2006-2510: A NEURAL ENGINEERING TRACK WITHIN BIOENGINEERING:LECTURE AND LAB COURSES

David Schneeweis, University of Illinois-Chicago

J Hetling, University of Illinois-Chicago

Patrick Rousche, University of Illinois-Chicago

© American Society for Engineering Education, 2006

Page 11.77.1

A NEURAL ENGINEERING TRACK WITHIN BIOENGINEERING:

LECTURE AND LAB COURSES

Neural engineering as a distinct specialty within bioengineering

Neural engineering (also called neuroengineering) has recently been identified as an

emerging field of specialization within the broader field of biomedical engineering, or

bioengineering. (The terms “biomedical engineering” and “bioengineering” are virtually

synonymous in most contexts, so “bioengineering” will be used in this article for simplicity.)

Neural engineers self-identify as engineers/scientists interested in engineering challenges

related to the brain and nervous system. It has been referred to as a “merger of engineering

and neuroscience” [1]. Many neural engineers work on clinically oriented challenges,

including for example developing sensory prostheses for the deaf and blind or designing

systems to stimulate walking motion in the legs of spinal chord injury patients. But other

neural engineers are interested primarily in understanding how the brain and nervous system

work, or are affected by disease.

Although engineers and scientists have been doing this kind of work for decades, it is only

within the last decade or so that neural engineering has become recognized as a named sub-

specialty. Indeed it has only been recently that “neural engineering” has existed as a distinct

subject track at the annual meeting of the Biomedical Engineering Society. But the field is

rapidly growing as witnessed by the establishment of the Journal of Neural Engineering in

2004, and the holding of the 1st International IEEE EMBS Conference on Neural

Engineering in 2003. Judging by the number of open faculty positions advertised for neural

engineers, it would appear that representation of neural engineers on engineering faculties is

increasing concomitantly.

This article will focus primarily on the neural engineering undergraduate curriculum

developed in the BioEngineering Department at the authors’ institution, the University of

Illinois at Chicago (UIC). Special emphasis will be placed on the laboratory component,

since this is in certain ways the most important, yet the most challenging.

Training neural engineers

Many undergraduate bioengineering programs require students to select an area in which to

focus their coursework during their latter undergraduate years. This so-called “tracking” is

meant to give students some depth within the very broad bioengineering field. It has been

argued that depth helps students to compete more successfully for jobs, but exploring a

subject area in depth is also a beneficial intellectual exercise in its own right.

It is difficult to determine how many bioengineering programs now include neural

engineering among their track options, but a search through the Whitaker Foundation

Biomedical Engineering Curriculum Database [2]– a repository for course and curricular

information in bioengineering– returns 187 courses having “neural” in the title. (According

to the web site, the database includes information for “more than 100 academic institutions”

[2].) In our own BioEngineering Department at UIC, neural engineering is one of several

tracks in which undergraduates may focus their studies. Over the past three years

approximately 40% of graduating seniors selected neural engineering for their track.

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Students concentrating in neural engineering begin their track by taking two foundational

neuroscience courses offered by the Biological Sciences Department. These courses, BioS

286:Biology of the Brain and BioS 484:Neuroscience I provide much of the core content

essential for understanding and working with the nervous system. The core of the neural

engineering track consists of three neural engineering courses taught by BioE faculty

(Fig. 1). BioE 472:Models of the Nervous System is a quantitative neurobiology course

focusing on fairly classical topics in the domains of membrane physiology, signaling in

excitable cells, and synaptic communication. BioE:475:Neural Engineering 1 (NE1) is a

seminar style course where students explore current issues in neural engineering by critically

discussing journal articles. BioE 476:Neural Engineering Lab (NE Lab) is a hands-on

experience where students get exposed to current research techniques (See below). NE1

together with NE Lab constitute the capstone courses of the undergraduate neural

engineering track.

