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Advanced Neural
Implants
and
Control
Daryl
R. Kipke
Associate
Professor
Department
of Bioengineering
Arizona
State University
Tempe,
AZ 85287
kipke@asu.edu
Approved
for Public Release, Distribution Unlimited: 01-S-1097
The
Underlying Premise…
The
ability to engineer reliable,
high-capacity
direct interfaces to the
brain
and then integrate these into a
host
of new technologies will cause
the
world of tomorrow to be much
different
than that of today.
However…
There
are some serious scientific barriers between
where
we stand today and where we can stand in
the
future.
•
How
do we establish permanent and reliable interfaces to
selected
areas of the central nervous system?
•
How
do we use these interfaces to directly and reliably
communicate
at high rates with the brain?
Applied Neural
Implants and Control
Project
Director
Kipke
(BME)
Advisory
Committee
Raupp,
Hoppensteadt,
Farin
Visualization
&
Modeling
Farin
(CSE)
Nelson
(CSE)
Razdan
(CSE)
Smith
(Math)
Systems
Science &
Signal
Processing
He
(BME)
Hoppensteadt
(Math &
EE)
Kipke
(BME)
Si
(EE)
Neural
& Tissue
Engineering
Kipke
(BME)
Massia
(BME)
Panitch
(BME)
Rousche
(BME)
Tissue
Culture &
Analysis
Capco
(Bio)
Massia
(BME)
Pauken
(Bio)
Materials
Synthesis
&
Bioactive
Coatings
Ehestraimi
(BME)
Massia
(BME)
Panitch
(BME)
Raupp
(ChemE)
MEMS
Shen
(EE)
Pivin
(EE)
Li
(EE)
INFO
BIO
MICRO
Primary
Goals of the
BIO:INFO:MICRO
Project
Develop
new neural implant
technologies
to establish
reliable,
high-capacity,
and long-
term
information channels
between
the brain and external
world.
Develop
real-time signal
processors
and system
controllers
to optimize
information
transmission
between
the brain and the
external
world.
SysSci
VizMod
NeuEng
MEMS
TisClt
Mat'lSyn
Systems-level
Approach…
Feedback
control signals
Subject
Neural
system
(global)
Controlled
neural
plasticity
local
Neural
Implant
Adaptive
Controller
External
World
Objective
2: Optimize
Adaptive
Controller
Objective
1: Optimize neural
interface
Topics
Project
overview
Towards
the Development of Next-
Generation Neural
Implants
(BIO,
MICRO,
and
INFO)
Bioactive
Coatings to Control the Tissue
Responses
to Implanted Microdevices
Modeling
the Device-Tissue Interface
Direct
Cortical Control of an Actuator
Neural
Control of Auditory Perception
Wrap-up
Focus
on Next-Generation
Neural
Implants
Feedback signals:
local
Subject
Neural
system
(global)
Controlled
neural
plasticity
local
Neural
Implant
Neural
Controller
External
World
host
response
Info. Signals:
electrical
&
chemical
Objective
2: Optimize
Adaptive
Controller
Objective
1: Optimize neural
interface
to achieve reliable, two-way,
high-capacity
information channels.
…and
“self-diagnostic”
Fundamental
Problem of Implantable
Microelectrode
Arrays
Brain
often encapsulates the device with scar tissue
Normal
brain movement may cause micro-motion at the tissue-
electrode
interface
Proteins
adsorb onto device surface
Useful neural
recordings are eventually lost
Electrode
1
Electrode
N
Implant
Failure
Month
1
Implant
Month
N
3
rd
-Generation Neural
Implants
Technology
Spectrum
1
st
-generation
Microwires
2
nd
-generation
Silicon
arrays
3
rd
-generation
Neural
Implants
Desired
Properties
•
Very
high channel count
(<1000)
•
Bioactive coatings
•
Flexible
•
Engineered surfaces
•
Controlled
biological
response
•
Integrated electronics
“Brain-centered”
Design of Neural Implants
Initial
conceptual designs
B
B
A
A
A
A
B
B
through
hole
connecting
channel
recording
site
bioactive
gel
Standard
Perforated Probe
Simple
Bioactive Probe
Differential
Bioactive Probe
through
hole
recording
site
bioactive
gel
flexible
polyimide
substrate
bond
pads
e.g.
corticosteroid
NGF
e.g.
