BME 6360 – Neural Engineering page 1 of 3

BME 6360 – Neural Engineering
page 1 of 3
BME 6360 – Neural Engineering
Spring 2010 Location: 350 NEB
Catalog Description:
Neural Engineering represents the application of Engineering to neuroscience including
such diverse areas as neural tissue engineering, models of neural function, and neural
interface technology. This course will focus on these areas primarily in the context of
neural interfaces/prosthetics beginning with basic neural physiology and models of neural
mechanisms to the advanced neural interfaces currently being developed and or produced
commercially by the field. Credits:03
Prerequisites:
Graduate students only. Programming experience in Matlab will be needed to
complete projects in this course. Some signal processing experience will be helpful as
well. This course will be using Octave (an open source implementation of Matlab)
available for both Mac and Windows machines at http://www.octave.org.
Image of InVivo
Wire Electrode
Array in Monkey
cortex from
Nicolelis's website
Professor:
Office Hours:
Dr. Thomas DeMarse
Thursdays for 1 hour after class
Department of Biomedical Engineering
in my office Rm J385.
Biomedical Engineering Building Rm 385
TA: TBA
Ph: 352-373-9327 Mobile: 404-384-7707
Email: [email protected]
Website: (Course schedule, assignments, readings and additional information).
http://cortex.bme.ufl.edu/~neuroeng/
Text:
Required Text:
•Willisch, Lusignan, Benayoun, Baker, Dickey, Hatsopoulos (2009). MATLAB for Neuroscientists: An
Introduction to Scientific Computing in MATLAB. Academic Press. ISBN 978-0-12-374551-4
All other readings and course material will be provided online. Optional material:
•Van Drongelen (2007). Signal Processing for Neuroscientists. Academic Press. (Available on Amazon).
(A very informative and more advanced text for any neuroscientist/neural engineering student with
Matlab examples).
Course Objectives:
The overall objective for this course is to introduce students to the field of neural engineering. Neural
engineering is an emerging field that combines neurobiology, neuroscience, biomedical engineering, electronics,
and instrumentation. This course will consist of a combination of lectures and guided problem sets. Each set is
focused on specific areas within the field of neural engineering including some of the most common neural
interfaces and illustrated within the context of neural prostheses. At the end of the course the student should
have mastered basic skills within neural engineering including:
•Basic neurobiology and modeling of the action potential
•Structure and function of the nervous system
•Principles of data acquisition
•Analytical measures of neural data (e.g., Spike train statistics, Cross-Correlation, Spike Sorting)
•Common neural interfaces including Intracellular (eg patch or whole cell recording), Extracellular
recordings including in vivo microwire and in vitro microelectrode arrays, and EEG.
BME 6360 – Neural Engineering
page 2 of 3
Goals:
At the end of the course, students should be knowledgeable in the following areas:
1.Basic Neuro-electrophysiology including the demonstrated ability to record and analyze neural activity.
2.Familiar with advanced neural interface technologies and techniques.
3.Be able to conduct rudimentary analysis of neural activity using some standard measures.
4.Employ software tools (i.e. Octave/ MATLAB) to support the acquisition/modeling/analysis scientific
paradigm.
Grading:
• 50% of your grade will be based on the Homework.
• 25% Midterm Exam
• 25% Final Exam
Late Homework Policy:
Grades for late homeworks be reduced 10% per each day it is late.
Topic
Basic neural anatomy of the Brain, CNS, PNS
Neurons, Ion Basis of the Action Potential, Cable Properties and Biophysical Compartmental
Neural Modeling.
Homework 1: Introduction to Matlab
Homework 2: Neural Modeling Using the Neuron Simulation Package
Fundamentals of Data Acquisition including A/D sampling techniques, nyquist frequency, etc
Homework 3: The Data Acquisition Chain. Noise, and Digital Filtering Basics
Brain-machine (computer) interfaces (BCI) based on EEG measures.
Homework 4: Signal Processing and Frequency Analysis of EEG signals
Homework 5: Hands-On EOG Data Acquistion using the Tucker Davis System (TDT)
In vitro microelectrode array neural interfaces
Homework 6: Poisson Distribution, Spike detection, PSTH, Cross-Correlation, Principle
Components, Spike Sorting, template matching, k-means clustering.
Midterm Exam
Cochlear implants and signal processing for functional restoration of hearing
Homework 7: Building a simulated Cochlear Implant
Neural prosthetics/Robotic/computer control- using in vivo multielectrode array technology.
Homework 8: Modeling Neural Motor Control-Georgopolis Vector
Multi-unit recording in retina- In vitro retinal interface and in vivo Retinal Prosthetics
Final Exam- Written Final Team Project to Build an EEG based BCI
Academic Honesty:
In adopting this Honor Code, the students of the University of Florida recognize that academic honesty and integrity are
fundamental values of the University community. Students who enroll at the University commit to holding themselves and
their peers to the high standard of honor required by the Honor Code. Any individual who becomes aware of a violation of
the Honor Code is bound by honor to take corrective action. A student-run Honor Court and faculty support are crucial to the
success of the Honor Code. The quality of a University of Florida education is dependent upon the community acceptance
and enforcement of the Honor Code. We, the members of the University of Florida community, pledge to hold ourselves and
our peers to the highest standards of honesty and integrity. On all work submitted for credit by students at the University of
Florida, the following pledge is either required or implied: On my honor, I have neither given nor received unauthorized aid
in doing this assignment
BME 6360 – Neural Engineering
page 3 of 3
Students with Disabilities:
Students requesting classroom accommodation must first register with the Dean of Students Office. The Dean of Students Office will
provide documentation to the student who must then provide this documentation to the Instructor when requesting accommodation.