Elective courses

Undergraduates in BioEngineering at UIC are required to take twelve hours of elective

courses. Figure 1 graphically depicts the relationship between the neural engineering track

courses and the electives that students select. Electives closer to the track are more

commonly selected than courses further from the track. Figure 1 is based on anecdotal

evidence, and meant to depict qualitative relationships only.

Although BioEngineering courses constitute the major fraction of elective courses taken by

students in the neural engineering track, courses from other departments, and even other

colleges, are not unpopular. Organic chemistry and biochemistry classes are extremely

popular, in part because they are required or recommended by many medical school

programs. Approximately one-third of UIC BioEngineering undergraduates are premed.

Figure 1: Neural Engineering course track at UIC

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A neural engineering laboratory course

The greatest challenge of the neural engineering curriculum is providing hands on training in

the modern techniques used by neural engineers. This challenge is formidable for several

reasons. First, the intellectual domain of neural engineering spans several traditional

curricula (i.e. engineering, neurobiology, materials science), making the scope of the labs

very broad. Second, the methods of the neural engineer are often technically challenging and

complex, making it difficult for students to gain sufficient competence in the timeframe of

typical labs. Finally, the equipment needed for neural engineering labs can be costly and not

generally available in an undergraduate learning environment. A search of the Whitaker

Foundation’s Biomedical Engineering Curricular Database, with the term “neural laboratory”

returns 27 courses from 16 distinct institutions [2]. But a closer examination of the course

descriptions reveals that only about a half dozen of the courses include a substantial emphasis

on what would be considered cutting edge neural engineering research techniques.

The NE Lab course (BioE 476) at UIC was developed with the following objectives:

! Students should receive practical hands-on training in techniques used in basic and

applications oriented neural engineering research

! Students should have the opportunity to interact with the nervous system at

different scales (i.e. molecular, cellular, system levels) using in vivo and in vitro

techniques

! Students should become aware of the unique challenges in developing hybrid

technology

! Students should have opportunities to test hypotheses, and design solutions to posed

challenges

The objectives of this laboratory course have been addressed using a format that combines

activities in a teaching laboratory with activities in faculty research labs. Initial funding for

the teaching lab came from an NSF CCLI grant awarded to establish a facility that would be

jointly used by BioEngineering and Biological Science students interested in neuroscience.

(Unfortunately the aim of having a lab jointly populated by BioEngineering and Biological

Sciences students never materialized.)

The NE Lab course in its current form was offered in spring of 2005 to 4 students. Three

neural engineering faculty divided responsibility for running the labs, and one teaching

assistant (TA) helped out. Students earned two credit hours for the course, which was

scheduled to meet for two hours per week, but often ran over. Assessment was based on

performance in lab, and homework.

The NE Lab consists of six distinct lab modules lasting between one and three weeks. Four

of these modules (see Table 2) are well developed and described in detail in the following

section. The remaining two modules are briefly described in a subsequent section.

Page 11.77.4

Table 1. Four Well Developed Lab Modules of the NE Lab Course

1. In vivo neural interfaces: Non-invasive recording of the electroretinogram (3 wks)

2. Bioelectrodes: Fabrication and characterization (3 wks)

3. In vivo neural interfaces: Cortical recording using implanted electrode arrays (2 wks)

4. Modeling of hybrid systems: Simulation of responses of retinal neurons to

extracellular electric fields (1 wk)

Lab modules #1-4 of the NE Lab course

Lab modules #1-4 are the most developed of the six modules, and are described in some

detail in this section.

Lab #1: In vivo neural interfaces: Non-invasive recording of the electroretinogram (ERG)

The first lab module involves the recording of the light-evoked electroretinogram (ERG)

from rats. This activity takes place in the research lab of one of the authors, and is intended

to provide students the opportunity to practice fundamental skills common to any experiment

involving the study of evoked sensory responses from animals. Skills such as data

acquisition, analysis and interpretation are emphasized (Fig. 2). Students assist in handling

the animals, and get an appreciation for the special challenges associated with animal

experiments. Specific objectives and activities are listed in Table 2.