GABA
cross-section
(A-A)
cross-section
(B-B)
Polymer-substrate Neural
Implants
•
2-D planar devices can be bent into 3-D structures
•
Increases insertion complexity
Holes
to
promote
integration
with
neuropil
90
degree
angles
Recordings
From Polymer-substrate
Neural
Implants
Chan.
9
Chan.
10
One
Day Post-op
Lost
most unit activity
after
7 days – Most likely
due
to failure to properly
close
dural opening.
Flexible Neural
Implants Present
Surgical
Challenges
While
the “micro-motion” hypothesis suggests that flexible
neural
implants should be more stable, the same flexibility
presents
significant new surgical challenges.
“Difficult”
insertion
“Easy”
insertion
Rdr2,
9-00
Rdr3,
9-00
Using
Dissolvable Coatings to
Stiffen
the Neural Implant
Dip-coat
microdevice with polyethylene glycol (PEG)
•
Provides
mechanical stiffening prior to implant
•
Quickly
dissolves when in contact with tissue
First
insertion of coated microdevice into
Second
insertion of coated microdevice
gelatin
-- Device easily penetrates
into
gelatin – The device is too flexible to
material
penetrate
material because the PEG has
dissolved.
Micromachined
Surgical Devices
Vacuum
nozzle
Flexible
probe
Insertion
aid
Vacuum
Actuated Knife/Inserter
PEG
Silicon
Knife/Inserter
Exploratory
Functionality
Bioactive
Component
Storage
Structures
Passive
Surface
Engineering
Active
FET
Devices,
ChemFETs
Electrical
Recording/Stimulating
Surfaces
Other
Active Devices
(Thermal,
Magnetic, Strain, etc.)
Fluid
Microchannels
Polymer
Substrate
•
Magnetic/thermal
stimulation
•
Drug delivery channels
•
Active micro-
manipulation
of probes
Currently...
Internal
Review
Feasibility
Studies
Insertion
Aids
Mechanical
Transfer
Structures
Signal
Processing
Termination
Multiple
Dimensions and Forms
Implant
Coatings and Surface Modifications
Parylene-N,C
Photo-crosslinked
Polyimides
Cl
Cl
O
O
O
n
C
C
C
C
smooth
smooth
smooth
porous
porous
porous
N
N
O
O
Surface
Plasma Treatments
NH
2
NH
2
NH
2
NH
2
(NH
3
-
Amination)
Advanced
Neuro-Device Interfaces
Passive
Chemical/Electronic
NH
NH
22
NH
NH
22
NH
NH
22
NH
NH
22
Amplification
ion
beam
metal
modified
region
site
or interdigits
release
layer
polymer
(PI/P-C)
or
substrate
Active
Silicon
FETs?
Topics
Project
overview
Towards
the Development of 3
rd
-Generation
Neural
Implants
(BIO,
MICRO, and INFO)
Bioactive
Coatings for Controlled
Biological
Response
(BIO,
MICRO, and INFO)
Modeling
the Device-Tissue Interface
Direct
Cortical Control of an Actuator
Neural
Control of Auditory Perception
Wrap-up
Approach
Advanced
biomaterials
and
micro-devices
for long-term
implants
(BIO, MICRO, INFO)
Cellular
and biochemical
response
characterization
(BIO,
MICRO)
Models
and 3-D visualization
of
device-tissue dynamics
(BIO,
INFO)
Engineer
the neural implant surface in order
to control
both
the material response and the host response.
