Computational Neuroscience Curriculum

PhD in Neuroscience & Computational Neuroscience

Post-bachelors students must complete 64 credits and post-masters students must complete 32 credits. These credits must include the following, with additional credits from directed research or additional graduate-level coursework approved by the student’s advisor.

Current Students: Please click the links below to access the Requirement Spreadsheets.

• Post-Bachelors Requirement Spreadsheet
• Post-Masters Requirement Spreadsheet

Required Courses

GRS BI755 Eldred and Rosene Principles of neuroscience I: From molecules to systems (4 cr)

GRS BI756 Bergethon and Moss Principles of neuroscience II: From systems to mind (4 cr)

GRS MA681 Eden Accelerated introduction to statistical methods for quantitative research (4 cr)

Students may petition to place out of up to two of these three required courses if they took substantially equivalent courses prior to entering the PhD program and received a grade of B (or the equivalent) or better. Students can then substitute additional courses (selected in consultation with and approved by the GPN Graduate Education Committee (GEC)) or directed study to replace the credits for any required courses that are waived.

GRS NE800 Lab rotations (2 – 4 cr). A minimum of two rotations (each 1 credit) is required. At least one rotation should be in an area of experimental research. Students are expected to spend 10 hours per week on their lab work for each semester-long rotation.

GRS NE500/501 Frontiers in neuroscience (2 cr). Core seminar course for all GPN students.

Basic Computational Neuroscience Courses

At least four credits selected from:

CAS CN510 Gorchetchnikov Principles and methods of cognitive and neural modeling I (4 cr)

CAS CN580 Schwartz Introduction to computational neuroscience (4 cr)

GRS MA665 Kramer Introduction to modeling and data analysis in neuroscience (2 cr)

GRS MA666 Kramer Advanced modeling and data analysis in neuroscience (2 cr)

Quantitative Systems Neuroscience Courses

At least four credits selected from:

CAS PS530 Hasselmo Neural models of memory function (4 cr)

CAS CN530 Mingolla Neural and computational models of vision (4 cr)

CAS CN540 Bullock Neural and computational models of adaptive movement planning and control (4 cr)

CAS CN560 Shinn-Cunningham Neural and computational models of hearing (4 cr)

CAS CN570 TBD Neural models of conditioning, reinforcement, motivation, and rhythm (4 cr)

CAS CN720 Bullock Neural and computational models of planning and temporal structure in behavior  (4 cr)

Advanced Computational Neuroscience Courses

At least four credits of advanced graduate coursework (700 level or above) selected from the list below, or approved by the GPN GEC:

ENG BE707 Ritt/Sen Quantitative studies of excitable cells (4 cr)

CAS CN720 Bullock Neural and computational models of planning and temporal structure in behavior  (4 cr)

CAS CN730 Mingolla Models of visual perception (4 cr)

CAS CN740 Guenther Topics in sensory motor control (4 cr)

GRS MA751 Kolaczyk/Kon Advanced statistical methods II (4 cr)

GRS CN780 Schwartz Topics in computational neuroscience (4 cr)

GRS CN810 Versace Topics in cognitive and neural systems: Adaptive mobile robots (4 cr)

GMS AN820 Bergethon Introduction to Interdisciplinary systems science: Dynamic modeling (2 cr)

Additional Courses

For post-masters students, 4 additional credits in neuroscience or related topics (neuroscience electives), selected in consultation with and approved by the GPN GEC.

For post-bachelors students, 16 additional credits in neuroscience or related topics (neuroscience electives), selected in consultation with and approved by the GPN GEC. At least eight of these additional credits must come from courses listed above and/or from the set of approved computational neuroscience electives, listed below. Students may petition to add other graduate-level computational courses at Boston University to the list of approved electives. Other coursework can be found on the general link to GPN Electives.  Note that courses counted towards the distribution requirements (above) cannot also count towards fulfilling the computational neuroscience elective requirement.

Approved Computational Neuroscience Electives

CAS MA976 Kopell Dynamical systems in neuroscience (4 cr)

CAS BI502 Gardner Computational perspectives on the control of behavior (4 cr)

ENG EC505 Karl Stochastic processes (4 cr)

ENG EC516 Nawab Digital signal processing (4 cr)

CAS CN520 TBD Principles and methods of cognitive and neural modeling II (4 cr)

CAS CN550 Gorchetchnikov/Ames Neural and computational models of recognition, memory, and attention (4 cr)

ENG BE567 Ritt Nonlinear systems in biomedical engineering (4 cr)

CAS MA568 Eden Statistical analysis of point process data (4 cr)

CAS MA583 Eden Introduction to stochastic processes (4 cr)

GMS IM651 Killiany Statistical analysis of neuroimaging data (2 cr)

ENG EC710 Caramanis Dynamic programming and stochastic control (4 cr)

GRS CN 710 Carpenter Advanced topics in neural modeling (4 cr)

ENG EC717 TBD Image reconstruction and restoration (4 cr)

ENG EC719 Saligrama Statistical pattern recognition (4 cr)

ENG BE 747 Colburn Advanced signals and systems analysis for biomedical engineering (4 cr)

GRS PY771 Rothschild Biophysics (4 cr)

GMS BY772 TBD Nuclear magnetic resonance spectroscopy in biology and biochemistry (2 cr)

GRS MA783 Guasoni Advanced stochastic processes (4 cr)

GRS CN811 Mingolla Topics in cognitive and neural systems: Visual perception (4 cr)