WPI’s MS in neuroscience advances understanding of the human brain—one of the most significant and urgent scientific challenges of our time. This complex biological system holds the key to who we are and how we perceive and interact with the world. A growing number of individuals are affected by neurological and psychiatric illnesses that are poorly understood. The field of neuroscience is at a point where deep learning, AI, neuroengineering, and related advances will stimulate major breakthroughs. 

The MS in neuroscience program provides students with a strong foundation in computational, molecular, psychological, quantitative, and interdisciplinary approaches to neuroscience. Students gain expertise in basic and translational neuroscience coupled with a strong computational base, links to industry partners, and supported study-abroad opportunities. In addition to a program partnership with WPI's Department of Biomedical Engineering (BME), our expertise in cutting-edge data science methods such as deep learning and AI will stimulate novel and productive purpose-driven research projects.

Our team of interdisciplinary faculty and students thrive from the synergy of our diverse approaches to understanding the brain and nervous system. The faculty involved in the program have a strong record of funding and provide an excellent research-oriented environment that provides collaboration and one-on-one mentorship.

Neuroscience MS

Curriculum

The four main participating departments—Computer Science, Biology & Biotechnology, Chemistry & Biochemistry, and Social Science & Policy Studies—define four broad areas of the program:

 

Computational Neuroscience: Training in the use of experimental and theoretical methods for the analysis of brain function 

 

Cellular and Molecular Neuroscience: Training in neurophysiological methods such as electrophysiology, optogenetics, molecular biology, genetics, biochemistry and biophysics, appropriate to topics in neurobiology

 

Systems Neuroscience: Training in structure-function relationship of neural networks, neural substrates of learning and memory, psychopharmacology of nervous system disorders including Alzheimer’s disease

 

Psychological Science: Training in how the brain and nervous system interact with development, mental health, cognition, and social processes to mediate behavior

The graduate program in neuroscience will train students in the complexity of the nervous system and position them to work on the many unanswered questions about the brain and how it functions. WPI’s core strengths in the areas of computational and data sciences, as well as in artificial intelligence and the life sciences areas, give students a comprehensive and cutting-edge approach to the field.

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Neuroscience
The rapid advances in neuroscience require a workforce ready to tackle the scientific, engineering, and ethical challenges of this fascinating field.

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Neuroscience
WPI’s neuroscience program is rooted firmly in a comprehensive and multidisciplinary approach to neuroscience with a computational methodology.

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Neuroscience
WPI’s neuroscience program challenges students to think about how to better understand the brain while considering things like genetics, neurological diseases, and human behavior.
  • Requirements for the Neuroscience MS Degree

    Students pursuing the MS degree in neuroscience must complete a minimum of 31 credits of relevant work at the graduate level. In consultation with their assigned neuroscience program academic advisor, students will prepare a plan of study outlining the selections that satisfy the degree requirements and that receive approval of the program’s review committee.

  • Core Neuroscience Coursework Requirements (minimum 19 credits)
    Requirements Minimum Credits
    • At least three graduate neuroscience courses
    9
    • At least one biology courses
    3
    • At least one computer science course
    3
    • One bioethics course
    1
    • One scientific writing or experimental design course
    3
  • Electives

    Relevant Neuroscience courses
    NEU 501 Neuroscience
    NEU 502 Neural Plasticity
    NEU 503 Computational Neuroscience
    NEU 504 Advanced Psychophysiology
    NEU 505 Brain-Computer Interaction

    Relevant Bioinformatics and Computational Biology courses
    BCB 501/BBT 581 Bioinformatics​
    BCB 502/CS 582 Biovisualization​
    BCB 503/CS 583 Biological and Biomedical Database Mining
    BCB 504/MA 584 Statistical Methods in Genetics and Bioinformatics​
    BCB 510 Bioinformatics and Computational Biology Seminar

    Relevant Biology and Biotechnology courses
    BBT 561 Model Systems: Experimental Approaches and Applications
    BBT 581/BCB 501 Bioinformatics​
    BB 570/CH 555 Cell Signaling

    Relevant Biomedical Engineering courses
    BME 550 Tissue Engineering
    BME 555 BioMEMS and Tissue Micro engineering
    BME 560 Physiology for Engineers
    BME 583 Biomedical Microscopy and Quantitative Imaging

