back to the website  http://www.us-government-torture.com/
 

P.S. here is where the url was found on Google:
http://www.google.com/search?hl=en&lr=&ie=ISO-8859-1&q=neural+signals+tracking+%2CDARPA&btnG=Google+Search

BUT READERS PLEASE DO CLICK THE FOLLOWING FOR THE INFORMATION with color pictures of implant insertion and details of the telemetry, AS FOLLOWS WHICH ENDS WITH .PDF
http://www.darpa.mil/dso/thrust/biosci/bim/briefings/ASU_Kipke.PDF.  <-----this one

which is also just below as the first click; its the pdf version of the html page typed below the PDF version has color pixtures of implants and actual tissue soft implants as well. If possible print the pdf and distribute with the flyer to pillars of the community at business meetings and PTA's.
 
 
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Page 1
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

Page 2
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.

Page 3
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?

Page 4
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

Page 5
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

Page 6
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

Page 7
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

Page 8
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”

Page 9
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

Page 10
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

Page 11
“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)

Page 12
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

Page 13
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.

Page 14
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

Page 15
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.

Page 16
Micromachined Surgical Devices
Vacuum nozzle
Flexible probe
Insertion aid
Vacuum Actuated Knife/Inserter
PEG
Silicon Knife/Inserter

Page 17
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

Page 18
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)

Page 19
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?

Page 20
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

Page 21
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.

Page 22
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

Page 23
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

Page 24
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

Page 25
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

Page 26
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 )

Page 27
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

Page 28
The Device-Tissue Interface
Neural Interface:
Micro-device, Neurons, Glia, Extracellular Space

Page 29
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

Page 30
Visualization of the Chronic Device-Tissue
Interface With Confocal Microscopy
A
B
C
D

Page 31
In vivo Visualization of the Chronic
Device-Tissue Interface

Page 32
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

Page 33
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 )
/

Page 34
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

Page 35
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

Page 36
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

Page 37
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…

Page 38
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

Page 39
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.

Page 40
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

Page 41
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.

Page 42
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

Page 43
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

Page 44
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?

Page 45
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.

Page 46
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

Page 47
Direct Cortical Control of Movement
Green ball: Target
Yellow ball: Actual hand position, or
hand position estimated from cortical
responses
m0602pa

Page 48
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

Page 49
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

Page 50
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

Page 51
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

Page 52
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

Page 53
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

Page 54
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

Page 55
Behavioral Performance
Ricms6

Page 56
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

Page 57
D
Expected Results to ICMS Stimuli
Begin ICMS
100
%
D% due
to ICMS
Trial #
Auditory trial =
ICMS Algorithm1 =
ICMS Algorithm2 =

Page 58
Behavioral Curve
RICMS 6 10/25 (Only Session)
100
80
Percentage
audPercent,
icmsPercent,
60
40
20
0
0
100
200
Trial

Page 59
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

Page 60
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

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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…

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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

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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

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“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

Page 65
--
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.

Page 66
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