icdr 2006 implantable biomimetic microelectronics as neural prostheses for lost cognitive function...
Post on 26-Mar-2015
222 Views
Preview:
TRANSCRIPT
ICDR 2006
Implantable Biomimetic Microelectronics as
Neural Prostheses for Lost Cognitive
Function
Theodore W. Berger, Ph.D.
David Packard Professor of Engineering
Professor of Biomedical Engineering and Neuroscience
Director, Center for Neural Engineering
University of Southern California
Classes of Brain Prostheses
Sensory: Artificial systems to transduce physical energy into electrical impulses for the brain, e.g., artificial retina
Motor: Artificial systems to activate or replace paralyzed limbs, e.g., injectable neuro-muscular stimulators
Goal: Develop a biomimetic model of hippocampus to serve as a neural prosthesis for lost cognitive/memory function
Strategy:
1. Biomimetic model/device that mimics signal processing function of hippocampal neurons/circuits
2. Implement model in VLSI for parallelism, rapid computational speed, and miniaturization
3. Multi-site electrode recording/ stimulation arrays to interface biomimetic device with brain
4. Goal: to “by-pass” damaged brain region with biomimetic cognitive function
long-term memory
short-term memory
Clinical Applications for a Hippocampal Cortical Prosthesis
• Brain trauma / head injury (preferential loss of hippocampal hilar neurons)
1.4 million patients: $56B/yr
• Stroke-induced cortical dysfunction (preferential damage to hippocampal CA1)
5.4 million patients: $57B/yr
• Epilepsy (hippocampal CA3 epileptogenic foci)
2.5 million patients: $12B/yr
• Memory disorders associated with dementia and Alzheimer’s disease (preferential cell loss throughout hippocampal formation)
4.5 million patients: $100B/yr
massive loss of hippocampal CA1 pyramidal cells following
an ischemic episode
pyramidal cell layer
Modeling the Transformation of Input Spatio-Temporal Patterns into Output Spatio-Temporal Patterns
r(x, y, t) = G[k(x, y, ), s(x, y, t)]
Stage 1: Replacing a Component of the Hippocampal Neural Circuit with a Biomimetic VLSI Device
- intrinsic circuitry of hippocampus: trisynaptic cascade of dentate-CA3-CA1 subregions
- develop experimentally-based, biomimetic model of the CA3 subregion
- surgically remove CA3 subregion of living hippocampal brain slice
- through neuromorphic, multi-site electrode array, interface VLSI device with brain slice to functionally replace CA3 subregion and replace whole-circuit dynamics
Hippocampal Model of CA3, Implemented in Hardware, Interfaced to a Slice through a Conformal, Multi-Site Planar Electrode
(1) Four-Pulse Input Train to Dentate
DENTATE CA3
CA1
(2) Dentate Output
(3) FPGA Model: CA3
(4) FPGA Simulated CA3 Output
(5) FPGA Input to CA1
(6) CA1 Output
Reconstitution of Hippocampal Trisynaptic Dynamics After Replacement of CA3 with a Biomimetic, Hardware Model
• random impulse train stimulation of dentate
• 1,500 impulses pre / 1,500 impulses post
• range of intervals: 1 msec – 5 sec
• CA1 field EPSP measured as output
• mean NMSE: 17.5%
Pathway to a Hippocampal ProsthesisHippocampal slice Single circuit replacement
Intact hippocampus Multiple circuit replacement
hippocampal slice: single circuit
intact hippocampus: multiple circuits
• develop biomimetic model of damaged hippocampal region
• establish bi-directional communication between biomimetic device and intact hippocampus
• restore whole circuit nonlinear dynamics: appropriate propagation of spatio-temporal patterns of activity through system
Microelectrode Designs (Univ of Kentucky)
10 Current designs
with improved polyimide mask
8 site microelectrodes
50x50 m
1
15x300 m
2
15x300 m
3
10x10 m
4
20x20 m
5
50x50 m
6
50x100 m
7
25x100 m
8
50x150 m
9
25x300 m
R1
50x150 m20x333 m
S1
15x333 m
W4
20x150 m
S2
15x333 m
W1
20x150 m
50x50 m
W3
20x150 m
W2
20x150 m
Original
SR
Hippocampal Ensemble “Memory” Firing Pattern
Hippocampal Spatio-Temporal Coding of Memory in the Behaving Rat
LEVER
LEVER
LEFT
RIGHT
Encoded Sample Lever Position
CA1
CA3
DG
CA3 CA1CA3 CA1
ElectrodeArray
CA1
CA3
DG
CA3 CA1CA3 CA1
HippocampalElectrodeArray
Reward
Nonmatch “Correct” Choice
=
“Delay” 1-30s
Delayed Nonmatch to Sample Task
NM RewardResponse
PresentLever
DNMS Trial
SampleResponse
Delay sec
NP
Modeling the Transformation of Input Spatio-Temporal Patterns into Output Spatio-Temporal Patterns
r(x, y, t) = G[k(x, y, ), s(x, y, t)]
Four patterns
CA3-CA1 Spatio-Temporal Patterns of Hippocampal Population Activity Recorded
