Computational Neuroscience Curriculum

PhD in Neuroscience & Computational Neuroscience

Post-bachelors students must complete at least 64 credits and post-masters students must complete at least 32 credits. These credits must include the following coursework, 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 and Computational Neuroscience Course Schedules.  Please see the BU Course Search for the most up-to-date course information and schedules.

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)
OR
CAS MA684 Heeren Applied multiple regression and multivariable methods (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 NE500/501 Frontiers in neuroscience (2 cr). Core seminar course for all GPN students.

GRS NE800/801 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.

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:

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

CAS CN730 Yazdanbakhsh Models of visual perception (4 cr)

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

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

ENG BE710 Vaina Neural plasticity and computational learning (4 cr)

ENG BE780 Ritt Brain machine interfaces (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)

GRS MA703 Kolaczyk Statistical analysis of network data (4 cr)

GRS MA750 Gangopadhyay Advanced statistical methods I (4 cr)

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

GRS MA770 Kon Mathematical and statistical methods of bioinformatics (4 cr)

GRS MA775 Kaper Ordinary differential equations (4 cr)

GRS MA776 Wayne Partial differential equations (4 cr)

Additional Courses

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

For post-bachelors students, 8 additional credits, including 4 credits in computational neuroscience (computational neuroscience elective) from courses listed above 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 computational neuroscience electives. The remaining four credits must be in neuroscience or related topics (neuroscience elective), selected in consultation with and approved by the GPN GEC. 

Note that courses counted towards the distribution requirements (above) cannot also count towards fulfilling the computational neuroscience elective requirement.

Approved Computational Neuroscience Electives

CAS BI502 Gardner Topics in the theory of biological networks (4 cr)

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

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

CAS MA575 Kolazyk Linear models (4 cr)

CAS MA576 Ray Generalized linear models (4 cr)

CAS MA578 Gupta Bayesian statistics (4 cr)

CAS MA581 Taqqu Probability (4 c)

CAS MA582 Ginovyan Mathematical statistics (4 cr)

CAS MA583 Eden Introduction to stochastic processes (4 cr)

CAS MA976 Kopell Dynamical systems in neuroscience (4 cr)

ENG BE/EC519 Ghitza Speech signal processing (4 cr)

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

ENG BE570 Vaina Introduction to computational vision (4 cr)

ENG BE703 Lepzelter Numerical methods and modeling in biomedical engineering (4 cr)

ENG BE747 Colburn Advanced signals and systems analysis for biomedical engineering (4 cr)
ENG EC501 Baillieul Dynamic system theory (4 cr)

ENG EC505 Karl Stochastic processes (4 cr)

ENG EC516 Nawab Digital signal processing (4 cr)

ENG EC520 Konrad Digital image processing and communication (4 cr)

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

ENG EC716 Nawab Advanced digital signal processing (4 cr)

ENG EC717 TBD Image reconstruction and restoration (4 cr)

ENG EC719 Saligrama Statistical pattern recognition (4 cr)

ENG EC734 TBD Hybrid systems (4 cr)

ENG EC741 TBD Randomized network algorithms (4 cr)

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

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

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

GRS CN710 Carpenter Advanced topics in neural modeling (4 cr)

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

GRS CS640 TBD Artificial intelligence (4 cr)

GRS MA771 TBD Introduction to Dynamical Systems (4 cr)*

GRS MA781 Ginovyan Estimation theory (4 cr)

GRS MA783 Guasoni Advanced stochastic processes (4 cr)

GRS PY771 Rothschild Biophysics (4 cr)

 

*Note: MA771 is very theoretical, and not much of it is directly connected with neuroscience.