Table 2. Lab #1: In vivo neural interfaces: ERG

Objectives

! Appreciate challenges inherent in in vivo animal experiments (anesthesia, small

signals, noise)

! Understand instrumentation required (electrodes, amplifiers, filters)

! Properly acquire ERG signals (set filter cutoffs, gain, sampling rate)

! Perform basic processing of raw data in order to extract useful parameters

! Fit ERG data to a model

Activities

Week #1: Background

! Anatomy and physiology of retina

! Origin of ERG

Week #2: ERG Recordings from rat

! Handling of animals (anesthesia, electrode placement)

! Hardware instrumentation (amplifiers, filters, flash stimulators)

! Software (data acq., sampling, protocols, data handling)

! Troubleshooting

Week #3: Analysis

! Averaging

! Measuring parameters

! Plotting results

! Modeling

Page 11.77.5

Figure 2: Lab #1 student work: Raw light-flash-evoked ERG records (left) and offline

analysis

Lab #2: Bioelectrodes: Fabrication and characterization

A substantial number of neural engineers use metal electrodes to either record

neurophysiological signals or stimulate nerve or muscle cells. Understanding the properties

of electrodes, and especially the electrode-tissue interface, is thus extremely important for

neural engineering students. This lab module focuses on the techniques neural engineers use

to characterize electrodes. Students perform impedance spectroscopy and cyclic voltametry

using specialized equipment in the research lab of one of the authors (Fig. 3). Students learn

to interpret their results in the context of electrical circuit models of the interface. Table 3

lists the specific objectives and activities for this lab module.

Table 3. Lab #2: Bioelectrodes: Fabrication and characterization

Objectives

! Understand basic electrochemical phenomena occurring at the electrode/electrolyte

interface

! Understand importance of interface reactions for neural engineering devices

! Be able to characterize electrodes using electrode impedance spectroscopy (EIS)

and cyclic voltametry (CV)

! Observe how physical properties of electrodes (e.g. materials, geometry, surface

coating) affect the EIS and CV

Activities

Week #1: Background/Lab

! Background on electrochemistry fundamentals and EIS

! Students measure EIS on different materials and geometries

Week #2: Background/Lab

! Background on cyclic voltametry

! Students measure CVs of different materials

-0.4

-0.2

0.0

0.2

10008006004002000

Raw Data

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! Students deposit iridium oxide on gold and measure CVs

Week #3: Modeling/Analysis

! Students explore circuit models of electrode/electrolyte interface using pSpice

simulation

Figure 3: Lab #2 student work: Impedance spectrograms (top left), cyclic voltamograms

(bottom left) and analysis of charge transfer

Lab #3: In vivo neural interfaces: Cortical recording using implanted electrode arrays

As a follow up to the electrode characterization activities of the second lab module, students

next experience using a microelectrode array to record action potentials (i.e. spikes) from

single neurons in rat cortex. Since the surgery associated with microelectrode array

implantation is extremely complex, students mostly observe that part of the experiment.

Students are, however, expected to understand the instrumentation and equipment, and most

importantly, how the data obtained (i.e. spike trains) are analyzed (Fig. 4). This lab is

conducted in the research laboratory of one of the authors. Objectives and activities of this

lab are listed in Table 4.

Charge Transfer Characteristics

-0.05

-0.04

-0.03

-0.02

-0.01

0

0 100 200 300 400 500 600 700 800 900 1000

Pulses

Charg

e S

tora

ge C

apacity (

mC

/cm

^2)

