Twin Cities campus

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Twin Cities Campus

Neuroengineering Minor

Department of Biomedical Engineering
College of Science and Engineering
Link to a list of faculty for this program.
Contact Information
Graduate Minor in Neuroengineering, 7-105 Nils Hasselmo Hall, 312 Church Street S.E., Minneapolis, MN 55455 (612-624-8396; fax 612-626-6583)
Email: igert-ne@umn.edu
Website: http://neuroengineering.umn.edu/
  • Program Type: Graduate free-standing minor
  • Requirements for this program are current for Fall 2014
  • Length of program in credits (doctoral): 12
  • This program does not require summer semesters for timely completion.
The Graduate Minor in Neuroengineering (NE) is motivated by the notion that future breakthroughs in this rapidly-growing area of research will be made by engineers who understand the fundamental issues and principles of neuroscience that occur during neural interventions, and by neuroscientists who are truly competent in engineering concepts and tools. The minor trains doctoral students to develop the skills to revolutionize technologies for interfacing with the brain and to advance our understanding of the neuroscience processes that arise when we interface with and modulate the brain.
Program Delivery
  • via classroom (the majority of instruction is face-to-face)
Prerequisites for Admission
Other requirements to be completed before admission:
Enrollment in the Neuroengineering Minor is open to all currently enrolled Ph.D. students who have the necessary science background to complete the coursework and who are in good standing in their major program.
Special Application Requirements:
Ph.D. students majoring in programs other than Biomedical Engineering, Electrical Engineering, Mechanical Engineering, and Neuroscience must have approval from the Neuroengineering Director of Graduate Studies (DGS) to participate in the minor program. Students will declare the minor by including it on the Graduate Degree Plan, following consultation with the DGS. Students must officially declare the minor before taking the Oral Preliminary Examination (OPE).
For an online application or for more information about graduate education admissions, see the General Information section of this website.
Program Requirements
Use of 4xxx courses towards program requirements is not permitted.
The minor course selection must be approved by the Neuroengineering Director of Graduate Studies (DGS) who can be found here: http://neuroengineering.umn.edu/faculty.html. For any course listed in multiple categories, students must choose which requirement that course will fulfill. A single course cannot be counted simultaneously toward multiple categories. Students may not use any of their minor courses to satisfy the core course requirements for their major program (i.e., a Neuroscience student cannot count NSCI 5101 as both a Neuroengineering Minor course and a core Neuroscience course).
Introduction Course
Take 1 or more course(s) from the following:
· BMEN 5411 - Neural Engineering (3.0 cr)
· NSCI 5101 - Neurobiology I: Molecules, Cells, and Systems (3.0 cr)
· NSC 5561 - Systems Neuroscience (4.0 cr)
Neuroengineering Core Courses
At least one course from the following must be taken: BMEN 5412, BMEN 5413.
Take 2 or more course(s) from the following:
· BMEN 5411 - Neural Engineering (3.0 cr)
· BMEN 5412 - Neuromodulation (3.0 cr)
· BMEN 5413 - Neural Decoding and Interfacing (3.0 cr)
· BMEN 8335 - Neuroengineering Practicum (3.0 cr)
Elective Course
One additional course from either an engineering or neuroscience discipline. The following is not an exhaustive list but merely a representative sample of courses that would be appropriate to satisfy this requirement. Additional courses may be approved as electives by the Neuroengineering DGS.
Take 1 or more course(s) from the following:
· BMEN 5411 - Neural Engineering (3.0 cr)
· BMEN 5412 - Neuromodulation (3.0 cr)
· BMEN 5413 - Neural Decoding and Interfacing (3.0 cr)
· BMEN 8335 - Neuroengineering Practicum (3.0 cr)
· EE 5231 - Linear Systems and Control (3.0 cr)
· EE 5239 - Introduction to Nonlinear Optimization (3.0 cr)
· EE 5542 - Adaptive Digital Signal Processing (3.0 cr)
· ME 5281 - Feedback Control Systems (4.0 cr)
· ME 5286 - Robotics (4.0 cr)
· NSC 5202 - Theoretical Neuroscience: Systems and Information Processing (3.0 cr)
· NSC 8217 - Systems and Computational Neuroscience (2.0 cr)
 
