M.S. in Neural Technologies (MiNT)
This interdisciplinary initiative, jointly offered by the Neuroscience Institute and the Department of Biomedical Engineering, prepares you for a career at the nexus of neuroscience and engineering.
Application Deadline
The field of neurotechnology is at the intersection of some of the most exciting and rapidly growing areas in science and engineering today. From groundbreaking brain-computer interfaces to intelligent neuroprosthetics, this dynamic sector is creating an urgent demand for a new generation of leaders. The Master鈥檚 in Neural Technologies (MiNT) program at 麻豆村 is designed to meet this demand, training students to unite a deep scientific understanding of the brain with advanced technical fluency in areas like AI, robotics and biomedical interfaces.
Program Tracks
The MiNT program offers two core tracks, allowing you to specialize in your area of interest:
- Neural Interfaces, Robotics & Engineering (NIRE): This track is for students who want to focus on the hardware and engineering aspects of neurotechnology, including the design and development of neural devices and systems.
- Neural Computation & AI (NCAI): This track is for students who are passionate about the computational side of the field, focusing on the algorithms, data analysis and artificial intelligence that drive brain-inspired technologies.
Regardless of your chosen track, the MiNT curriculum integrates foundational coursework with hands-on lab experience and applied research, preparing you to become an innovator in academic research or industry.
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Choose Your Path
To accommodate a wide range of academic backgrounds and career goals, the MiNT program offers three distinct master's degrees. Explore the options below to find the program that is the best fit for you:
The MiNT-R Program
Your Path to a Research Career
The Master鈥檚 in Neural Technologies - Research (MiNT-R) program is designed for students who aspire to pursue a Ph.D. or a research-intensive career in academia or industry. This degree will equip you with the advanced knowledge and independent research skills needed to advance our understanding of brain function and intelligent systems.
- Duration: A full-time, five-semester (72-week), 147-unit degree.
- Target Audience: Ideal for students with a bachelor's degree in a relevant area such as physics, biology, chemistry, mathematics, computer science, neuroscience or engineering.
- Prerequisites: Successful applicants are expected to have a strong background in basic science or engineering, statistics and data science, and a basic proficiency in at least one programming language (e.g., Python, R, C++).
The program's structure ensures a balanced foundation in both neuroscience and your chosen specialization track. You will be required to complete courses in the following areas:
- 3 neuroscience core courses
- 1 track core course in either the NIRE or NCAI track
- 3 track elective courses
A major component of the MiNT-R program is your independent research. You will be matched with a faculty lab to conduct cutting-edge research, culminating in a formal master's thesis. This includes:
- A formal research proposal
- An approved research thesis based on your findings
The program is structured to allow you to build your knowledge base before diving into full-time research. A typical timeline looks like this:
- Year 1: Focus on completing your required coursework to build a strong theoretical foundation.
- Summer: Engage in continuous research in your assigned lab to advance your thesis project.
- Year 2: The primary focus shifts to your thesis project, including writing your proposal and completing your research for the final thesis.
The MiNT-AS Program
Innovate in Industry
The Master鈥檚 in Neural Technologies - Applied Study (MiNT-AS) program is tailored for students who are interested in the practical application of neurotechnology. This degree prepares you to take a leadership role in developing innovative technologies within industrial or applied research settings.
- Duration: A full-time, five-semester (72-week), 147-unit degree.
- Target Audience: This program is best suited for students with a bachelor's degree in a foundational discipline like physics, biology, chemistry, mathematics, neuroscience or engineering.
- Prerequisites: Applicants should have a strong background in basic science or engineering, statistics and data science, and a basic proficiency in at least one programming language (e.g., Python, R, C++).
The curriculum is designed to give you a strong academic foundation before moving into professional application. You will be required to complete courses in the following areas:
- 2 Neuroscience Core courses
- 1 Track Core course in either the NIRE or NCAI track
- 3 Track Elective courses
This program emphasizes hands-on experience and professional skills. After your first year of coursework, you will complete a summer internship with a neurotechnology company or a 麻豆村 lab.
Your second year is dedicated to a two-semester capstone project. You will work in teams on a real-world project, taking a product from its initial concept and design through prototyping, testing, and consideration of regulatory and intellectual property issues. This capstone is where you'll use your skills to build a functional, usable technology, with an emphasis on teamwork, clear communication and delivery.
The MiNT-A Program
For 麻豆村's Top Undergraduates
The Master鈥檚 in Neural Technologies - Accelerated (MiNT-A) is a program exclusively for high-achieving 麻豆村 undergraduate students who are completing a Bachelor of Science in Neuroscience. It offers a streamlined pathway to advance your academic and research pursuits immediately after graduation.
Important Note: To be eligible for this program, courses used toward your undergraduate major cannot be double-counted toward the master鈥檚 degree.