Factors
Limiting Chronic
Soft
Tissue Implants
Inability
to control cellular interactions at
biomaterial-tissue
interface
Initial
adsorption of biological proteins
•
Non-selective
cellular adhesion
Unavoidable
“generic” foreign body reactions
•
Inflammation
•
Fibrous
capsule formation
Potential
Solution
Engineer
surface for minimal protein adsorption
and
selective cell adhesion
•
Cell-resistant
polymer coatings
•
Synthetic: Polyethylene Glycol, Polyvinyl Alcohol
•
Natural: Polysaccharides, Phospholipids
•
Surface
immobilization of biologically active
molecules
•
Mimic biochemical signals of extracellular
matrix
•
Cell binding domains for integrin receptors
Biomimetic
Surface Modification
NH
2
NH
2
OH
O
HO
N
O
O
OH
OH
O
O
OH
OH
O
HO
HO
HO
O
OH
OH
O
O
HO
N
OH
HO
O
HO
O
NTF
NTF
Material
Surface
Recombinant
NGF Fusion Protein
Factor
IIIa
Active
or inactive plasmin
degradable
substrate
Degraded
plasmin
substrate
substrate
Human
b-NGF
plasmin
Fibrin
Plasmin
cleavage
Human
b-NGF
Fibrin
Bioactive
Functionality
Methods
6-hour
diffusion in rat cortex
Fluorescence
Intensity Profile
250
NeuroTraceDiI
tissue-labeling paste,
inverted
fluorescent microscope with
FITC/rhodamine
filter cube
200
150
Pixel
Value
100
5
0
0
0
2
0
4
0
6
0
8
0
100
120
140
160
D
i s t a n c e ( m i c r o n s )
Topics
Project
overview
Towards
the Development of 3
rd
-Generation
Bioactive
Coatings to Control the Tissue
Neural
Implants
(BIO,
MICRO, and INFO)
Responses
to Implanted Microdevices
(BIO,
MICRO,
and
INFO)
Modeling
the Device-Tissue Interface
(BIO,
MICRO, and INFO)
Direct
Cortical Control of a Motor Prosthesis
Neural
Control of Auditory Perception
Wrap-up
The
Device-Tissue Interface
Neural
Interface:
Micro-device,
Neurons, Glia, Extracellular Space
The
Goal is to Characterize, Predict, and Control
the
Device-Tissue Interface
Tissue
State
(e.g.,
encapsulation,
excitability)
Biophysical
Model
of the
Device-Tissue
Interface
Device
Function
(e.g.,
impedance
spectrum)
•
Integrate bioelectrical, histological and biochemical data
•
Optimize electrode specifications
Visualization
of the Chronic Device-Tissue
Interface
With Confocal Microscopy
A
B
C
D
In
vivo Visualization of the Chronic
Device-Tissue
Interface
Multi-Domain
Continuum Model
(
)
(
)
(
)
/
/
/
At
each "point" in space:
volume
fraction
potential
,
conductivity
tensor
membrane
parameters
,
, ,
etc.
e
i
e
i
e
i
L
r
f
r
t
G
C
g
a
F
r
r
r
r
•
Tissue is two (or more) coupled
volume-conducting
media
•
Electrode is boundary condition
r
r
Equations
for a Multi-Domain
Continuum
Model
Volume
conductor equations (conservation of current)
-
f
e
(
G
e
F
e
)
=
+
I
mem
i
+
I
app
i
-
f
i
(
G
i
F
i
)
=
-I
mem
i
i
=
index
over intracellular domains
Membrane
potential(s) and membrane current(s)
¶V
V
i
=
F
i
-
F
e
I
mem
i
=
a
i
Ł
C
i
¶t
i
+
I
ion
i
ł
-1
F
=
potential
(mV)
a
i
=
surface
to volume ratio (cm )
V
i
=
membrane
potential (mV)
/
e
i
3
2
G
e
i
=
conductivity
(mS/cm)
I
mem
i
=
membrane
current (mA/cm )
C
i
=
membrane
capacitance (mF/cm )
/
3
2
f
e
i
=
volume
fraction
I
app
=
applied
current (m A/cm )
I
ion
i
=
membrane
current (mA/cm )
/
Levels
of Modeling
Numerical
Multiple
intracellular domains
Voltage-dependent
conductances
ion
i
=
g
ij
q
ijk
(
V
i
-
E
j
)
j
k
¥
¶q
ijk
q
ijk
-q
V
ijk
(
i
)
=
-
¶t
t
ijk
(
V
i
)
Complex
electrode geometry
Tissue
inhomogeneous and
anisotropic
under
construction
Analytical
A
single intracellular domain
Passive
membrane conductance
I
ion
=
g
L
(
V
E
L
)
-
Simple
electrode geometry
Tissue
assumed homogenous and
isotropic
much
progress
I
Bi-domain
Model for the
Microcapillary
Bioreactor
Calculate
profiles
F
1
e
i
(
x;w
)
/
in
bioreactor
...and
impedance...
Z
(
w
)
=
F
1
e
(
L;w
)
-F
1
e
(
0;w
)
j
1
100
Hz
1
1
/
Write
BCs and assume:
(
,
)
(
; )
i
t
i
t
e
i
e
i
j
j e
x
t
x
w
w
=
F
=
F
Z
w
(
)
/
...and
predict
as
tissue parameters
,
,
, , ,
are
experimentally
manipulated
e
i
e
i
L
Z
f
G
C
g E
w
a
V
e
F
i
F
L
E
/
e
w
/
L
Recap
Focused
& integrated effort
•
BioMEMS…Neural
Engineering…Materials…
Computational
Neuroscience…Cellular
Biology…Visualization
Why
are we so excited?