More program views..
View college catalog(s):
· College of Science and Engineering

View future requirement(s):
· Fall 2022
· Spring 2021
· Fall 2020
· Fall 2018
· Fall 2016
· Fall 2015

View sample plan(s):
· Biomedical Engineering Ph.D. Students
· Electrical Engineering/Mechanical Engineering/Other Ph.D. Students
· Neuroscience Ph.D. Students
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BMEN 5411 - Neural Engineering
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Theoretical basis. Signal processing techniques. Modeling of nervous system, its response to stimulation. Electrode design, neural modeling, cochlear implants, deep brain stimulation. Prosthetic limbs, micturition control, prosthetic vision. Brain machine interface, seizure prediction, optical imaging of nervous system, place cell recordings in hippocampus. prereq: 3401 recommended
NSCI 5101 - Neurobiology I: Molecules, Cells, and Systems
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
This course discusses the basic principles of cellular and molecular neurobiology and nervous systems. The main topics include: Organization of simple networks, neural systems and behavior; how the brain develops and the physiology and communication of neurons and glia; the molecular and genetic basis of cell organization; ion channel structure and function; the molecular basis of synaptic receptors; transduction mechanisms and second messengers; intracellular regulation of calcium; neurotransmitter systems, including excitation and inhibition, neuromodulation, system regulation and the cellular basis of learning, memory and cognition. The course is intended for students majoring in neuroscience, but is open to all students with the required prerequisites.
NSC 5561 - Systems Neuroscience
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Principles of organization of neural systems forming the basis for sensation/movement. Sensory-motor/neural-endocrine integration. Relationships between structure and function in nervous system. Team taught. Lecture, laboratory. prereq: NSc grad student or instr consent
BMEN 5411 - Neural Engineering
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Theoretical basis. Signal processing techniques. Modeling of nervous system, its response to stimulation. Electrode design, neural modeling, cochlear implants, deep brain stimulation. Prosthetic limbs, micturition control, prosthetic vision. Brain machine interface, seizure prediction, optical imaging of nervous system, place cell recordings in hippocampus. prereq: 3401 recommended
BMEN 5412 - Neuromodulation
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Fundamentals of bioengineering approaches to modulate the nervous system, including bioelectricity, biomagnetism, and optogenetics. Computational modeling, design, and physiological mechanisms of neuromodulation technologies. Clinical exposure to managing neurological disorders with neuromodulation technology.
BMEN 5413 - Neural Decoding and Interfacing
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Neural interface technologies currently in use in patients as well as the biophysical, neural coding, and hardware features relating to their implementation in humans. Practical and ethical considerations for implanting these devices into humans. prereq: CSE upper division student, CSE graduate student, or instructor approval. recommended: BMEn 3411
BMEN 8335 - Neuroengineering Practicum
Credits: 3.0 [max 6.0]
Grading Basis: A-F only
Typically offered: Every Spring
Topics/issues in neuroengineering. Ethics, professional conduct, conflicts, plagiarism, copyright, authorship, research design considerations, IRB, intellectual properties, review process, professional presentations, proposal writing. prereq: PhD student in BMEn, EE, ME, or NSci or instr consent
BMEN 5411 - Neural Engineering
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Theoretical basis. Signal processing techniques. Modeling of nervous system, its response to stimulation. Electrode design, neural modeling, cochlear implants, deep brain stimulation. Prosthetic limbs, micturition control, prosthetic vision. Brain machine interface, seizure prediction, optical imaging of nervous system, place cell recordings in hippocampus. prereq: 3401 recommended
BMEN 5412 - Neuromodulation
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Fundamentals of bioengineering approaches to modulate the nervous system, including bioelectricity, biomagnetism, and optogenetics. Computational modeling, design, and physiological mechanisms of neuromodulation technologies. Clinical exposure to managing neurological disorders with neuromodulation technology.
