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: bmengp@umn.edu
Website: http://cne.umn.edu/
  • Program Type: Graduate free-standing minor
  • Requirements for this program are current for Spring 2022
  • Length of program in credits (doctoral): 12
  • This program does not require summer semesters for timely completion.
The doctoral 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 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
Special Application Requirements:
Students interested in the minor are strongly encouraged to confer with their major field advisor and director of graduate studies, and the Neuroengineering director of graduate studies regarding feasibility and requirements – including, for students pursuing PhDs in majors other than neuroscience, or in biomedical, electrical, or mechanical engineering, consultation with the Neuroengineering director of graduate studies about the necessary background to succeed in the minor.
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.
Coursework must be approved by the neuroengineering director of graduate studies. 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. Coursework applied to minor field requirements offered on both the A-F and S/N grading basis must be graded A-F, with a minimum grade of B- earned for each course. The minimum cumulative GPA for the minor is 3.00.
Neuroengineering Seminar (2 credits)
Take the following course:
BMEN 8411 - Neuroengineering Seminar (2.0 cr)
Neuroengineering Course (3 credits)
Select 1 of the following in consultation with the Neuroengineering director of graduate studies. BMEN 5411 is strongly recommended for students who have not already completed a neural engineering course.
BMEN 5411 - Neural Engineering (3.0 cr)
BMEN 5412 - Neuromodulation (3.0 cr)
Neuroscience Course (3 credits)
Select 1 of the following courses in consultation with the Neuroengineering director of graduate studies:
NSCI 5101 - Neurobiology I: Molecules, Cells, and Systems (3.0 cr)
NSC 5561 - Systems Neuroscience (4.0 cr)
Electives
Select electives as needed, in consultation with the Neuroengineering director of graduate studies, to complete the 12-credit minimum. Other courses may be selected with approval of the Neuroengineering director of graduate studies.
BMEN 5401 - Advanced Biomedical Imaging (3.0 cr)
BMEN 5411 - Neural Engineering (3.0 cr)
BMEN 5412 - Neuromodulation (3.0 cr)
BMEN 5413 - Neural Decoding and Interfacing (3.0 cr)
BMEN 8101 - Biomedical Digital Signal Processing (3.0 cr)
BMEN 8151 - Biomedical Electronics and Implantable Microsystems (3.0 cr)
BMEN 8411 - Neuroengineering Seminar (2.0 cr)
BMEN 8502 - Physiological Control Systems (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)
MPHY 5178 - Physical Principles of Magnetic Resonance Imaging (3.0 cr)
MPHY 8147 - Advanced Physics of Magnetic Resonance Imaging (MRI) (3.0 cr)
NSC 8111 - Quantitative Neuroscience (3.0 cr)
NSC 8217 - Systems and Computational Neuroscience (2.0 cr)
PSY 5036W - Computational Vision [WI] (3.0 cr)
PSY 5038W - Introduction to Neural Networks [WI] (3.0 cr)
PSY 5063 - Introduction to Functional MRI (3.0 cr)
PSY 5065 - Functional Imaging: Hands-on Training (3.0 cr)
Program Sub-plans
Students are required to complete one of the following sub-plans.
Students may not complete the program with more than one sub-plan.
Doctoral
  
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· College of Science and Engineering