•
We
have the very real
potential
of characterizing
the
biological responses to
neural
implants and then
engineering
new classes
of
microdevices to provide
a
permanent high-capacity
interface
to the brain
BIO
INFO
MICRO
Why
the BIO, INFO, and
MICRO
Program?
Wide-open
Challenges
•
Characterizing
and modeling the biological (cellular and
chemical)
responses around a neural implant
•
Controlling
the dynamic biological responses around a neural
implant.
•
Designing,
fabricating, and using “advanced” neural
implants
Collaboration
Possibilities
•
Additional
functionalities for implantable microdevices of the
class
that we are working on.
•
Exploring
fundamentally new types of tissue-device interfaces.
•
Complementary
studies of the neural interface
(experimental
and
analytical)
•
Confocal
microscopy of the neural interface
•
Sharing
technologies, procedures, insights, etc…
•
New
emergent ideas…
Systems-level
Analysis of Advanced
Neuroprosthetic
Systems
Feedback
control signals
Subject
Neural
system
(global)
Controlled
neural
plasticity
local
Neural
Implant
Adaptive
Controller
External
World
Objective
2: Optimize
Adaptive
Controller
Objective
1: Optimize neural
interface
Systems-level
Approach for Advanced
Neuroprosthetic
Systems
Subject
Neural
system
(global)
Controlled
neural
plasticity
local
Neural
Implant
Adaptive
Controller
External
World
Feedback
control signals
Objective
2: Develop
Objective
1: Optimize neural
adaptive
controller to
interface
optimize
system
performance.
Advanced
Neuroprosthetic Systems
High-Level
Neural
Computation
Sensory
Transduction
&
Pre-processing
Motor
Commands
Movement
Perception,
Decision,
Detection
Sensory
Integration
External
World
Neuroprosthetic
System
Underlying
System Principles
•Two-way
communication with targeted neural
systems
•Harness neural
plasticity to our advantage
•Appropriately
balanced
“wet-side” and “dry-side” computation
Approach
Four
Project Areas
Direct neural
control of actuators
Detection
of novel sensory stimuli through
monitoring neural
activity
Neural
control of behavior
Investigate
signal transformations from
ensembles
of single neurons to local field
potentials
to EEG.
Topics
Project
overview
Towards
the Development of 3
rd
-Generation Neural
Implants
(BIO,
MICRO, and INFO)
Bioactive
Coatings to Control the Tissue Responses to
Implanted
Microdevices
(BIO,
MICRO, and INFO)
Modeling
the Device-Tissue Interface
(BIO,
MICRO, and
INFO)
Direct
Cortical Control of a Motor Prosthesis
(BIO,
MICRO, and INFO)
Neural
Control of Auditory Perception
Wrap-up
Direct
Cortical Control of Actuators
High-Level
Neural
Computation
Sensory
Transduction
&
Pre-processing
Motor
Commands
Movement
Perception,
Decision,
Detection
Sensory
Integration
External
World
Neuroprosthetic
System
Goal:
Control
arm-related
actuator
External
Actuator
Robotic
Arm or
Virtual
Reality
Fundamental
Questions
What
are “optimal” real-time signal processing
strategies
for precise 3-D control of external, arm-
related
actuators in the presence of sensory
distractions
and/or physical perturbations to the
arm?
To
what extent can we use composite neural
signals
[neuronal (unit) recordings, local field
potentials,
and brain-surface recordings] for control
signals?
How
do we take advantage of inherent or
controlled neural
plasticity in order to optimize
system
performance?
Experimental
Preparation
•
Train monkeys to perform tracking and/or reaching tasks.
•
Record cortical responses with multichannel neural
implants.
•
Measure arm movement in 3-D space.
Chronic Neural
Recordings
Multi-channel neural
implants in motor and sensorimotor cortical areas.
