Computational Neuroscience Curriculum

PhD in Computational Neuroscience

What we expect from students beginning a PhD in computational neuroscience 

We expect all students joining the computational neuroscience (CompNeuro) pathway will have a strong quantitative background. This can be established with an undergraduate degree in a quantitative field (e.g., Computational Neuroscience, Mathematics, Statistics, Engineering, Computer Science, Physics, or similar), but other evidence of experiential quantitative training will be considered on a case by case basis.

At a minimum, we expect students to have taken the following courses (or other evidence of training):

  • Calculus (all neuroscience students)
  • Differential Equations
  • Linear Algebra
  • Introductory course in Computer Science and/or Programming

Exceptions can be made on a case by case basis for students with a strong quantitative background who are missing one or more of these specific requirements.

What we expect from students completing a PhD in computational neuroscience

On completion of a PhD in computational neuroscience, students will have a broad knowledge of theoretical/mathematical neuroscience, statistics, and data science (core topics listed below). Graduates will be proficient in research programming, with experience in data management and experience in implementing best practices for reproducible computational research. Finally, graduates will have deep knowledge and expertise related to their chosen research topic (including additional graduate-level coursework, as necessary).

Students will be well-prepared for both academic and non-academic careers.

Required Coursework

Students specializing in computational neuroscience must complete the required GPN core neuroscience coursework listed below, plus the additional core courses in BU CompNeuro:

GPN Core Courses (18 cr): 

  • Frontiers in Neuroscience (NE500/NE501) (4 cr)
  • Principles of Neuroscience I: From Molecules to Systems (GMS NE 700) (4 cr)
  • Neural Systems I: Functional Circuit Analysis (GRS NE 741) (4 cr)
  • Neural Systems II: Cognition and Behavior (GRS NE 742) (4 cr)
  • Introduction to Modeling and Data Analysis in Neuroscience (GRS MA 665) (2 cr)

 Computational Neuroscience Core Courses (10 credits): 

  • Advanced Modeling and Data Analysis in Neuroscience (GRS MA 666) (2 cr)
  • Accelerated Introduction to Statistical Methods for Quantitative Research  (GRS MA 681) (4 cr) 
  • Time Series Analysis for Neuroscience Research (GRS MA 765) (4 cr)

 Computational Neuroscience Electives (5 courses):

One upper level / graduate computational neuroscience course, approved by the student’s primary research mentor and the Computational Neuroscience Curriculum Committee. Courses chosen for this elective should explicitly include elements of both computation and neuroscience.

Four upper level / graduate courses determined by the student’s research interests, and approved by the student’s primary research mentor and the Computational Neuroscience Curriculum Committee. 

Note: 

  • CAS MA 510 may be substituted for GRS MA 665/666 when the latter is not offered.