View future requirement(s):
· Fall 2022

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BMEN 8411 - Neuroengineering Seminar
Credits: 2.0 [max 4.0]
Grading Basis: S-N only
Typically offered: Every Fall & Spring
Lectures presented by researchers in the field of neuroengineering. Students will discuss speaker papers in advance of the talks and meet with presenters afterwards. Each student will also deliver one seminar presentation per semester.
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.
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 5401 - Advanced Biomedical Imaging
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Functional biomedical imaging modalities. Principles/applications of technologies that offer high spatial/temporal resolution. Bioelectromagnetic and magnetic resonance imaging. Other modalities. prereq: CSE upper div or 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 8101 - Biomedical Digital Signal Processing
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Signal processing theory for analyzing real world digital signals. Digital signal processing and mathematically derived algorithms for analysis of stochastic signals. Spectral analyses, noise cancellation, optimal filtering, blind source separation, beamforming techniques. prereq: [[MATH 2243 or MATH 2373], [MATH 2263 or MATH 2374]] or equiv
BMEN 8151 - Biomedical Electronics and Implantable Microsystems
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
This class is about bioelectronics and the synergy between electronics and biomedical applications. It discusses how to architect robust ultra-low-power electronics with applications in implantable, noninvasive, wireless, sensing, and stimulating biomedical systems. Half of the classes span feedback systems, transistor device physics, noise, and circuit-analysis techniques to provide a circuit-foundation. The other half are research papers that describe the utilization of these circuits in implantable and wearable systems. Some of these systems include cochlear implants for the deaf, brain implants for the blind and paralyzed, cardiac devices for noninvasive medical monitoring, and biomolecular sensing systems. Prerequisites: BMEn 5101 or equivalent background in bioinstrumentation and electric circuits.
BMEN 8411 - Neuroengineering Seminar
Credits: 2.0 [max 4.0]
Grading Basis: S-N only
Typically offered: Every Fall & Spring
Lectures presented by researchers in the field of neuroengineering. Students will discuss speaker papers in advance of the talks and meet with presenters afterwards. Each student will also deliver one seminar presentation per semester.
BMEN 8502 - Physiological Control Systems
Credits: 3.0 [max 3.0]
Prerequisites: 8101 or equiv
Grading Basis: A-F only
Typically offered: Every Spring
Simulation, identification, and optimization of physiological control systems. Linear and non-linear systems analysis, stability analysis, system identification, and control design strategies, including constrained, adaptive, and intelligent control. Analysis and control of physiological system dynamics in normal and diseased states. prereq: 8101 or equiv
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]
MPHY 5178 - Physical Principles of Magnetic Resonance Imaging
Credits: 3.0 [max 3.0]
Typically offered: Spring Even Year
Magnetic resonance imaging physics, spatial selection and encoding, imaging hardware and system engineering. Imaging sequences, signal-to-noise, and contrast.
MPHY 8147 - Advanced Physics of Magnetic Resonance Imaging (MRI)
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
NMR (nuclear magnetic resonance) and MRI physics, spatial selection and encoding, imaging hardware and system engineering. Imaging sequences, associated contrast/resolution. Recent developments in MRI. prereq: 5174 or instr consent
NSC 8111 - Quantitative Neuroscience
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Principles of experimental design and statistical analysis in neuroscience research. Includes an introduction to computer programming for data analysis using both classic and modern quantitative methods.
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
PSY 5036W - Computational Vision (WI)
Credits: 3.0 [max 3.0]
Typically offered: Fall Even Year
Applications of psychology, neuroscience, computer science to design principles underlying visual perception, visual cognition, action. Compares biological/physical processing of images with respect to image formation, perceptual organization, object perception, recognition, navigation, motor control. prereq: [[3031 or 3051], [Math 1272 or equiv]] or instr consent
PSY 5038W - Introduction to Neural Networks (WI)
Credits: 3.0 [max 3.0]
Typically offered: Fall Odd Year
Parallel distributed processing models in neural/cognitive science. Linear models, Hebbian rules, self-organization, non-linear networks, optimization, representation of information. Applications to sensory processing, perception, learning, memory. prereq: [[3061 or NSC 3102], [MATH 1282 or 2243]] or instr consent
PSY 5063 - Introduction to Functional MRI
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall
How to understand and perform a brain imaging experiment. Theory and practice of functional MRI experimental design, execution, and data analysis. Students develop experimental materials/acquire and analyze their own functional MRI data. Lectures/lab exercises. prereq: Jr or sr or grad or instr consent
PSY 5065 - Functional Imaging: Hands-on Training
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Basic neuroimaging techniques/functional magnetic resonance imaging (fMRI). First half of semester covers basic physical principles. Second half students design/execute fMRI experiment on Siemens 3 Tesla scanner. prereq: [3801 or equiv], [3061 or NSCI 3101], instr consent