Eventually:
Sub-dural electrodes for local potentials
-0.2
0
0.2
0.4
0.6
0
10
dsp009b
-0.2
0
0.2
0.4
0.6
0
20
40
dsp012a
-0.2
0
0.2
0.4
0.6
0
10
20
dsp018a
-0.2
0
0.2
0.4
0.6
0
20
40
dsp024a
-0.2
0
0.2
0.4
0.6
0
20
40
dsp025a
-0.2
0
0.2
0.4
0.6
Time
(sec)
0
10
dsp030a
-0.2
0
0.2
0.4
0.6
0
100
dsp034a
-0.2
0
0.2
0.4
0.6
0
50
100
150
dsp037a
-0.2
0
0.2
0.4
0.6
0
5
10
15
dsp040a
-0.2
0
0.2
0.4
0.6
0
10
20
dsp042a
-0.2
0
0.2
0.4
0.6
0
20
dsp042b
-0.2
0
0.2
0.4
0.6
Time
(sec)
0
10
20
30
dsp045a
-0.2
0
0.2
0.4
0.6
0
20
40
dsp046a
-0.2
0
0.2
0.4
0.6
0
5
10
15
dsp051a
-0.2
0
0.2
0.4
0.6
0
20
40
dsp057a
-0.2
0
0.2
0.4
0.6
0
40
80
dsp058a
Perievent
Histograms Target 1, reference = C_rel, bin = 20 ms
Neural
Recording
System
Offline
Analysis
Real-time
Signal
Processing
Actuator
Control
Extracellular
recordings
Direct
Cortical Control of Movement
Green
ball: Target
Yellow
ball: Actual hand position, or
hand
position estimated from cortical
responses
m0602pa
Topics
Project
overview
Towards
the Development of 3
rd
-Generation Neural
Implants
(BIO,
MICRO, and INFO)
Bioactive
Coatings to Control the Tissue Responses to
Implanted
Microdevices
(BIO,
MICRO, and INFO)
Modeling
the Device-Tissue Interface
(BIO,
MICRO, and
INFO)
Direct
Cortical Control of a Motor Prosthesis
(BIO,
MICRO,
and
INFO)
Neural
Control of Auditory Perception
(BIO,
MICRO,
and INFO)
Wrap-up
Neural
Control of Auditory Perception
High-Level
Neural
Computation
Sensory
Transduction
&
Pre-processing
Motor
Commands
Movement
Perception,
Decision,
Detection
Sensory
Integration
External
World
Neuroprosthetic
System
Goal:
Control
auditory
perception
Fundamental
Questions
To
what extent can we control auditory-mediated behavior using
intra-cortical
microstimulation (ICMS) through the neural
interface?
Transmitter
Channel
Receiver
Source
Signal
Received
Signal
Stimulator
Neural
Interface
Auditory
Cortex
What
are the information transmission characteristics of the
multichannel neural
implant in high-level cortical areas using
ICMS?
Channel
capacity (bits per second)
Channel
reliability
Channel
resolution
How
can we optimize information transmission
Implant
designs, Neural implant locations,
Signal encoding strategies,
Controlled neural
plasticity
Chronic Neural
Recordings
Multi-channel neural
implants in primary auditory cortex
Extracellular
recordings
in
auditory cortex
Estimation
of
Neural
Recording
System
Offline
Analysis
Neuronal
Response
Properties
Algorithm
Selection
Signal
Encoder
Sounds
Electrical
Stimulation
to
Aud. Ctx.
Behavioral
performance to both sounds and
cortical
electrical stimulation
Auditory
Behavior
•
Lever-press sound or ICMS discrimination task
•
Center paddle hit starts trial, 2-tone pair presented
•
Reward obtained by signaling the correct stimulus
sequence
center
left
right
rat
Frequency
response
areas
Frequency
Selectivity in Auditory Cortex
1
2
5
10
20
30
40
60
80
dsp024b
28.
56.
1
2
5
10
20
30
40
60
80
dsp018d
11.
22.
1
2
5
10
20
30
40
60
80
dsp020a
10.
20.
1
2
5
10
20
30
40
60
80
dsp024a
10.
20.
1
2
5
10
20
30
40
60
80
dsp012a
21.
42.
1
2
5
10
20
30
40
60
80
dsp018b
10.5
21.
1
2
5
10
20
30
40
60
80
dsp018c
22.
44.
1
2
5
10
20
30
40
60
80
dsp002a
3.
6.
1
2
5
10
20
30
40
60
80
dsp002b
5.5
11.
1
2
5
10
20
30
40
60
80
dsp010b
12.
24.
Freq.
