Cognitive, Computational and Systems Neuroscience Curriculum Pathway
Recent years have seen a blurring of the traditional lines between brain-related research in psychology, biology and engineering. To train the next generation of top-flight brain scientists, Washington University has developed an integrated curriculum that provides graduate students with the training and resources to become leaders in this new interdisciplinary science.
The Cognitive, Computational and Systems Neuroscience Pathway (CCSN) is a specialized curriculum available to students pursuing a PhD in Neuroscience, Psychology or Biomedical Engineering at Washington University in St. Louis (including students in the Medical Scientist Training Program). The CCSN Pathway is not a separate degree-granting program, and CCSN students must fulfill all of the degree requirements of their home departments.
The CCSN Pathway provides an integrated curriculum that is compatible with course scheduling constraints in the three degree-granting programs. The curriculum will be challenging and is designed to help students tackle problems using an interdisciplinary approach.
The CCSN Pathway curriculum consists of three core and two advanced courses.
In the first year, each student takes three core courses: Neural Systems, Advanced Cognitive Psychology and Biological Neural Computation. This is a challenging load (in addition to program-specific requirements), but it is highly desirable to expose students to all three areas in the first year, as this provides the foundation for the second year of tailored, integrative coursework. Options are available to spread out the coursework. In consultation with the CCSN Pathway Advising Committee, each student develops a plan of coursework that best suits his or her individual needs.
The second year of the CCSN curriculum consists of two semester-long courses: Advanced CCSN, occurs during fall semester and focuses on faculty-led case studies that involve tackling fundamental issues in neuroscience using an interdisciplinary approach. A required pre-requisite to the course is participation in the week-long mini-course (aka "boot camp") that takes place at the end of summer (i.e., right before the semester begins). The mini-course provides preparation for Advanced CCSN and facilitates increased interactions and discussion among students and faculty in a more informal setting. CCSN Project Building is completed during the spring semester. In this course each student, in consultation with faculty, develops a research plan in his or her chosen area of interest. The culmination of this CCSN Project Building is an NIH-style grant proposal that, for many students, will serve as a solid precursor to a thesis proposal.
CCSN also provides a number of training opportunities beyond the core curriculum, including:
Washington University has received funding from the National Institutes of Health Training Grant to support students in CCSN. Fellowships are funded for a two-year period and available each year for U.S. citizens and residents. In addition, the McDonnell Center for Systems Neuroscience is funding a limited number of additional two-year fellowships for students who are not U.S. citizens or residents. Students apply for the NIH Training Grant/McDonnell fellowships during their first year of graduate school. Successful applicants commit to completing the CCSN program.
CCSN Summer Undergraduate Research Experience (C-SURE):
CCSN has established a summer undergraduate research program for students at Washington University. The program is described in this announcement.
Graduate students interested in the CCSN pathway can learn more about it by clicking below for course information and lists of the people involved with CCSN teaching and administration.
Undergraduates seeking to do research in CCSN-affiliated laboratories may also find the course instructor lists helpful.
Information about other faculty with interests related to CCSN can be found at the web sites for the Neurosciences Program, Department of Biology, and Department of Biomedical Engineering.