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: 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

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

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)]

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

(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

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%

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

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

= 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

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)]

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

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

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

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

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.

First Order Kernel (Linear) Model

Second Order (Nonlinear) Self-Kernel Model

Third Order Self-Kernel Model

Modeling the Contribution of Interneurons k1 k2 Peri-Event Histograms Sample Non-Match Left interneuron Right Right brain Autocorrelogram Left brain CA3

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

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

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

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

Spatio-Temporal Pattern Stimulation of Hippocampus with MI/MO Model Output 8 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