Spring 2024 Featured Courses

*Note: The course information below has been provided to us by faculty members and we will update this page on a regular basis. If you are a student who would like additional courses highlighted for your colleagues please contact the GPN Administrative Coordinator Yinglin Li (yinglinl@bu.edu).


ENG BE 700 A2 – Foundations of Biomedical Data Science and Machine Learning

Contact Information: Dr.Brian DePasquale (bddepasq@bu.edu) and Dr. Michael Economo (mne@bu.edu)

Lecture/discussion: Tue & Thur 1:30 pm-3:15 pm

Location: CAS 218

Course description: This course will cover conceptual and practical aspects of data science and introductory machine learning for biomedical engineers. This course serves as a foundational course in data analytics for BME Ph.D. students. It is designed to follow a graduate-level introductory programming course and will prepare students for graduate-level courses and research focused on more advanced applications of machine learning and data science. This course will cover the theory and practical applications of hypothesis testing, model fitting and parameter estimation, classification, clustering, dimensionality reduction, and machine learning.



CAS PS 530 A1 – Neural Models of Memory Function

Contact Information: Dr. Michael Hasselmo (hasselmo@bu.edu)

Lecture/discussion: Thurs, 3:30 pm – 6:15 pm

Location: Contact Dr. Michael Hasselmo

Course description: Computational models of neurobiological mechanisms for memory function and spatial navigation, with a particular emphasis on cellular and circuit models of the hippocampus and related cortical structures. Also offered as CAS NE 530. 4 credits

Prereq: consent of instructor.


CAS CN 530 A1 – Neural and Computational Models of Vision

Contact Information:  Dr. Arash Yazdanbakhsh (yazdan@bu.edu)

Lecture/discussion:  Mon & Wed, 10:10 am – 11:55 am 

Location: Contact Dr. Yazdanbakhsh 

Course description: This course explores the psychological, biological, mathematical and computational foundations of visual perception. Lectures and readings combine with simulation and essay assignments to provide an intensive and self-contained examination of core issues in early and middle visual processing. Mathematically specified neural and computational models elucidate the structure and dynamics of the mammalian visual system. The course elucidates the psychophysics and physiology of mammalian vision, both as a means of better understanding our own human intelligence, and as a foundation for tomorrows machine vision architectures and algorithms. 4 credits

Prereq: (CASCN510) or consent of instructor.


GRS PS 704 A1 – Theoretical Cognitive Neuroscience

Contact Information: Dr. Marc Howard (marc777@bu.edu)

Lecture/discussion: Thurs, 3:30 pm – 6:15 pm

Location: Contact Dr. Howard

Course description: This course covers computational models of cognition, including working memory, classical conditioning, episodic memory, and evidence accumulation. For each of these topics, we work through the math and the behavioral evidence for them as well as providing some history. In the second part of the class, we attempt to unify these computational approaches using Laplace neural manifolds, which we contrast with typical artificial neural networks. Comfort with calculus is required; other math (linear algebra, differential equations, integral transforms) will be introduced as needed. 4 credits


GRS NE 741 A1 – Neural Systems: Functional Circuit Analysis

Contact Information: Dr. Ian Davison (idavison@bu.edu)

Lecture/discussion:  LEC  Thursday, 12:30 pm – 3:15 pm,                            DIS  Friday, 10:10 am – 11:00 am

Location: Contact Dr. Davison

Course description: An in-depth survey of powerful new approaches for understanding nervous system function, linking neural activity to behavior. Topics include anatomical connectivity, behavioral methods, and both recording and manipulating the activity of neural populations. Required course for 2019 entering GPN class. 4 credits


GRS NE 742 A1 Neural Systems: Cognition and Behavior

Contact Information: Dr. Chantal Stern (chantal@bu.edu)

Lecture/discussion: Tues, 12:30 pm – 3:15 pm

Location: Contact Dr. Stern

Course description: Surveys current neuroscience research. The goal is to develop an understanding of nervous system function in animals and humans, linking cellular and systems level neural circuitry to cognition and behavior. 4 credits

Grad Prereq: graduate standing in Neuroscience, or Brain, Behavior, and Cognition, or Neurobiology; or consent of instructor.


SAR HS 549 – Mechanisms of disruption in brain disorders

Contact Information:  Dr. Basilis Zikopoulos (zikopoul@bu.edu)

Lecture/discussion: Wed, 6:30 pm – 9:15 pm

Location: Contact Dr. Zikopoulos

Course description: The goal of this course is to familiarize graduate students and advanced undergraduates (at least one prior neuroscience or related class and permission of instructor required) with the organization and functions of brain networks that are preferentially disrupted in disorders, so we can then discuss mechanisms underlying their disruption in pathology. The focus will be on the central nervous system, including the cortex, thalamus, amygdala, hippocampus, and basal ganglia and disorders involving these brain regions, the pathways connecting them, their interactions, and their neural circuits. We will cover psychiatric disorders, including autism, schizophrenia, depression, sleep deficits, anxiety, and phobias, as well as traumatic and neurodegenerative disorders like traumatic encephalopathy, dementias, Alzheimer’s, and Parkinson’s. We will also examine different methods used to study disorders, including use of post-mortem brain tissue, animal models, functional and structural neuroimaging, neurophysiology, and computational modeling, therefore the approach will be multi-disciplinary. 4 credits

Prereq: Students should have some basic knowledge of nervous system structure and function and/or human physiology.


SAR HS 582 A1 – Neuroanatomy and Neurophysiology

Contact Information:  Dr. Basilis Zikopoulos (zikopoul@bu.edu)

Lecture/discussion:  Tues & Thurs, 2:00 pm – 3:15 pm

Location: Contact Dr. Zikopoulos

Course description: Lecture and laboratory related to the detailed study of the development, morphology, internal configuration, functions, and pathological deficits of the peripheral and central nervous system in humans. Spring semester only. 4 credits


SAR HS 755 A1 – Readings in Neuroscience

Contact Information:  Dr. Helen Barbas (barbas@bu.edu)

Lecture/discussion:  Tues, 6:30 pm – 9:15 pm

Location: Contact Dr. Barbas

Course description: Review of basic principles of neuroscience at an intermediate level, followed by readings and discussion on topics from the current neurosciences research. 4 credits, 2nd semester every other year

Grad Prereq: Consent of instructor.


SPH BS 704 A1 Introduction to Biostatistics

Contact Information: Dr. Fatema Shafie Khorassani

Lecture: Monday, 2:00 pm – 4:20 pm

Location: Med campus

Description: This course provides an overview of biostatistical methods, and gives students the skills to perform, present, and interpret basic statistical analyses. Topics include the collection, classification, and presentation of descriptive data; the rationale of estimation and hypothesis testing; analysis of variance; analysis of contingency tables; correlation and regression analysis; multiple regression, logistic regression, and the statistical control of confounding; sample size and power considerations; survival analysis. Special attention is directed to the ability to recognize and interpret statistical procedures in articles from the current literature. Students will use the R statistical package to analyze public health related data. 3 credits


GRS MA 765 A1 – Time Series Analysis for Neuroscience Research

Contact Information: Dr. Emily P. Stephen (estephen@bu.edu)

Lecture/Discussion: Tues/Thurs 9:30 am – 10:45 am

Course description: Provides an overview of statistical time-series modeling for neuroscience applications. Topics include regression and generalized linear modeling, state space modeling, and parametric and nonparametric spectral analysis. Special emphasis on reading and discussing applications in recent literature. 4 credits

Prereq:  CAS MA 213 or GRS MA 681CAS MA 242CAS MA665/MA666; or consent of instructor.