Proceedings of International Joint Conference on Neural Networks, Montreal, Canada, July 31 - August 4, 2005

Implantable Biomimetic Electronics as Neural Prostheses for Lost CognitiveFunction

'Theodore W. Berger, Ph.D., 2John J. Granacki, Ph.D., 'Vasilis Z. Marmarelis, Ph.D., 3ArmandR. Tanguay, Jr., Ph.D., 4Sam A. Deadwyler, Ph.D., 5Greg A. Gerhardt, Ph.D.

'Department of Biomedical Engineering, University of Southern California, 2InformationSciences Institute, University of Southern California, 3Department of Electrical Engineering,

University of Southern California, 4Department of Physiology and Pharmacology, Wake ForestSchool of Medicine, 5Department of Neurobiology and Anatomy, University of Kentucky

Medical Center

Abstract - A multi-disciplinary project will bedescribed that is developing a microchip-basedneural prosthetic for the hippocampus, a regionof the brain responsible for the formation oflong-term memories, and that frequently isdamaged as a result of epilepsy, stroke, andAlzheimer's disease. The essential goals of thiseffort include: (1) experimental study ofhippocampal neuron and neural networkfunction, (2) formulation of biologicallyrealistic mathematical models of neural systemdynamics, (3) microchip implementation ofhippocampal system models, and (4) hybridneuron-silicon interfaces for bi-directionalcommunication with the brain. By integratingsolutions to these component problems, theteam is realizing a microchip-based model ofhippocampal nonlinear dynamics that canperform the same function as a removed,damaged hippocampal region. Through bi-directional communication with other neuraltissue that normally provides the inputs andoutputs to/from the damaged hippocampalarea, the neural model can serve as a neuralprosthesis. A proof-of-concept is presented inthe context of an application to thehippocampal slice. How the current work inbrain slices is being extended to behaving ratsand primates also is described.

I. INTRODUCTION

One of the frontiers in the biomedical sciences isdeveloping prostheses for the central nervous

system (CNS) to replace higher thought processesthat have been lost due to damage or disease. Thetype of neural prosthesis that performs or assists acognitive function is qualitatively different than acochlear implant, artificial retina, or functionalelectrical stimulation: cognitive prostheses mustfunction in a biomimetic manner to replaceinformation transmission between cortical brainregions [1]. In such a prosthesis, damaged CNSneurons are replaced with a biomimetic systemcomprised of silicon neurons. The replacementsilicon neurons have functional properties specificto those of the damaged neurons, and both receiveas inputs and send as outputs electrical activity toregions of the brain with which the damaged regionpreviously communicated (Fig. 1).

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Fig. 1. Schematic diagram for the general case ofreplacing a damaged central brain region with a VLSI(Very Large Scale Integrated system) implementationof a biomimetic model, and connecting the inputs ofthe VLSI-based model to the afferents of the damagedregion and the outputs of the VLSI-based model to theefferents of the damaged region.

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Such a new generation of neural prostheseswould have a profound impact on the quality of lifethroughout society, as it would offer a biomedicalremedy for the cognitive and memory loss thataccompanies dementia, the speech and languagedeficits that result from stroke, and the impairedability to execute skilled movements followingtrauma to brain regions responsible for motorcontrol.

We are in the process of developing such acognitive prosthesis for the hippocampus, a regionof the brain responsible for long-term memory.The goals of our effort include: (1) experimentalstudy of hippocampal neuron and neural networkfunction (2) formulation of biologically realisticmodels of neural system dynamics (3) microchipimplementation of hippocampal system models,and (4) hybrid neuron-silicon interfaces. Byintegrating solutions to these componentproblems, our team is realizing a microchip-basedmodel of hippocampal nonlinear dynamics thatcan perform the same function as a removed,damaged hippocampal region. Through bi-directional communication with other neural tissuethat normally provides the inputs and outputsto/from the damaged hippocampal area, the neuralmodel can serve as a neural prosthesis.

A proof-of-concept is described here in thecontext of an application to the hippocampal slice[2]. More specifically, the hippocampus, consistsof the dentate, CA3, and CA1 subregions organizedin an excitatory cascade network (dentate-+CA3-3CA1), and which can be maintained in abrain slice preparation. We have successfullyreplaced the biological CA3 subregion with a VLSI-based model of the nonlinear dynamics of CA3(Fig. 2), such that the propagation of spatio-temporal patterns of activity from dentate-+VLSImodel-.CA1 reproduces that observedexperimentally in the biological dentate-3CA3-)CA1 circuit. How the current work inbrain slices is being extended to behaving rats andprimates also is described.

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Fig. 2. A: Dentate-+CA3-3CAI circuit of ahippocampal slice; B: Concept of replacing the CA3region with a biomimetic VLSI device; C: Bi-directional communication between a hippocampalslice (CA3 region removed) with the VLSI devicethrough a multi-site electrode array.