Impedance Spectrograms

Cyclic Voltamograms

Charge-Transfer

Page 11.77.7

Table 4. Lab #3: In vivo neural interfaces: Cortical recording

Objectives

! Understand hardware and software requirements unique to spike train recording

! Appreciate challenges inherent to cortical implant prosthesis strategies

! Be able to do basic spike train analysis

! Be able to interpret results of basic auditory tuning curve experiments

Activities

Week #1: Background

! Review of hardware and software for acquiring action potentials

! Introduction to basic analysis methods

Week #2: Background/Lab

! Students observe implantation of a multielectrode array into auditory cortex

! Responses to auditory stimuli obtained

Week #3: Modeling/Analysis

! Analysis/interpretation of spike train data

Spike Sorting

Event01 dsp003 dsp007

0 0.012042 0.012042

0.499999 0.512246 0.047514

0.999997 0.634675 0.435118

1.499996 0.886374 0.512246

1.999995 0.965059 0.706519

2.499994 0.998482 0.886784

2.999992 1.055212 0.965304

3.499991 1.202545 1.012531

3.99999 1.241702 1.259274

4.499988 1.512325 1.512325

4.999987 1.577288 1.896079

5.499986 1.896038 2.250793

5.999985 2.251612 2.618655

6.499983 2.50794 2.715156

6.999982 2.618655 2.848604

7.499981 2.715484 2.943304

7.99998 2.881413 3.012116

8.499978 3.012198 3.018301

8.999977 3.018301 3.511992

9.499976 3.511992 3.663094

9.999974 3.520553 3.80375

10.499973 3.663053 3.987456

Time Stamps

PSTHs

TONE

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Figure 4: Lab #3 student work: Spike trains were obtained from a multielectrode array, and

spikes from individual cells were sorted (top). Spike data was used to generate post-

stimulus-time histograms (PSTHs, middle) which could be used for quantifying neural

activity.

Lab #4: Modeling of hybrid systems (1 wk)

Neural engineers typically deal with hybrid systems consisting of a biological component

(e.g. tissue, organs, cells), and a synthetic component (e.g. metal electrodes, polymer

scaffolds). Lab module #4 is a computer-based lab intended to provide students experience

with the kind of modeling required for an understanding of how these components interact.

Students learn the basics of NEURON, a popular program for modeling the biophysical

properties of neurons, and through simulation explore spatial and geometric factors that are

important for determining how efficiently neurons are excited by stimulating electrodes. An

appreciation for these concepts is essential to designing successful hybrid systems. Because

of the limited time available for learning the software, a TA created a friendly “front end” to

the software that expedited the simulations. The specific goals and activities of this module

are listed in Table 5.

Table 5. Lab #4: Modeling of hybrid systems

Objectives

! Develop an appreciation for the importance of developing mathematical models for

studying these systems

! Develop an understanding of the role that spatial relationships (between electrode

and neuron) have in determining the efficacy of applied electrical stimuli

! Understand the role that various biophysical parameters have in determining cell

excitability

! Become proficient in using NEURON at the basic level

Activities

! Learned basics of the modeling software NEURON

! Formed and tested hypotheses about the role of electrode geometry in exciting a

model neuron (friendly user interface created by TA)

Response-

Intensity

Curves

Tuning

Curves

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! Formed and tested hypotheses about the effect of changing cell membrane

parameters (passive and active) on cell excitation

A module involving an in vitro neural interface

In addition to the four modules described above, each year two other less well-developed

modules have been incorporated as well. One of these is intended to provide students with

the experience of interfacing with neural material using an in vitro model system. This lab

module was conducted in a teaching lab outfitted with four basic electrophysiological

recording rigs. Each rig included an electronics rack with appropriate amplifiers, filters and

PC, as well as a low power microscope sufficient for viewing large cells or issue

preparations. To date we have tried three different experiment models in this module.

One experiment involved students recording from single neurons in the buccal ganglion of

the snail. The objectives of this lab included successfully completing a microdissection of

the snail, and successfully recording from single neurons using sharp electrodes. This

experiment suffered because successful recording relied heavily on obtaining a high quality

dissection. Moreover, the manipulations required to successfully impale neurons without

killing them proved challenging to master in a short time.

A second experimental model involved having students make patch (or sharp) electrode

recordings from oocytes. By using oocytes the difficult dissection is avoided, but other

activities are retained.