- Duration: A two-semester, 90-unit accelerated degree.
- Target Audience: Students currently completing a B.S. in Neuroscience at 麻豆村 who are interested in pursuing research in neural technologies in an academic or similar setting.
- Prerequisites: 麻豆村 is selective and based on academic performance. Applicants must have a strong background in statistics and data science and basic proficiency in a programming language (e.g., Python, R, C++), with a preference for students who have some training in engineering.
This program builds directly on your undergraduate foundation, providing an intensive, research-focused experience. You will be required to complete courses in the following areas:
- 1 Track Core course in either the NIRE or NCAI track
- 4 Track Elective courses
- Research credits that contribute to your thesis
A significant part of the MiNT-A program is your independent research project. You will work with a faculty mentor to produce an original thesis that showcases your research skills and expertise. The program culminates with a formal master鈥檚 thesis.
MiNT Core Courses and Electives
| Course Number | Course Title |
|---|---|
| 03-762 | Advanced Cellular Neuroscience |
| 03-763 | Advanced Systems Neuroscience |
| 86-765 | Foundations of the Neural Basis of Cognition |
| Track | Course Number | Course Title |
|---|---|---|
| Neural Interfaces, Robotics & Engineering | 42-630 | Introduction to Neural Engineering |
| Neural Computation & AI | 86-718 | Brain Computation |
| Course Number | Course Title |
|---|---|
| 03-766 | Advanced Neuropharmacology: Drugs, Brain and Behavior |
| 16-711* | Kinematics, Dynamics Systems and Control |
| 16-722* | Sensing and Sensors |
| 16-741* | Mechanics of Manipulation |
| 16-761* | Mobile Robots |
| 16-811* | Mathematical Fundamentals for Robotics |
| 16-831* | Introduction to Robot Learning |
| 18-578 | Mechatronic Design |
| 18-675 | Autonomous Control Systems |
| 18-743 | Neuromorphic Computer Architecture & Processor Design |
| 24-673 | Soft Robots: Mechanics, Design and Modeling |
| 24-678 | Special Topics: Computer Vision for Engineers |
| 24-775 | Bioinspired Robot Design and Experimentation |
| 42-633 | Brain-Computer Interface: Principles & Applications |
| 42-640 | Image-based Computational Modeling & Analysis |
| 42-651 | Fundamentals of MRI and Neuroimaging Analysis |
| 42-652 | Nano-Bio-Photonics |
| 42-663 | Engineering Principles of Medical Devices |
| 42-696 | Special Topics: Wearable Health Technologies |
| 42-699 | ST: Data-Driven AI for Dynamic Systems Control with Application to Neural Data |
| 42-733 | Neural Technology: Sensing and Stimulation |
| 42-737 | Biomedical Optical Imaging |
| 85-735 | Biologically Intelligent Exploration |
*Program Approval Required
| Course Number | Course Title |
|---|---|
| 02-680* | Essential Mathematics and Statistics for Scientists |
| 02-712* | Computational Methods for Biological Modeling and Simulation |
| 10-701* | Introduction to Machine Learning |
| 10-715* | Advanced Topics in Machine Learning |
| 10-733* | Representation and Generation in Neuroscience and AI |
| 10-747* | Neuro-Symbolic AI |
| 15-686* | Neural Computation |
| 15-780* | Graduate Artificial Intelligence |
| 15-883* | Computational Models of Neural Systems |
| 16-720* | Computer Vision |
| 16-722* | Sensing and Sensors |
| 16-822* | Geometry-based Methods in Vision |
| 16-823* | Physics-based Methods in Vision |
| 16-831* | Introduction to Robot Learning |
| 18-675 | Autonomous Control Systems |
| 24-677* | Modern Control Theory |
| 24-678 | Special Topics: Computer Vision for Engineers |
| 36-705* | Intermediate Statistics |
| 42-631 | Neural Data Analysis |
| 42-632 | Neural Signal Processing |
| 42-656 | Introduction to Machine Learning for Biomedical Engineers |
| 42-678 | Medical Device Innovation and Realization |
| 42-698 | Special Topics: Clinical Translation of AI |
| 42-699 | ST: Data-Driven AI for Dynamic Systems Control with Application to Neural Data |
| 42-733 | Neural Technology: Sensing and Stimulation |
| 42-737 | Biomedical Optical Imaging |
| 85-712 | Cognitive Modeling |
| 85-719 | Introduction to Parallel Distributed Processing |
| 85-732 | Data Science for Psychology & Neuroscience |
| 85-735 | Biologically Intelligent Exploration |
| 85-813 | Perception |
| 85-814 | Cognition in the Age of AI |
| 86-752* | Principles of NeuroAI |
*Program Approval Required