Highlighted Fall 2022 Courses


GRS MA 681 A1 – Accelerated Introduction to Statistical Methods for Quantitative Research

Contact Information: Dr. Uri Eden (tzvi@bu.edu)

Lecture: Tue/Thu, 9:30 am – 10:45 am

Discussion: GRS MA681 A2, AC Stat Methods, Monday, 1:25 pm – 2:15 pm          

Discussion: GRS MA681 A3, AC Stat Methods, Monday, 2:30 pm – 3:20 pm         

Location: Contact Dr. Uri Eden

Description: Introduction to statistical methods relevant to research in the computational sciences. Core topics include probability theory, estimation theory, hypothesis testing, linear models, GLMs, and experimental design. Emphasis on developing a firm conceptual understanding of the statistical paradigm through data analyses. 4 cr

GRS MA 861 – Seminar: Applied Mathematics:
Current Topics in Theoretical Neuroscience

Contact Information: Dr. Gabriel Koch Ocker (gkocker@bu.edu)

Lecture:  Arranged

Location: Contact Dr. Gabriel Koch Ocker

Description: A central theme of physiology, including neurophysiology, is the study of structure-function rela-tionships. These questions are gaining increased relevance with our increasing ability to make joint measurements of neural activity and neuronal network structure, either through large-scale functional recordings or anatomical reconstructions. These types of question are also of central importance in understanding how neural network architectures and weights impact the performance of neural networks in machine learning.

This seminar will focus on theoretical tools to understand structure-function relationships in neural network models. We will introduce classic theoretical methods for deriving links between the structure of anatomical or functional networks and their activity, and examine recent applications and extensions of these in neuroscience. 4 cr


GRS MA 881 A1 – Statistics Seminar: Time series analysis for neuroscience research

Contact Information: Dr. Emily Stephen, estephen@bu.edu

Lecture/Location: TBA

Description: This seminar course will provide an overview of statistical timeseries modeling for neuroscience applications. Core topics include spectral analysis, regression and generalized linear modeling, state space modeling, and other latent state models. Special emphasis on reading and discussing applications in recent literature. 4 cr

Prerequisites: basic probability and statistics, linear algebra, Python or Matlab programming


SPH BS 704 A1 – Introduction to Biostatistics

Contact Information: Dr. Sarah Van Ness Leavitt (sv1205@bu.edu)

Lecture: Monday, 6:00 pm – 8:20 pm

Location: Contact Dr. Sarah Van Ness Leavitt 

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 cr


CAS BI 598 A1 – Neural Circuits

Contact Information: Dr. Alberto Cruz-Martin (acmartin@bu.edu)

Lecture: Tue/Thu, 9:00 am – 10:45 am

Location: Contact Dr. Alberto Cruz-Martin

Description: Reviews modern techniques and toolsets that are capable of dissecting neural circuits, which are critical for understanding how coordinated patterns of neural activity lead to complex behavior. Recent literature on information processing, guided behavior and cognition is discussed. Also offered as CAS NE 598. 4 cr.


GRS BI 645 A1 – Cellular and Molecular Neurophysiology

Contact Information: Dr. Jen-Wei Lin (jenwelin@bu.edu)

A1 LEC: Tue/Thu, 2:00 pm – 3:15 pm

D1 LAB: Mon, 2:30 pm – 6:15 pm

Location: Contact Dr. Jen-Wei Lin

Description: Cellular and molecular basis of neural excitability and synaptic transmission. The molecular understanding of ion channels is extrapolated to higher brain functions such as learning, memory, and sleep. Three hours lecture, three hours laboratory, one hour pre-lab. 4 cr.


CAS PS 528/NE 528 A1 – Human Brain Map 

Contact Information:  Dr. Joseph McGuire (jtmcg@bu.edu 

Lecture/discussion:  Tues/Thurs, 9:30 am – 10:45 am

Location: Contact Dr. Joseph McGuire

Course description: Localization in the brain of human mental functions and the study of their neural mechanisms. Topics include methods (fMRI, PET, TMS, ERP), memory, perception, recognition, attention, and executive processes. 4 credits


CAS CN 510 A1 – Principles and Methods of Cognitive and Neural Modeling

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

LEC:  Tue/Thu, 11:00 am – 12:15 pm


Location: PRB 146

Description: Explores psychological, biological, mathematical, and computational foundations of behavioral and brain modeling. Topics include organizational principles, mechanisms, local circuits, network architectures, cooperative and competitive non-linear feedback systems, associative learning systems, and self-organizing code-compression systems. The adaptive resonance theory model unifies many course themes. CAS CN 510 and 520 may be taken concurrently. 4.0 credits.


SAR SH 523 A1 – Introduction to Speech Science

Contact: Nicole Tomassi (ntomassi@bu.edu)

Lecture/discussion: Mon/Wed, 12:20 pm – 2:05 pm          

Location: Contact Nicole Tomassi

Description: Lecture, laboratory, and demonstrations. Introduction to the basic physics of sound, including the decibel scale, spectral analysis, and resonance. Includes speech production, speech perception and suprasegmental effects. 4 cr

Prereq: (SARSH221 & SARSH522)



GMS NE 710 A1 – Neural Plasticity and Perceptual Learning (Meets w/ENG BE 710)

Contact: Lucia Vaina (vaina@bu.edu)

Lecture/discussion: Mon/Wed, 4:30 pm – 6:15 pm          

Location: Contact Lucia Vaina

Description: This course has two distinct parts. The first part focuses on molecular, cellular, networks, and behavioral aspects of neural plasticity and its relationship to visual perception, hippocampal learning and memory and it will emphasize the fundamental principles of cortical plasticity in the primary sensory areas, from synapses, to circuits, to cortical maps.  We will discuss critical periods of plasticity in the visual cortex and the rules that allow strengthening or weakening synapses that impact learning and how these circuits can be manipulated to change the course of critical periods in adult mice. Modern imaging and computational tools in neuroscience,  such as optogenetics and predictive coding methods will be discussed with examples of experimental applications in mice. The second part of the course addresses neuroplasticity in non-human and human primates, in particular the features that contribute to plasticity of high-order processes underlying cognition, emotion and memory. The focus will be on neural circuit mechanisms underlying plasticity in networks linking the thalamus, amygdala, and cerebral cortex, their synaptic interactions and the role they play in flexible behavior, attention, and memory consolidation, and how they are disrupted in psychiatric and neurologic disorders. Discussion of the neural basis of working memory including recent controversies about the relative importance of persistent activity vs short-term synaptic plasticity and the stationary vs dynamic neural codes. We will discuss fundamental properties of primate visual, auditory and motor control systems, and their investigation via magnetoencephalography (MEG),  MEG/fMRI data fusion and advanced deep learning.  Homework, class presentations and semester project are required. The course will involve course faculty drawn from Boston University, MIT and Harvard Medical School. 4 cr