    Relevant Chemistry and Biochemistry courses
    CH 538 Medicinal Chemistry
    CH 541 Membrane Biophysics
    CH 555D Drug and Regulations
    CH 555R Drug Safety and Regulatory Compliance
    CH 555/PH597 Cell Mechanics
    CH 555/BB570 Cell Signaling

    Relevant Computer Science courses
    CS 5007 Introduction to Applications of Computer Science with Data Structures and Algorithms
    CS 5084 Introduction to Algorithms: Design and Analysis
    CS 528 Mobile and Ubiquitous Computing
    CS 534 Artificial Intelligence
    CS 539 Machine Learning
    CS 541/DS 541 Deep Learning
    CS 542 Database Management Systems
    CS 546 Human-Computer Interaction
    CS 548 Knowledge Discovery and Data Mining
    CS/RBE 549 Computer Vision
    CS/SEME 565 User Modeling
    CS/SEME 566 Graphical Models for Reasoning under Uncertainty
    CS/SEME 567 Empirical Methods for Human-Centered Computing
    CS 573 Data Visualization
    CS 584 Algorithms: Design and Analysis
    CS 585/DS 503 Big Data Management
    CS 586/DS 504 Big data Analytics

    Relevant Data Science courses:
    DS 501 Introduction to Data Science
    DS 502/MA 543 Statistical Methods for Data Science

    Relevant Mathematical Sciences courses:
    MA 508 Mathematical Modeling
    MA 543/DS 502 Statistical Methods for Data Science
    MA 510/CS 522 Numerical Methods
    MA 511 Applied Statistics for Engineering and Scientists
    MA 542 Regression Analysis
    MA 546 Design and Analysis of Experiments
    MA 550 Time Series Analysis
    MA 556 Applied Bayesian Statistics

    In addition to the 19 credits in the core neuroscience coursework requirement, MS students must complete either the thesis option or the non-thesis option described below. Students supported with a teaching assistantship, research assistantship or fellowship for more than one academic year are required to do the thesis option.

  • Neuroscience MS Thesis Option

    Students in the neuroscience MS thesis option must complete a 9-credit thesis that is advised or co-advised by a faculty member affiliated with the neuroscience program. Students interested in research, and in particular those who are considering pursuing a PhD degree in neuroscience or a related area, are strongly encouraged to select the MS thesis option.

     

  • Neuroscience MS Non-Thesis Option

    As part of the completion of the remaining credits, students in the neuroscience MS non-thesis option are strongly encouraged to pursue a 3-6 credit research or practice-oriented internship that is approved and overseen by a faculty member of the neuroscience program Internships are generally in an industry setting or a research lab and will require a written report.

Neuroscience Faculty

Faculty members from intersecting and complementary departments join forces to provide a comprehensive and cutting-edge neuroscience program. With expertise in everything from psychology to AI, neuroscience at WPI combines all our core strengths.
 

Neuroscience Core and Affiliated Faculty

Faculty Profiles

Jean King

Jean King

Peterson Family Dean of Arts & Sciences
Arts and Sciences

A widely respected neuroscientist, Jean King joined the WPI community as the Peterson Family Dean of Arts and Sciences in 2017. In addition to her duties as dean, she is a professor in the Department of Biology and Biotechnology.

[...]
Erin Solovey

Erin Solovey

Assistant Professor
Computer Science

My research is in human-computer interaction. One focus of my research is on next-generation interaction techniques, such as brain-computer interfaces, physiological computing, and reality-based interaction. I design, build and evaluate interactive computing systems that use machine learning approaches to adapt and support the user’s changing cognitive state and context. I also investigate novel paradigms for designing with accessibility in mind, particularly for the Deaf community.

[...]
Jagan Srinivasan

Jagan Srinivasan

Associate Professor-Biological Science
Biology & Biotechnology

It has been my lifelong dream to become a professor in the field of Biology. Being a faculty member provides a great opportunity to teach and interact with students. Students by nature are highly inquisitive and motivated, and as teachers, we have the responsibility to guide our students to explore and think in new ways. I believe that teaching is a two-way interaction between teachers and students. I come from India and my parents, both of whom were teachers, taught me to strive for excellence in my scholarly pursuits.

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