Computational Certificate

Certificate in Computational Neuroscience

 

Overview: Qualified PiN students will have the opportunity to earn a certificate in Computational Neuroscience as part of their PhD training. The goal of the Certificate in Computational Neuroscience is to provide PiN students who have an interest in computational and/or theoretical neuroscience with the opportunity to enhance their skills in the field and to engage intellectually with the computational/theoretical community at Harvard through targeted coursework, journal clubs, and interactions with computational/theoretical faculty members. Leveraging both Harvard’s wealth of cutting-edge experimental research in neuroscience and the strong computational/theoretical community spanning Harvard University, the PiN Certificate in Computational Neuroscience provides the training and resources to enable graduate students to work at the interface of experimental science and theory.

 

Certificate Requirements: The general strategy of the CiCN training program is to provide students with a solid foundation in computational approaches to solve problems in neuroscience, while maintaining sufficient flexibility to meet the needs of individual students as they embark on their dissertation work. This is achieved through:

  • 1) A clear computational focus in trainees’ dissertation work
  • 2) Formal coursework in computational neuroscience
  • 3) Professional development opportunities that include an annual symposium, a series of workshops (CiCN orientation, rigor and reproducible computing, and science communication), and other programming within the computational community.

 

  • Computational Focus in Trainees’ Dissertation Work

    Students do not need to join a computational lab to be considered for the CiCN; however, CiCN candidates are expected to incorporate computational and/or theoretical work into their dissertation research. Accordingly, one member of both the student’s Dissertation Advisory Committee (DAC) and thesis examination committee must be a faculty member who specializes in computation or theory.

     

    Potential computational/theoretical specialists include (but are not limited to): Jan Drugowitsch, Sam Gershman, Nao Uchida, Haim Sompolinsky, Cengiz Pehlevan, Demba Ba, Gabriel Kreiman, L. Mahadevan, Maurice A. Smith, Bence P. Ölveczky, Aravi Samuel, Florian Engert, Venki Murthy, Mark Andermann, Chris Harvey, Rick Born, Rachel Wilson, Marge Livingstone, John Assad, Leah Somerville, Talia Konkle, Liz Phelps, Ben de Bivort, Bob Datta, and Finale Doshi-Velez.

     

    Formal Coursework

    To earn the Certificate, students are required to complete some computational coursework beyond the core PiN curriculum. This includes a foundational course in computational neuroscience (either MCB131: Computational Neuroscience or NEURO1401/PSY1401: Computational Cognitive Neuroscience: Building Models of the Brain) and four quarters’ equivalence (equals two full semester courses) of advanced computational electives. Of note, this computational coursework completely satisfies the electives requirement for PiN students. Additional information about CiCN coursework and a list of potential electives can be found here.

     

    Professional Development

    As part of the CiCN, students will present at an annual computational symposium and will participate in a series of three workshops spaced throughout the year: CiCN orientation, rigor and reproducible computing, and scientific communication (offered in conjunction with the annual symposium). CiCN trainees will also meet with the CiCN Directors (Dr. Jan Drugowitsch and Dr. Sam Gershman) for an annual mentoring session to discuss their individual progress and training goals.

     

    CiCN trainees are expected to be active participants in the computational community, attending journal clubs, symposia, and other programming events as offered. Activities of particular interest to CiCN students (beyond CiCN-specific programming) are given below.

    • The Harvard Brain Initiative (HBI) hosts topic-specific affinity groups, symposia, and social mixers. In Fall of 2018 HBI hosted the “Bridging Theory & Data in Neuroscience” symposium. Beginning Summer of 2019 HBI will be launching a new affinity group that will continue the theme of bridging computational and experimental neuroscience.
    • PiN’s “Nocturnal Journal Club” is a student-run journal club in Longwood that features data talks by senior PiN students paired with related paper presentations presented by junior PiN students.
    • The Harvard Center for Brain Science (CBS) “Neurolunch” seminar series in Cambridge features talks by graduate students and postdocs.
    • The PiN Computational Methods Club in Longwood provides a place for students and postdocs to discuss and to learn about methods in computational/theoretical neuroscience and machine learning.
    • The weekly MIT/Harvard Computational & Theoretical Neuroscience Journal Club alternates between Harvard and MIT and features discussions of scientific articles presented by graduate students, postdocs, and faculty (https://compneurojc.github.io/).
    • The Computational Neuroscience Journal Club in Longwood features short “chalk talks” and discussions of scientific articles by graduate students, postdoc, and faculty presenters (https://sites.google.com/view/compneurojc/).
    • The Harvard School of Engineering and Applied Sciences (SEAS) hosts external speakers in their Theory of Computation seminar series (https://www.seas.harvard.edu/events?field_calendar_tid=283).
    • The Models, Inference, & Algorithms (MIA) weekly meeting at the Broad Institute emphasizes learning and collaboration by featuring an educational primer, seminar, and discussion at each meeting. Videos of past presentations are also available online (https://www.broadinstitute.org/scientific-community/science/mia/models-inference-algorithms).
    • The Biostatistics-Biomedical Informatics Big Data seminar series at the Harvard School of Public Health features research talks on statistical, computational, and machine learning methods from local and external speakers (https://www.hsph.harvard.edu/biostatistics/b3d-seminar/).

 

Applications: Students will typically apply for admission to the Certificate program at the end of the second (G2) year, after joining a dissertation lab and passing their Preliminary Qualifying Exam (PQE). However, it may be possible for students to gain admittance to the Certificate program at a later stage of graduate school, pending changes in the direction of their research. Students are expected to possess basic mathematical and programming competence to be considered for the Certificate, though there are no specific prerequisites. To apply for the program, students will be asked to briefly describe their prior preparation (including coursework and research experiences) and to describe how the additional training in computational/theoretical neuroscience will be applied to their dissertation research.


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