Overview
The interdisciplinary BS/MS program in Neural Engineering delivers a rigorous training and the necessary skills required to solve complex problems at the interface of engineering, medicine and neuroscience. Graduates are prepared for successful careers in the biomedical industries, academia, or government (FDA, US Patent Office), or for further study in doctoral or health-related programs. The interdisciplinary BS/MS program is designed for undergraduate students enrolled at the University of Miami in Neuroscience, Psychology, Computer Science, Biology, Mathematics, Physics, Biomedical Engineering, or Electrical and Computer Engineering. The BS/MS students will receive undergraduate degrees in their selected majors and a graduate degree in Neural Engineering administered by the Department Biomedical Engineering. The interdisciplinary MS students will receive a graduate degree in Neural Engineering from the Department Biomedical Engineering.
The interdisciplinary nature of the departments and our strong ties with the University of Miami Miller School of Medicine provides students with many opportunity to collaborate with clinicians and researchers at several world-renowned research and clinical centers, including the Bascom Palmer Eye Institute, The Miami Project to Cure Paralysis, the Diabetes Research Institute, the University of Miami Ear Institute, the Biomedical Nanotechnology Institute (BioNIUM), the McKnight Brain Institute, the Sylvester Comprehensive Cancer Center, and the Miami Veterans Administration Research Service. There are opportunities to develop collaboratively courses, training, and new foci that take advantage of our existing institutional strengths and will foster new avenues for collaborations across each of the departments listed above.
Neural engineers build tools, techniques, and methods to understand, interface with and manipulate the nervous system. They are trained to solve problems and provide rehabilitative solutions for various pathologies or disorders afflicting the nervous system. Graduates with neural engineering background often find positions in industry, research and development, regulatory affairs, and quality engineering. Many graduates complete advanced degrees and join academic ranks.
Admission Requirements
The BS/MS program in Neural Engineering welcome students from diverse backgrounds, including
- Students enrolled in UM undergraduate degrees in biomedical engineering and other engineering disciplines who seek advanced professional training or specialization in a particular area of neural engineering;
- Students enrolled in UM undergraduate programs in Physics, Mathematics, Neuroscience, Computer Science, Chemistry, Biology or other fields of natural or health science who seek to diversify their career opportunities by acquiring an engineering degree;
- Students preparing for admission to advanced health-related or other professional programs such as medical school.
When to apply:
For BS/MS: Qualified students must apply prior to the advising period but at the latest before the final exams in the second semester of their junior year. Students are strongly advised to apply to the BS/MS program as early as possible in their junior year to facilitate academic advising and course selection in the second semester of their junior year. Before submitting an application, interested students should discuss the program and the possibility of entering the program with an academic advisor.
Curriculum Requirements
The graduate component of the BS/MS in Neural Engineering curriculum consists of three components: core courses, elective courses on neural engineering, and an industry or capstone project. Students must complete at least 30 credits of graduate level courses to complete the degree.
The core courses teach the fundamental skills of neuroscience, neuroanatomy, and physiology. The interdisciplinary electives in neural engineering courses are designed to fit the student’s chosen competency in specific areas of neural engineering supported by the program. The academic units participating in the graduate program will each offer courses relevant to their discipline.
The industry or capstone project will be taken for 6 credits. Projects are done typically within two semesters, supervised by a faculty member in an appropriate academic unit within the program (Biomedical Engineering, Computer Science, Neuroscience, Biology, Physics, Physiology and Biophysics, Psychology, or Electrical Engineering). Students can also complete their projects in an industry-setting. The project culminates with a report (or a research manuscript) detailing the milestones achieved/work completed and knowledge gained, and a presentation to faculty and students in the program.
