Core Courses

Our trainees will enter the program with diverse backgrounds, for example individuals trained in the biological / behavioral sciences, engineering, or quantitative sciences. To address this diversity, we will tailor curricula to each trainee using a core curriculum that covers three course areas: 1) Optical Engineering, 2) Neuroscience, 3) Data and Statistical Analysis. Trainees are required to take one course from each category and a fourth course from one of the categories chosen to complement their dissertation research. Students are allowed to count courses they are taking for their degree program (in other words these do not have to be additional courses). Each topic area is considered necessary as a foundation for neurophotonics trainees irrespective of their graduate discipline. The courses are offered at BU and emphasize communal learning, with each NRT Fellow cohort taking the same courses together (along with other BU students).  Each trainee will pursue elective courses to (i) satisfy the degree completion requirements of the trainee’s home department (BME, ECE, BIO, PBS, MED, SAR, GPN, and MATH), and (ii) acquire the advanced knowledge required to undertake the trainee’s research and fill knowledge gaps. Many courses relevant to neuroscience, engineering, and data analysis allow us the flexibility to develop trainee knowledge at the most appropriate level.

Each trainee will work with the Mentorship Committee to identify his/her/their educational experience and needs and select the courses most appropriate to achieving each trainee’s goals. For example, BI755 is required for students in the BIO and GPN programs and fulfills the Neuroscience core area for NRT.  These students will then take elective courses that fulfill the remaining core areas in Optical Engineering and Data and Statistical Analysis. In this way, we will establish for each trainee a unique curriculum that combines rigorous – yet accessible – training in computation, optics, and neuroscience. The course options are vast, and suitable to satisfy a wide diversity of trainee interests and knowledge gaps.

A list of potential courses that trainees can select from include the following. However, students are welcome to suggest other courses, but please discuss with the program directors to confirm other courses meet the requirement.

Optical Engineering Core Area Courses

BE772: Neurotechnology Devices, Instructor David Boas – From electro-physiology to optical and MRI, non-invasive to invasive, neuro-sensing to neuro-modulation, and spanning applications in humans and animals; this course will cover the latest developments in devices used to study the brain. The course will center around eight recent journal papers that introduces or utilizes novel devices for the advancement of neuroscience. Lectures are given on the topic and then students read and critically discuss the techniques used, the background of the literature leading up to the paper, the strengths and weaknesses of the paper and the next steps.

BE571: Introduction to Neuroengineering, Instructor: Xue Han. – This course covers existing and future neurotechnologies for analyzing brain signals and for treating neurological and psychiatric diseases. It focuses on the biophysical, biochemical, anatomical principles governing the design of current neurotechnologies, with a goal of encouraging new techniques. Topics include basic microscopic and macroscopic architecture of the brain, the fundamental properties of individual neurons and ensemble neural networks, electrophysiology, imaging methods, optical neural control technologies, optogenetics, neuropharmacology, gene therapy, and stem-cell therapy. All students complete design projects.

BE517: Optical Microscopy of Biological Materials, Instructor: Jerome Mertz – This course provides hands-on training in state-of-the-art optical microscopy techniques to address questions in biology and biological materials. Students learn present basic concepts in imaging and detection, which are then applied in biological imaging laboratory exercises. By the end of the course, students will be familiar with all of the instruments in the BME imaging facility and will have gained the necessary tools to carry out modern biological imaging experiments.

EC 555: Introduction to Biomedical Optics, Instructor: Irving Bigio – This course surveys the applications of optical science and engineering to a variety of biomedical problems, with emphasis on optical and photonics technologies that enable real, minimally-invasive clinical and laboratory applications.

EC 522: Computational Optical Imaging, Instructor: Lei Tian – Computational optical imaging systems have a wide range of applications in consumer photography, scientific and biomedical imaging, microscopy, defense, security and remote sensing. This course looks at this new design approach as it is applied to modern optical imaging, with a focus on the tools and techniques at the convergence of physical optical modeling, and signal processing.

