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Graduate Education

Interdisciplinary Training in Computational Neuroscience (ITCN)

The Computational Neuroscience Initiative at the University of Pennsylvania offers a new program for Interdisciplinary Training in Computational Neuroscience (ITCN) at the doctoral level. The goal of the ITCN is to train a new generation of scientists who will make quantitative links between properties of the brain and properties of the mind. ITCN trainees will learn to use computational methodology to interpret experimental results, build predictive models of brain function that inform the design of the experiments, and support an ever-growing list of applications from brain-machine interfaces in the clinic to cutting-edge forms of artificial intelligence.

The ITCN brings together students and faculty from Penn’s PhD programs in Neuroscience (Biomedical Graduate Studies, Perelman School of Medicine), Bioengineering (School of Engineering and Applied Science), Physics (School of Arts and Sciences), and Psychology (School of Arts and Sciences). To join the ITCN, students first apply to one of those "home" PhD programs. Once admitted, students then typically apply to the ITCN before starting classes in their first year. Those selected meet with the ITCN Advisory Committee, both individually and as a group, to develop individualized training plans that meet the goals and requirements of the ITCN and each student’s home program. These plans include coursework, research, and other activities that aim to build both expertise in computational neuroscience and a sense of community in the ITCN. Details are provided below.

The ITCN embraces diversity and inclusion in their many forms, including welcoming students with a broad range of personal, professional, and academic backgrounds into both our training program and the field of computational neuroscience as a whole. 

Application. Prospective PhD students must first apply and be accepted into one of our participating graduate programs: NeuroscienceBioengineeringPhysics, or Psychology (PhD students admitted to other PhD programs at Penn may also be considered; see below for additional requirements). Students typically apply to the ITCN before starting classes in their first PhD year, although we will also consider applications from students in more advanced years who believe they will be able to meet our academic requirements. Selection into the ITCN does not require a background in computational neuroscience. Instead, we require: 1) a brief (~½ page) essay describing why you want to join the ITCN and how it would fit into your training and career plans; 2) written permission from your Graduate Chair (and, for MD-PhD or VMD-PhD students, the Director of your Combined Degree Program); and 3) if you are not in one of our four participating programs, descriptions of the funding source(s) and course and research requirements for your home PhD program. Please send your application materials, and/or any questions, to Vijay Balasubramanian, Maria Geffen, and/or Joshua Gold.

Funding. ITCN trainees are currently expected to be funded via their home PhD programs. This funding may (but is not required to) include support from one of several training grants at Penn with close associations with our training program, including (please contact the PI(s) for more details about each training grant): 1) for NGG students, the Neuroscience Training Grant (PIs: Joshua Gold and Minghong Ma); 2) for students training in vision science, the Vision Training Grant (PIs: Diego Contreras and Jessica Morgan); and 3) for students training in auditory neuroscience, the CANAC training programComputational Approaches to the Neuroscience of Audition and Communication (CANAC; PIs: Yale Cohen, Vijay Balasubramanian, and Maria Geffen).

Advising. The ITCN Advisory Committee consists of Vijay Balasubramanian, Maria Geffen, and Joshua Gold. Each ITCN trainee meets with the full Advisory Committee at least two times per year (typically once at the beginning of each of the fall and spring semesters) to establish academic and career plans and monitor progress. Each trainee is also paired with one of the Advisory Committee members to hold individual meetings more regularly, as needed. A major goal of ITCN advising is to ensure that each trainee is able to balance the training components of the ITCN with the training components of their home program.

Courses. ITCN trainees are expected to take all of the required courses from their home program, and then use their electives to fulfill ITCN course requirements. These requirements are:

  • PHYS 585: Theoretical and Computational Neuroscience
  • Two or three electives chosen (with the help of the Advisory Committee) to provide appropriate foundations for each trainee, selected from the many courses offered at Penn. Possible course include:
  • BE 5210/521: Brain-Computer Interfaces
  • BE 5660/566: Network Neuroscience
  • CIS 5200/520: Machine Learning
  • ESE 3130/313: Robotics and Bio-inspired Systems
  • ESE 4060/406: Control of Systems
  • ESE 4080/408: Communication Systems
  • ESE 5390/539: Hardware/Software Co-Design for Machine Learning
  • ESE 5730/573: Building Brains in Silicon
  • ESE 6740/674: Information Theory
  • MATH 2400/MATH 240: Differential Equations and Linear Algebra
  • MATH 2410/MATH 241: Fourier Analysis and Complex Analysis
  • NGG 573: Systems Neuroscience
  • PHYS 662 Statistical Physics
  • PHYS 2280/PHYS 280: Physical models of Biological Systems
  • PSYC 5110: Probabilistic Models of Perception
  • PSYC 3790/BIBB 473: Neuroeconomics
  • PSYC 719 Experimental Methods in Perception
  • PSYC 739 Probabilistic Models in Perception and Cognition

Research. ITCN trainees are expected to fulfill all laboratory rotation requirements from their home program, which may include a combination of labs that are and are not affiliated with the ITCN. We expect trainees to ultimately join a dissertation laboratory affiliated with the ITCN (i.e., CNI laboratories) but will provide exceptions or add the laboratory to the ITCN as needed.

Other Activities. ITCN trainees are expected to participate in the weekly trainee-led CNI+/- Journal Club and attend weekly CNI seminars (listed here). There will also be opportunities to not just participate in but also organize other CNI-sponsored workshops and activities determined on an annual basis.


Undergraduate Education

Penn offers a minor in computational neuroscience. You can find more information here:

Many of the researchers at CNI accept undergraduate students for work study, part-time research, or independent-study opportunities. Undergraduate students interested in working in specific CNI laboratories should reach out to the PIs directly to inquire about open opportunities.