Curriculum Requirements
| Code | Title | Credit Hours |
|---|---|---|
| BACHELOR'S DEGREE REQUIREMENTS | 120 | |
| Refer to the links below for more information on the BS requirements. | ||
| MASTER'S DEGREE REQUIREMENTS | ||
| Core Courses | ||
| BME 615 | Current Trends in Neural Engineering | 3 |
| Graduate Level Neuroscience Course chosen from the following: | 3 | |
| Neurophysiology for Engineers | ||
| NEU II - Systems Neuroscience | ||
| Neuroanatomy | ||
| Principles of Membrane Physiology and Biophysics I | ||
| Statistics Course Chosen from the Following: | 3 | |
| Biostatistics for the Biosciences | ||
| Statistical Learning | ||
| Statistical Analysis | ||
| Introduction to Mathematical Statistics | ||
| Statistical Principles of Clinical Trials | ||
| Advanced Biostatistics | ||
| Neural Engineering Interdisciplinary Electives | 15 | |
| To be selected from the following any graduate level courses for the neural engineering track (some courses may have pre-requisites that must be met prior to enrollment): | ||
| Introduction to Machine Learning with Applications | ||
| Biomedical Data Science | ||
| Computational Neuroscience | ||
| Developmental Neuroscience | ||
| Auditory and Visual Neural Systems | ||
| Regulatory Control of Biomedical Devices | ||
| Current Trends in Regenerative Medicine | ||
| Advanced Biomaterials | ||
| Microcomputer-Based Medical Instrumentation | ||
| Neuromotor Engineering | ||
| Introduction to Medical Robotics | ||
| Pattern Recognition and Neural Networks | ||
| Principles of Artificial Intelligence | ||
| Machine Learning | ||
| Data Mining | ||
| Introduction to Artificial Intelligence | ||
| Neural Networks and Deep Learning | ||
| Partial Differential Equations I | ||
| Partial Differential Equations II | ||
| Ordinary Differential Equations | ||
| Numerical Methods in Differential Equations | ||
| NEU II - Systems Neuroscience | ||
| Neuroanatomy | ||
| Principles of Membrane Physiology and Biophysics I | ||
| Project | 6 | |
| Special Problems | ||
Industry Project | ||
This can be a three-summer month or six-month (equivalent of 2 semesters) industry project. The project will culminate with a report detailing the work done and knowledge gained, and a presentation to faculty and students in the program. | ||
Capstone Project | ||
To complete the project, the student will have at least one supervisor within an appropriate academic unit in the program. Prior to initiating the thesis project, approvals from the academic advisor and BME department chair are required. | ||
| BME / Miami Project / NEU Seminars | 0 | |
| Students must attend at least 9 seminars in topics on neural engineering and neuroscience at the University. | ||
| Total Credit Hours | 150 | |
The MS program in Neural Engineering provide competency in one of the three areas:
- neurostimulation
- neurorehabilitation
- regenerative medicine
Curriculum setup:
Students admitted in the dual degree BS/MS program can take a maximum of six (6) graduate credits per semester in their senior year, for a maximum of twelve (12) graduate credits per year, without incurring additional costs if they are full-time undergraduate students during this period. Graduate technical electives taken in the senior year must be chosen with the approval of their academic advisor. The credits of graduate technical electives completed in the fourth year are counted toward the 30 credits required for the MS degree. In the fifth year, BS/MS students complete the rest of their 18 credits of graduate course requirements, including completion of the MS Project.
Pre-requisites:
Applicants for BS/MS may be enrolled in any undergraduate major. However, they will be expected to have taken and passed a course each (or equivalent training) in Statistics and Probability, and Programming.
Sample Plan of Study (5 Years)
BS in Computer Science/MS in Neural Engineering
| Freshman Year | ||
|---|---|---|
| Fall | Credit Hours | |
| Undergraduate Courses | 15 | |
| Credit Hours | 15 | |
