouraged. If possible, when a QCN-relevant outside speaker is scheduled for another seminar series (e.g., the Neuroscience Seminar), the journal club will read a paper by that speaker before their visit. Note that the journal club is open to all Princeton graduate students doing relevant research (not just PNI graduate students in the QNTP).

Thesis Committee : For students in the training program, at least one committee member should be actively conducting quantitative/computational research.

 Quantitatively-Focused Research Seminars:Students in the training program must attend at least one quantitatively-focused research seminar on a regular basis. These seminars include: PDP Meeting, the Neuroimaging Analysis Methods Meeting, the Quantitative and Computational Biology Seminar, the Biophysics Seminar, the Physical Biology Journal Club, and the Nonlinear Dynamical Systems Seminar. Students can petition the QNTP Executive Committee to have other research seminars count for this requirement if necessary.
 
QNTP Retreat: Each year, we will have a QNTP-specific retreat (note that this is separate from the PNI-wide retreat) where a subset of QNTP trainee can present their research in full quantitative detail. This retreat will be attended by all QNTP trainees along with computationally-focused PNI faculty.
 

Research Presentations: Students in the QNTP will be required to present their ongoing thesis research least once at each of the following venues: (1) the QNTP-specific retreat; (2) the annual PNI retreat or the PNI in-house seminar; and (3) a quantitatively-oriented conference. Also, one of these presentations must discuss the relevance of the student's work to clinical issues.

Clinical Neuroscience Evening Seminar: This seminar will host speakers whose primary area of research is translational and/or clinical. To encourage participation and interaction between the trainees and speaker, it will take place over a pizza dinner, and only graduate students will be allowed to attend. Note that this seminar is open to all PNI graduate students, not just students in the QNTP.
 

FAQ

 
What does it mean to receive a “credential in Quantitative and Computational Neuroscience from the PNI”?
 
If you join the QNTP and complete the requirements, you will receive a certificate from the PNI attesting that you received additional training in Quantitative and Computational Neuroscience. You can list this on your CV. Note that the actual Ph.D. certificate issued by the graduate school will just say “Neuroscience”.
 
If I am a PNI graduate student who is not participating in the QNTP, can I take advantage of the training opportunities listed above (e.g., the QCN Journal Club)?
 
Yes, absolutely! All of the training opportunities listed above are open to all PNI graduate students.
 

LIST OF ELECTIVES (CURRENT AS OF SUMMER 2013)

  Computational neuroscience courses:
 
MOL 437/537 Computational Neuroscience (Brody)
PSY 330 Introduction to Connectionist Models (Norman)
PSY 338 Animal Learning and Decision Making (Niv)
APC/MAT 351 Mathematical Neuroscience (Holmes)
NEU 508 Computation and Coding in Microcircuits: The Retina and Beyond (Berry)
PHY 562 Biophysics (Bialek)
ELE 480 fMRI Decoding: Reading Minds Using Brain Scans (Norman, Ramadge)
 

Additional computational courses:

 
ELE 521 Linear Systems Theory (Ramadge, Electrical Engineering)
ELE 488 Image Processing (Ramadge, Electrical Engineering)
MAE 434 Modern Control (Leonard, Mechanical and Aerospace Engineering)
ELE 523 Nonlinear Systems Theory (Leonard, Mechanical and Aerospace Engineering)
MAE 542 Advanced Dynamics (Leonard, Mechanical and Aerospace Engineering)
COS 402 Artificial Intelligence (Schapire, Computer Science)
COS 424 Interacting with Data (Blei, Computer Science)
COS 513 Foundations of Probabilistic Modeling (Blei, Computer Science)
COS 597C Advanced Probabilistic Modeling (Blei, Computer Science)
COS 598A Boosting: Foundations and Algorithms (Schapire, Computer Science)
APC 520 Mathematical Analysis of Massive Data Sets (Singer, Math)
ELE 525 Random Processes and Information Systems (Kobayashi, Electrical Engineering)
ELE 530 Theory of Detection and Estimation (Cuff, Electrical Engineering)
ORF 526 Stochastic Modeling (Fan, Operations Research)
MOL 515 Methods and Logic in Quantitative Biology (Botstein/Wingreen, Molecular Biology)
EEB 355 Introduction to Statistics for Biology (Andolfatto/Storey, Molecular Biology)
MAE 546 Optimal Control and Estimation (Stengel, Mechanical and Aerospace Engineering)
ORF 523 Advanced Optimization (Cook, Operations Research)