Sound
Level
Signal
Encoding Algorithm:
Frequency
Selectivity
ICMS
pattern is based
solely
on frequency
selectivity
of neurons
recorded
on an electrode
dB
80
60
40
u5b
8
6
Spikes
4
2
0
1
5
10
30
kHz
u32a
0
2
4
6
8
kHz
1
5
10
30
Spikes
80
60
dB
40
Behavioral
Performance
Ricms6
Rat
Behavioral Performance
RICMS
6
100
09/06/00
09/16/00
09/26/00
10/06/00
10/16/00
10/26/00
Training
day
Implanted
90
80
Percent
Correct
70
60
50
40
30
20
10
0
Cortical
Electrodes
D
Expected
Results to ICMS Stimuli
Begin
ICMS
100
%
D%
due
to
ICMS
Trial
#
Auditory
trial =
ICMS
Algorithm1 =
ICMS
Algorithm2 =
Behavioral
Curve
RICMS
6 10/25 (Only Session)
100
80
Percentage
audPercent,
icmsPercent,
60
40
20
0
0
100
200
Trial
Alternative
Signal Encoding Algorithm:
Cortical
Activation Pattern
For
a given electrode, the unit firing pattern is used as a
template
for ICMS delivery
Auditory
Stimulus
Sound
on
Response
Raster
Matching
ICMS
‘pattern’
***Procedure
is simultaneously duplicated on each active electrode
Recap
Focused
& integrated effort
•
Neural
Engineering…Signal
Processing…Systems
Neurophysiology…Visualization
Why
are we so excited?
•
We
have the very real
potential
of developing new
classes
of neuroprosthetic
systems
to explore our ability
to
interact directly with the
brain.
BIO
INFO
MICRO
BIO,
INFO, and MICRO…
Wide-open
Challenges
•
Appropriate
mathematical constructs for describing neural
encoding
and decoding.
•
Advanced
data visualization techniques for understanding this
new
class of neural data.
•
Understanding
signal transformations as a function of the
spatial
and temporal scale of the neural
data.
Collaboration
Possibilities
•
Exploring
new signal encoding and decoding strategies for
particular
neuroprosthetic applications.
•
Sharing
technologies, procedures, insights, etc…
•
New
emergent ideas…
Topics
Project
overview
Towards
the Development of 3
rd
-Generation Neural
Implants
(BIO,
MICRO, and INFO)
Bioactive
Coatings to Control the Tissue Responses to
Implanted
Microdevices
(BIO,
MICRO, and INFO)
Modeling
the Device-Tissue Interface
(BIO,
MICRO, and
INFO)
Direct
Cortical Control of a Motor Prosthesis
(BIO,
MICRO,
and
INFO)
Neural
Control of Auditory Perception
(BIO,
MICRO, and
INFO)
Wrap-up
Project
Challenges
Scientific
•
Overcoming
engineering and scientific hurdles.
•
Identifying
and fostering strategic alliances with appropriate
external
groups.
•
Crossing
disciplines
Management
•
Strategic
planning
•
Resource
allocation
•
Open
and effective communication among the diverse project
team
•
Team-building:
Maintaining enthusiasm, energy, and focus
after
the initial “honeymoon” period
“Insanely
Intense
Interdisciplinary”
Research
“pieces
of a puzzle”
“easy
synergism”
BIO
INFO
MICRO
BIO
MICRO
INFO
Breakthrough
Science
•Hard
work
•Open
minds
•Honesty
•Top-notch
research
--
What
Does the Future Hold?
“Perhaps
within 25 years there will be some new ways to put
information
directly into our brains. With the implant technology
that
will
be available by about 2025, doctors will be able to put something
like
a chip in your brain to prevent a stroke, stop a blood clot, detect
an
aneurysm, help your memory or treat a mental condition. You
may
be able to stream (digital) information through your eyes to the
brain.
New
drugs may enhance your memory and fire up your
neurons.”
Dr.
Arthur Caplan,
Director
of the Center of Bioethics,
University
of Pennsylvania
Arizona
Republic, Dec 27, 1998.
Acknowledgments
ASU
Colleagues
•
13
co-PI’s, 5 research faculty, numerous graduate
and
undergraduate students.
Arizona
State University administration
•
Seed
funding from Department, College, and
University
•
Significant
cost-share on this project
DARPA
Program Managers
•
Eric
Eisenstadt, Abe Lee, and Gary Strong