1. 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

2. 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

3. Goal: Develop a biomimetic model of hippocampus to serve as a neural prosthesis for lost cognitive/memory function
Strategy:
Biomimetic model/device that mimics signal processing function of hippocampal neurons/circuits
Implement model in VLSI for parallelism, rapid computational speed, and miniaturization
Multi-site electrode recording/ stimulation arrays to interface biomimetic device with brain
Goal: to “by-pass” damaged brain region with biomimetic cognitive function
short-term memory
long-term memory

4. 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
pyramidal cell layer
massive loss of hippocampal CA1 pyramidal cells following an ischemic episode

5. Modeling the Transformation of Input Spatio-Temporal Patterns into Output Spatio-Temporal Patterns
r(x, y, t) = G[k(x, y, t), s(x, y, t)]

6. 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

7. (4) FPGA Simulated CA3 Output (1) Four-Pulse Input Train to Dentate
Hippocampal Model of CA3, Implemented in Hardware, Interfaced to a Slice through a Conformal, Multi-Site Planar Electrode
(3) FPGA Model: CA3
(2) Dentate Output
(4) FPGA Simulated CA3 Output
(5) FPGA Input to CA1
(1) Four-Pulse Input Train to Dentate
DENTATE
CA3
CA1
(6) CA1 Output

8. 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%

9. hippocampal slice: single circuit
Pathway to a Hippocampal Prosthesis
Hippocampal slice g Single circuit replacement
Intact hippocampus g Multiple circuit replacement
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
hippocampal slice: single circuit
intact hippocampus: multiple circuits

10. Microelectrode Designs (Univ of Kentucky)
Original
50x50 mm 1
15x300 mm 2
15x300 mm 3
10x10 mm 4
20x20 mm 5
50x50 mm 6
50x100 mm 7
25x100 mm 8
50x150 mm 9
25x300 mm 10 R1
50x150 mm S1
15x333 mm
Current designs with improved polyimide mask
20x333 mm
8 site microelectrodes W1
20x150 mm W2
20x150 mm W3
20x150 mm W4
20x150 mm S2
15x333 mm
50x50 mm

11. = Hippocampal Spatio-Temporal Coding of Memory in the Behaving Rat
Delayed Nonmatch to Sample Task
CA1
CA3 DG
Electrode
Array
Hippocampal
Hippocampal Ensemble
“Memory” Firing Pattern
Encoded Sample Lever
Position
Nonmatch “Correct”
Choice SR
LEFT
“Delay”
1-30s
LEVER =
RIGHT
Reward
LEVER
DNMS Trial
Present
Lever
Sample
Response
Delay
sec NP
NM Reward
Response

12. Modeling the Transformation of Input Spatio-Temporal Patterns into Output Spatio-Temporal Patterns
r(x, y, t) = G[k(x, y, t), s(x, y, t)]

13. TEMPORAL
CA1
CA3 DG
SEPTAL
MEDIAL
ELECTRODE
ARRAY 1 9 16 8
CA3-CA1 Spatio-Temporal Patterns of Hippocampal Population Activity Recorded During DNMS Learned Behavior
GOAL: Predicting CA1 Spatio-Temporal Patterns of Activity Given CA3 Spatio-Temporal Patterns of Activity Recorded During Behavior
Four patterns

14. A Physiologically-Plausible Stochastic Spike Model
Physiologically-plausible model structure
Post-synaptic potential (U)
Dendritic integration (K)
Threshold (q)
Spike-triggered “after potential” (H)
Stochastic model
Noise term (s)
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

15. Volterra Kernel Model Single-Input Single-Output Case
Multiple-Input Multiple-Output Case

16. Interpretation of First-, Second-, and Third-Order Kernels for Spike-In, Spike-Out Systems
Two-Input / Single-Output (including the 2nd order Cross Interactions)
Model
S2(n)
Input 2 k0 t2 t4
k1self
Output
Threshold + u
r(n)
k2self
Input 1
S1(n) t1 t2 t3 t4 t5
k3self
time t1 t3 t5
time
k2cross

17. Time-Rescaling Theorem and Kolmogorov-Smirnov Test for Model Accuracy
If P predicted by the model is correct, spike interval t 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).
v can then be tested with Kolmogorov-Smirnov (KS) plot. t1 t2 t3 t4 t5 t6 u1 u2 u3 u4 u5 u6
Within 95% confidence boundary: Good model.
Out of boundary: Inaccurate model.

18. First Order Kernel (Linear) Model

19. Second Order (Nonlinear) Self-Kernel Model

20. Third Order Self-Kernel Model

21. Modeling the Contribution of Interneuronsk1 k2
Peri-Event Histograms
Sample
Non-Match
Left
interneuron
Right
Right brain
Autocorrelogram
Left brain CA3

22. Multi-Input Multi-Output Stochastic Model
Array of multi-input single-output models

23. Predicting Hippocampal Spatio-Temporal Activity with a 16-Input, 7-Output Nonlinear Model: Case 1k1 k2
Predicted
CA1 S-T
Pattern
16 CA3 Inputs
7 CA1 Outputs
Recorded
CA1 S-T
Pattern h

24. Predicting Hippocampal Spatio-Temporal Activity with a 16-Input, 7-Output Nonlinear Model: Case 1k1 k2
Predicted
CA1 S-T
Pattern
16 CA3 Inputs
7 CA1 Outputs
Recorded
CA1 S-T
Pattern h

25. WFUHS 16-Channel Stimulator
High-Voltage Boost and Tri-State Circuit C.
STIM3 Chip Block Diagram A. 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 mA
aynchronous pulse generation capacity on each channel

26. Spatio-Temporal Pattern Stimulation of Hippocampus with MI/MO Model Output8 9 1 16
CA3 DG
Medial
Lateral
Array
Electrode
Ensemble Firing Pattern
Online Stimulation
Online Analysis
Stimulation Pattern
Online Recording
Hampson & Deadwyler 2006, WFUHS
Predicted Firing Pattern