During DNMS Learned Behavior
TEMPORAL
CA1
CA3
DG
SEPTALCA3 CA1
MEDIA
L
ELECTRODEARRAY
CA1
CA3
DG
CA3 CA1
19
16 8
GOAL: Predicting
CA1 Spatio-
Temporal Patterns
of Activity Given
CA3 Spatio-
Temporal Patterns
of Activity
Recorded During
Behavior
• Physiologically-plausible model structure
– Post-synaptic potential (U)
– Dendritic integration (K)
– Threshold ()
– Spike-triggered “after potential” (H)
• Stochastic model
– Noise term ()
• Intrinsic neuronal noise
• Unobserved inputs
– K-S validation based on time-rescaling theorem
– Estimation of firing probability (P)
• Maximum likelihood estimation
– Error function: integral of Gaussian function
– Iterative estimation
A Physiologically-Plausible Stochastic Spike Model
...)()()(),,(
)()(),()()()(
1 0 0 0321321
),(3
1 0 02121
),(2
1 0
),(10
1 2 3
1 2
N
n
M M M
nnnni
N
n
M M
nnni
N
n
M
nnii
i
txtxtxk
txtxktxkkty
Volterra Kernel Model
kernel Volterra:
output:
input:
k
y
x
length memory:
inputs ofnumber :
M
N
...)()()(),,(
)()(),()()()(
0 0 03213213
0 021212
01110
1 2 3
1 21
M M M
M MM
txtxtxk
txtxktxkkty
• Single-Input Single-Output Case
• Multiple-Input Multiple-Output Case
Interpretation of First-, Second-, and Third-Order Kernels for Spike-In, Spike-Out Systems
k2cross
Two-Input / Single-Output (including the 2nd order Cross Interactions)
1 1 1
2 2 2
, , ,
, , ,
s s s
s s s
1 1 1
1 2 1 2 3
2 2 2
1 2
M -1
0 1s 1 2s 1 2 1 1 1 2 3s 1 2 3 1 1 1 2 1 3m=0 m m m m m
M -1
1s 2 2s 1 2 2 1 2 2 3s 1 2 3 2 1 2 2 2m=0 m m
u n = k + k m s (n - m)+ k m m s (n - m )s (n - m ) k m m m s (n - m )s (n - m )s (n - m )+
k m s (n - m)+ k m m s (n - m )s (n - m ) k m m m s (n - m )s (n - m )s (
2
,s
1 2 3
1
1 2
3m m m
2s 1 2 1 1 2 2m m
n -m )+
k m m s (n - m )s (n - m )
r(n) = Threshold [u(n)]
k3self
k2self
+
time
Th
resh
old
k0
Output
Model
t1t3 t5
time
t1t3 t5
k1self
ur(n)
Input 1
Input 2
S1(n)
S2(n)
t2 t4
t4t2
Time-Rescaling Theorem and Kolmogorov-Smirnov Test for Model Accuracy
1 2 3 4 5 6
u1 u2 u3 u4 u5 u6
dttPui
i
)(
Time-Rescaling Theorem
If P predicted by the model is correct, spike interval should be transformed into an exponential random variable u with unitary mean.
u can be further transformed into a uniform random variable v on the interval (0, 1).
)(1 iui ev
v can then be tested with Kolmogorov-Smirnov (KS) plot.
Within 95% confidence boundary: Good model.
Out of boundary: Inaccurate model.
First Order Kernel (Linear) Model
31.0
946)log( L
Second Order (Nonlinear) Self-Kernel Model
30.0
874)log( L
Third Order Self-Kernel Model
30.0
867)log( L
Modeling the Contribution of Interneurons
Right brain
Left brain CA3
-1 -0.5 0 0.5 1Time (sec)
0
0.02
0.04
0.06
0.08
0.1
nr_5_2_1
Autocorrelograms, bin = 2 ms
Pro
babi
lity
-2 -1 0 1 2Time (sec)
0
10
20
30
40
nr_5_2_1
Perievent Histograms, reference = A_NONMATCH, bin = 20 ms
Cou
nts/
bin
-2 -1 0 1 2Time (sec)
0
10
20
30
40
50
nr_5_2_1
Perievent Histograms, reference = A_SAMPLES, bin = 20 ms
Cou
nts/
bin
-2 -1 0 1 2Time (sec)
0
20
40
60
nr_5_2_1
Perievent Histograms, reference = B_SAMPLES, bin = 20 ms
Cou
nts/
bin
-2 -1 0 1 2Time (sec)
0
10
20
30
40
50
nr_5_2_1
Perievent Histograms, reference = B_NONMATCH, bin = 20 ms
Cou
nts/
bin
k1 k2 Sample Non-Match
Left
Right
Peri-Event Histograms
Autocorrelogram
interneuron
Multi-Input Multi-Output Stochastic Model
Array of multi-input single-output models
16 CA3 Inputs7 CA1 Outputs
k1 k2
hRecorded CA1 S-TPattern
Predicted CA1 S-TPattern
Output #4
Predicting Hippocampal Spatio-Temporal Activity with a 16-Input, 7-Output Nonlinear Model: Case 1
16 CA3 Inputs7 CA1 Outputs
k1 k2
hRecorded CA1 S-TPattern
Predicted CA1 S-TPattern
Output #4
Predicting Hippocampal Spatio-Temporal Activity with a 16-Input, 7-Output Nonlinear Model: Case 1
WFUHS 16-Channel Stimulator
High-Voltage Boost and Tri-State Circuit
C.
STIM3 Chip Block DiagramA. B.
Triangle Biosystems STIM3 Programmable 16-Channel Stimulator
• 16 channels, programmable
• programmable parameters: delay, frequency, voltage, polarity, sense-line monitoring of actual pulse delivery
• current delivery capacity: 150 A
• aynchronous pulse generation capacity on each channel
Spatio-Temporal Pattern Stimulation of Hippocampus with MI/MO Model Output
Temporal
CA1
8
91
16
CA1
CA3DG
CA3
Med
ial
Late
ral
ArrayElectrode
Ensemble Firing Pattern
Online Stimulation
Online Analysis
Stimulation Pattern
Online Recording
Hampson & Deadwyler 2006, WFUHS
Predicted Firing Pattern
top related