| Spring | ||
| Undergraduate Courses | 15 | |
| Credit Hours | 15 | |
| Sophomore Year | ||
| Fall | ||
| Undergraduate Courses | 15 | |
| Credit Hours | 15 | |
| Spring | ||
| Undergraduate Courses | 15 | |
| Credit Hours | 15 | |
| Junior Year | ||
| Fall | ||
| Undergraduate Courses | 18 | |
| Credit Hours | 18 | |
| Spring | ||
| Undergraduate Courses | 18 | |
| Credit Hours | 18 | |
| Senior Year | ||
| Fall | ||
| Undergraduate Courses | 12 | |
| CSC 646 | Introduction to Machine Learning with Applications | 3 |
| BME 603 | Neurophysiology for Engineers | 3 |
| Credit Hours | 18 | |
| Spring | ||
| Undergraduate Courses | 12 | |
| CSC 650 | Computational Neuroscience | 3 |
| BME 615 | Current Trends in Neural Engineering | 3 |
| Credit Hours | 18 | |
| Fifth Year (Graduate) | ||
| Fall | ||
| BME 640 | Microcomputer-Based Medical Instrumentation | 3 |
| CSC 746 | Neural Networks and Deep Learning | 3 |
| BME 725 | Special Problems | 3 |
| Credit Hours | 9 | |
| Spring | ||
| BME 612 | Regulatory Control of Biomedical Devices | 3 |
| ECE 753 | Pattern Recognition and Neural Networks | 3 |
| BME 725 | Special Problems | 3 |
| Credit Hours | 9 | |
| Total Credit Hours | 150 | |
Sample Plan of Study (5 Years)
BS in Neuroscience/MS in Neural Engineering
| Freshman Year | ||
|---|---|---|
| Fall | Credit Hours | |
| Undergraduate Courses | 15 | |
| Credit Hours | 15 | |
| Spring | ||
| Undergraduate Courses | 15 | |
| Credit Hours | 15 | |
| Sophomore Year | ||
| Fall | ||
| Undergraduate Courses | 15 | |
| Credit Hours | 15 | |
| Spring | ||
| Undergraduate Courses | 15 | |
| Credit Hours | 15 | |
| Junior Year | ||
| Fall | ||
| Undergraduate Courses | 18 | |
| Credit Hours | 18 | |
| Spring | ||
| Undergraduate Courses | 18 | |
| Credit Hours | 18 | |
| Senior Year | ||
| Fall | ||
| Undergraduate Courses | 12 | |
| CSC 646 | Introduction to Machine Learning with Applications | 3 |
| BME 615 | Current Trends in Neural Engineering | 3 |
| Credit Hours | 18 | |
| Spring | ||
| Undergraduate Courses | 12 | |
| CSC 650 | Computational Neuroscience | 3 |
| BME 624 | Neuromotor Engineering | 3 |
| Credit Hours | 18 | |
| Fifth Year (Graduate) | ||
| Fall | ||
| CSC 746 | Neural Networks and Deep Learning | 3 |
| NEU 762 | NEU II - Systems Neuroscience | 4 |
| BME 725 | Special Problems | 3 |
| Credit Hours | 10 | |
| Spring | ||
| BME 612 | Regulatory Control of Biomedical Devices | 3 |
| BME 695 | Current Trends in Regenerative Medicine | 3 |
| BME 725 | Special Problems | 3 |
| Credit Hours | 9 | |
| Total Credit Hours | 151 | |
Sample Plan of Study (5 Years)
BS in Biomedical Engineering/MS in Neural Engineering
| Freshman Year | ||
|---|---|---|
| Fall | Credit Hours | |
| Undergraduate Courses | 15 | |
| Credit Hours | 15 | |
| Spring | ||
| Undergraduate Courses | 15 | |
| Credit Hours | 15 | |
| Sophomore Year | ||
| Fall | ||
| Undergraduate Courses | 15 | |
| Credit Hours | 15 | |
| Spring | ||
| Undergraduate Courses | 15 | |
| Credit Hours | 15 | |
| Junior Year | ||
| Fall | ||
| Undergraduate Courses | 18 | |
| Credit Hours | 18 | |
| Spring | ||
| Undergraduate Courses | 18 | |
| Credit Hours | 18 | |
| Senior Year | ||
| Fall | ||
| Undergraduate Courses | 12 | |
| BME 603 | Neurophysiology for Engineers | 3 |
| CSC 646 | Introduction to Machine Learning with Applications | 3 |
| Credit Hours | 18 | |
| Spring | ||
| Undergraduate Courses | 12 | |
| BME 612 | Regulatory Control of Biomedical Devices | 3 |
| BME 615 | Current Trends in Neural Engineering | 3 |
| Credit Hours | 18 | |
| Fifth Year (Graduate) | ||
| Fall | ||
| BME 640 | Microcomputer-Based Medical Instrumentation | 3 |
| CSC 746 | Neural Networks and Deep Learning | 3 |
| BME 725 | Special Problems | 3 |
| Credit Hours | 9 | |
| Spring | ||
| BME 635 | Advanced Biomaterials | 3 |
| PHS 741 | Principles of Membrane Physiology and Biophysics I | 2 |
| BME 725 | Special Problems | 3 |
| Additional Elective | 1 | |
| Credit Hours | 9 | |
| Total Credit Hours | 150 | |