Neuroscience Core Area Courses

BI755: Cellular and Systems Neuroscience, Instructors: Douglas Rosene and Jerry Chen – This course covers the electrical properties of neurons and synaptic processing, to the cytology of Neurons and Glia, to synaptic plasticity and neuropsychiatric disorders, to the molecular tools that drive discovery, the morphology of the cortex, thalamus and its development, neurophysiology, channels and transporters, overview of neuroanatomy, molecular signaling and gene regulation, auditory and vestibular receptors, sensory receptors, somatosensory cortex, pain, vision, olfaction and taste, autonomic motor system, striatum and its dopaminergic pathways that differentially regulate motor systems and reward, basic and advanced development of the CNS and finally learning and memory and its pervasive disorders.

BI741/NE741: Neural systems: Functional Circuit Analysis. Instructor: Ian Davison – This course complements first-semester foundational curriculum by offering an in-depth survey of powerful cutting-edge experimental and analysis approaches for understanding nervous system function and linking neural activity to behavior. Broad topics include connectomics, high-throughput behavioral measurements, and tools for large-scale recording and manipulation of activity within neuronal populations. A major goal of the course is to provide early-career graduate students with first-hand interactions with BU faculty who are recognized leaders in course topics to help foster collaborative efforts.

NE742: Neural Systems: Cognition and Behavior. Instructor: Chantal Stern – Graduate-level introduction to cognitive neuroscience. The course aims to provide students with an understanding of the cognitive functions of the normal human brain, with one goal being to tie this material in with current knowledge at the cellular and systems level from animal models.

NE528: Human Brain Mapping. Instructor: Joseph McGuire – Introduction to human brain mapping. Topics include methods (fMRI, PET, TMS, ERP), memory, perception, recognition, attention, and executive processes.

Data and Statistical Analysis Core Area Courses

BE700: Foundations of Biomedical Data Science and Machine Learning, Instructors: Michael Economo and Brian Depasquale – 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. All students complete individual projects in which they apply course concepts to their own experimental data.

MA666: Advanced Modeling and Data Analysis in Neuroscience, Instructor: Mark Kramer – This course surveys advanced spectral analysis techniques to characterize neural time series data, regression approaches to characterize behavioral data, and mathematical models of neural activity and behavior. A major focus is the implementation of computational methods using computer software and graphical methods for model analysis.

MA681: Accelerated Introduction to Statistical Methods for Quantitative Research, Instructor: Uri Eden – This course introduces statistical methods relevant to research in the computational sciences. Core topics include probability theory, estimation theory, hypothesis testing, linear models, generalized linear models, and experimental design. An important emphasis is developing a firm conceptual understanding of the statistical paradigm through data analyses.

MA765: Time series analysis for neuroscience research, Instructor: Emily Stephen – This course provides an overview of statistical timeseries modeling for neuroscience applications. Topics include regression and generalized linear modeling, state space modeling, and parametric and nonparametric spectral analysis, with special emphasis on reading and discussing applications in recent literature.

MA769: Mathematical neuroscience, Instructor: Gabe Ocker – This course surveys fundamental models, methods, and questions in mathematical and theoretical neuroscience. Topics include biophysical and reduced single-neuron models, synaptic plasticity and learning, applied dynamical systems, stochastic processes and stochastic differential equations, and population density and mean field approaches.

CN510: Principles and Methods of Cognitive and Neural Modeling: Instructor Arash Yazdanbakhsh – This course introduces students to important themes and approaches in the computational modeling of biological neural systems. The class combines systems-level neuroscience, mathematical modeling techniques, and computer simulation techniques. Neural network models are covered in detail, thus exposing students to mathematical modeling techniques that will serve as the basis for sensory, motor, and memory models. A major theme is the use of these modeling techniques to tie together anatomical, physiological, and psychological data, as exemplified by the neural network modeling studies covered in the course.