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.
 
 
This is the html version of the file http://www.darpa.mil/dso/thrust/biosci/bim/briefings/ASU_Kipke.PDF.
Google automatically generates html versions of documents as we crawl the web.
To link to or bookmark this page, use the following url: http://www.google.com/search?q=cache:XZ5J3obaJSQJ:www.darpa.mil/dso/thrust/biosci/bim/briefings/ASU_Kipke.PDF+neural+signals,DARPA&hl=en&ie=UTF-8

Google is not affiliated with the authors of this page nor responsible for its content.
These search terms have been highlighted:  neural  signals  darpa 

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
(