BMEN 5413 - Neural Decoding and Interfacing
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Neural interface technologies currently in use in patients as well as the biophysical, neural coding, and hardware features relating to their implementation in humans. Practical and ethical considerations for implanting these devices into humans. prereq: CSE upper division student, CSE graduate student, or instructor approval. recommended: BMEn 3411
BMEN 8335 - Neuroengineering Practicum
Credits: 3.0 [max 6.0]
Grading Basis: A-F only
Typically offered: Every Spring
Topics/issues in neuroengineering. Ethics, professional conduct, conflicts, plagiarism, copyright, authorship, research design considerations, IRB, intellectual properties, review process, professional presentations, proposal writing. prereq: PhD student in BMEn, EE, ME, or NSci or instr consent
EE 5231 - Linear Systems and Control
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
The course studies finite-dimensional linear systems in continuous and discrete time. Such systems are described by ordinary differential and difference equations. Input-output and state-space descriptions are provided and analyzed. Introductory methods for controlling such systems are developed. prereq: [3015, CSE grad student] or instr consent
EE 5239 - Introduction to Nonlinear Optimization
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Nonlinear optimization. Analytical/computational methods. Constrained optimization methods. Convex analysis, Lagrangian relaxation, non-differentiable optimization, applications in integer programming. Optimality conditions, Lagrange multiplier theory, duality theory. Control, communications, management science applications. prereq: [3025, Math 2373, Math 2374, CSE grad student] or dept consent
EE 5542 - Adaptive Digital Signal Processing
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Design, application, and implementation of optimum/adaptive discrete-time FIR/IIR filters. Wiener, Kalman, and Least-Squares. Linear prediction. Lattice structure. LMS, RLS, and Levinson-Durbin algorithms. Channel equalization, system identification, biomedical/sensor array processing, spectrum estimation. Noise cancellation applications. prereq: [4541, 5531, CSE grad student] or dept consent
ME 5281 - Feedback Control Systems
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Continuous and discrete time feedback control systems. Frequency response, stability, poles and zeros; transient responses; Nyquist and Bode diagrams; root locus; lead-lag and PID compensators, Nichols-Ziegler design method. State-space modeling/control. Digital implementation. Computer-aided design and analysis of control systems. prereq: 3281
ME 5286 - Robotics
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
The course deals with two major components: robot manipulators (more commonly known as the robot arm) and image processing. Lecture topics covered under robot manipulators include their forward and inverse kinematics, the mathematics of homogeneous transformations and coordinate frames, the Jacobian and velocity control, task programming, computational issues related to robot control, determining path trajectories, reaction forces, manipulator dynamics and control. Topics under computer vision include: image sensors, digitization, preprocessing, thresholding, edge detection, segmentation, feature extraction, and classification techniques. A weekly 2 hr. laboratory lasting for 8-9 weeks, will provide students with practical experience using and programming robots; students will work in pairs and perform a series of experiments using a collaborative robot. prereq: [3281 or equiv], [upper div ME or AEM or CSci or grad student]
NSC 5202 - Theoretical Neuroscience: Systems and Information Processing
Credits: 3.0 [max 3.0]
Course Equivalencies: NSc 5202/Phsl 5202
Typically offered: Every Spring
Concepts of computational/theoretical neuroscience. Distributed representations and information theory. Methods for single-cell modeling, including compartmental/integrate-and-fire models. Learning rules, including supervised, unsupervised, and reinforcement learning models. Specific systems models from current theoretical neuroscience literature. Lecture/discussion. Readings from current scientific literature. prereq: [3101, 3102W] recommended
NSC 8217 - Systems and Computational Neuroscience
Credits: 2.0 [max 2.0]
Grading Basis: S-N or Aud
Typically offered: Every Fall & Spring
Advanced seminar. prereq: 5561 or instr consent