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ICDR 2006 Implantable Biomimetic Microelectronics as Neural Prostheses for Lost Cognitive Function Theodore W. Berger, Ph.D. David Packard Professor of.

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Presentation on theme: "ICDR 2006 Implantable Biomimetic Microelectronics as Neural Prostheses for Lost Cognitive Function Theodore W. Berger, Ph.D. David Packard Professor of."— Presentation transcript:

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: 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

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 Alzheimers 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

5 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)]

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 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

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 Pathway to a Hippocampal Prosthesis Hippocampal 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

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

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

12 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)]

13 Four patterns 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

14 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

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 k 2cross Two-Input / Single-Output (including the 2 nd order Cross Interactions) k 3self k 2self + time Threshold k0k0 Output Model t1t1 t3t3 t5t5 time t1t1 t3t3 t5t5 k 1self u r(n) Input 1 Input 2 S 1 (n) S 2 (n) t2t2 t4t4 t4t4 t2t2

17 Time-Rescaling Theorem and Kolmogorov-Smirnov Test for Model Accuracy 1 2 3 4 5 6 u1u1 u2u2 u3u3 u4u4 u5u5 u6u6 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). v can then be tested with Kolmogorov-Smirnov (KS) plot. 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 Interneurons Right brain Left brain CA3 k 1 k 2 SampleNon-Match Left Right Peri-Event Histograms Autocorrelogram interneuron

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

23 16 CA3 Inputs 7 CA1 Outputs k1k1 k2k2 h Recorded CA1 S-T Pattern Predicted CA1 S-T Pattern Output #4 Predicting Hippocampal Spatio-Temporal Activity with a 16-Input, 7-Output Nonlinear Model: Case 1

24 16 CA3 Inputs 7 CA1 Outputs k1k1 k2k2 h Recorded CA1 S-T Pattern Predicted CA1 S-T Pattern Output #4 Predicting Hippocampal Spatio-Temporal Activity with a 16-Input, 7-Output Nonlinear Model: Case 1

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 A aynchronous pulse generation capacity on each channel

26 Spatio-Temporal Pattern Stimulation of Hippocampus with MI/MO Model Output Temporal CA1 8 9 1 16 CA1 CA3 DG CA3 Medial Lateral Array Electrode Ensemble Firing Pattern Online Stimulation Online Analysis Stimulation Pattern Online Recording Hampson & Deadwyler 2006, WFUHS Predicted Firing Pattern


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