A third experiment tested in this module involved the students using pH sensitive electrodes

to record the pH near and within retina isolated from goldfish. Objectives for this lab

included fabrication and calibration of a pH electrode, and measurements of pH. This lab

dealt with an important neural engineering concept not addressed by the others– namely the

need to sense and measure local physiological variables.

All three experimental models described have merits and drawbacks. The difficulty for us in

making any of them an unqualified success is that none of the three techniques is within the

area of core expertise of the neural engineering course instructors.

A module involving the engineering of neural interfaces

The final component of the lab course is a lab module in which students engineer a neural

interface. Using techniques of soft lithography, students pattern a glass coverslip with a

protein that promotes cell survival, adhesion and outgrowth. By exploring different patterns

of their own design, students achieve one of the primary objectives of appreciating the

various factors involved in creating a successful interface. The other main objective for this

module is that students become knowledgeable in the practical skills (e.g. cell culture, pattern

stamping) required for micropatterning so that they can use them in novel situations.

The micropatterning lab module is not described in more detail since it is still being

optimized.

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Summary and future of the NE lab

At this time no formal assessment has been done on the NE Lab course. Of the four

objectives for the course, all are certainly being met to some extent. Improvement could

certainly be made on the final objective of providing opportunities to test hypotheses, and

design solutions to posed challenges.

Anecdotal feedback from the small number of students who took the most current version of

the NE Lab was overwhelmingly positive. They perceived the experience as extremely

exciting and cutting edge, no doubt in part because much of the time they worked inside

research laboratories with faculty and other research staff. Of course these very same

reasons made the NE Lab resource intensive. As long as the number of students enrolled is

relatively small, the burden on a research lab is tolerable. If enrollment is to increase,

however, it will be necessary to identify a different model. One possibility is to develop core

facilities that are by design jointly used for teaching and research activities.

Another difficulty with the present model is that some of the lab activities– particularly the

ones involving animal experiments– are centered about a single experiment setup. In this

case– or in any situation where equipment is limiting– it can be challenging to design

activities that engage all students. Finally, experiments containing multiple complex

components can easily go wrong. A lab that does not work is often worse than no lab at all.

Discussion

All of the UIC neural engineering courses described in this article are open to both

undergraduates and graduate students. Currently we offer two additional courses, Neural

Prostheses, and Introduction to Neural Coding, that are designated as graduate level (Fig. 1).

Although there is sometimes a disparity in the skill and knowledge levels between the

undergraduates and graduate students, this is typically not a problem. In fact many graduate

students naturally assume more of a mentorship role and help the undergrads as needed. This

is particularly true in the laboratories where the amount of data manipulation can be

significant.

Although there is little question that neural engineering will in the long term contribute

enormously to our ability to repair, replace and even augment nervous system function– to

enormous clinical benefit– it is less clear how, and especially when– neural engineering

concepts should be taught to students. There is no arguing that neural engineering belongs in

the graduate curriculum, but is it an appropriate concentration or track for undergraduates?

At UIC approximately one third of the BioE undergraduates go on to medical school.

Approximately a third go on to graduate school, and about a third find jobs in industry. This

distribution is fairly representative of the national trend. The two-thirds of the students

enrolling in medical and graduate school probably benefit from training in neural

engineering, but what about the students going into industry? Unfortunately, the emergent

nature of neural engineering means that the job market for neural engineers is rather soft.

There are few companies hiring specifically neural engineers, and those that are typically

seek students with higher degrees. Over the next several years it will be important to follow

our neural engineering track graduates and determine where their careers lead them.

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References

1. Bellamkonda, RV, Potter, Steve, & Kipke, D (2005). Neuroengineering: What, Why and How? White

paper, Whitaker Foundation Biomedical Engineering Education Summit, 2005.

2. http://www.whitaker.org/academic/database/index.html The Whitaker Foundation Bioengineering

Curriculum Database

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