II. A Biomimetic Prosthesis for the HippocampalSlice

A. Experimental Characterization of NonlinearResponse Properties of the HippocampalTrisynaptic Pathway

Our first step in developing a prosthesis for thehippocampus is to experimentally characterize thenonlinear input/output properties both of fieldCA3 and of the entire trisynaptic pathway, i.e., thecombined nonlinearities due to propagation throughthe dentate-)CA3-3CAI subfields. The model of

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CA3 will be used to develop the biomimeticreplacement device to substitute for CA3 dynamicsafter the biological CA3 has been removed from theslice. The input/output properties of the trisynapticpathway will be used to evaluate the extent to whichhippocampal circuit dynamics have been restoredafter substituting the biomimetic device for fieldCA3. Random impulse train stimulation is appliedonly to the perforant path (pp) afferents to dentate(1,500 impulses; range of intervals: 2 msec - 5 sec;mean interval: 500 msec). Nonlinearities of thedentate then determine the actual input to CA3; inturn, the nonlinearities of CA3 determine the inputto CAl. Field potentials are used as the measure ofoutput from each of the three hippocampal regions:population spikes of dentate granule cells,population spikes of CA3 pyramidal cells, andpopulation EPSPs (excitatory postsynapticpotentials) of CAl pyramidal cells. Thus, for bothfields CA3 and CAl, not only does the continuousinput of the random train vary in terms of inter-impulse interval, but because of nonlinearities"upstream", the input also varies in terms of thenumber of active afferents. The model, therefore,must be capable of predicting CA3 output as afunction of both the temporal pattem and theamplitude ofdentate input.

A segment of one random train and thecorresponding evoked dentate, CA3, and CAl fieldpotential responses are shown in Fig. 3. The toptrace represents the impulse stimulations used toactivate perforant path (pp in the upper panel)afferents to dentate (DG). The strong nonlinearitiesof the dentate are evident in the second trace:amplitude of the negative-going population spikevaries considerable as a function of inter-impulseinterval. Likewise, CA3 population spikes and CAlpopulation EPSPs also exhibit strong variation inamplitude as a function of the temporal intervals ofthe train. It is the dependence of CA3 output oninter-impulse interval and dentate populationamplitude that must be captured by the model.

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Fig. 3. Example electrophysiological responses recordedfrom the dentate gyrus (DG), CA3, and CAl subfields ofthe hippocampal slice during random impulsestimulation of inputs to DG (pp). Second trace: Fieldpotential responses recorded from the DG. Thenarrow, biphasic deflections preceding the fieldpotential responses are stimulation artifacts, and thus,correspond to the occurrence of stimulation impulses inthe first trace. The large negative-going deflection ineach response is the "population spike". Amplitude ofthe population spike correlates positively with thenumber of granule cells generating an action potential.Bottom trace: Population excitatory synaptic potentials(EPSPs) recorded from the dendritic region ofCAl.

B. Modeling Nonlinearities of the HippocampalCA3 Subregion

These experimental datasets of population spikesequences recorded at the granule cell layer (input)and the corresponding population spike sequencesrecorded at the pyramidal cell layer of CA3(output) were used to estimate the Volterra-Poissonmodel of CA3. The model estimation wascompleted using amplitude and the interspikeintervals of the population spikes in the input andoutput sequences (Fig. 3). The equationrepresenting the single input/single output, thirdorder Volterra-Poisson model employed to capturethe CA3 nonlinear dynamic properties was adaptedas follows:

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where Ai, Aj represent the varying amplitudes ofthe population spikes recorded at the granule celllayer (input), y(ni) represents the amplitude of thepopulation spikes recorded at CA3 (output), k,, k2,and k3 are the first, second, and third order kernelsrespectively, ni is the time of occurrence of thecurrent impulse in the input/output sequence, and njis the time of occurrence of the jh impulse prior tothe present impulse within the kernel memorywindow ,.

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Fig. 4. Predictions of the nonlinear input/output modelfor hippocampal CA3 vs. observed data. Predictedoutput is for amplitudes of CA3 population spikes inresponse to random impulse train stimulation of theperforant path. Panel A: predicted output (blue) anderror (red). Panels B and C are expanded segments ofpanel A for better resolution. Pink overlays are forvisual emphasis only.

Estimation of the kernels is facilitated byexpanding them are on the orthonormal basis ofLaguerre polynomials, the coefficients of whichcan be obtained via least squares as describedpreviously [3]. Data collected from hippocampalslices were analyzed and the associated Volterra-Poisson kernels computed. A third order modelprovided a consistent improvement in NMSEbetween 4% and 9% compared to a second ordermodel. The class of computed kernels withconsistently low NMSE in the neighborhood of6%exhibited behavior similar to the representative case

shown in Fig. 4.

C. Hardware Implementation of the NonlinearModel ofHippocampal CA3

The nonlinear model of hippocampal CA3, andsystem components required for real-timeinteraction with CA3 inputs (dentate) and outputs(CA1) has been implemented in FPGA (fieldprogrammable gate array) technology, as a

precursor to VLSI design and fabrication. TheFPGA system that we have developed for thehippocampal CA3 prosthesis, as depicted in Fig. 5,accepts analog signals from the hippocampaldentate region (In: DG), buffers and amplifies thesignals, and then performs an analog-to-digitalconversion (ADC). The sequence of processing iscontrolled by a finite state machine (FSM), andautomatic gain control circuitry adjusts theamplitude of the input signal. Once the signals arein binary form, other circuitry identifies andcalculates amplitude of the population spike ("SpikeDetector" in Fig. 5), and transmits the result to thecircuitry that performs the nonlinear modelingfunction ("Polynomial Update" and "ResponseGenerator" in Fig. 5). Each digital nonlinearresponse prediction is delayed by an msec timeperiod appropriate to dentate-to-CA3 propagation,and then converted to a biphasic representationcompatible with electrical stimulation of neuronaltissue ("Pulse-to-Biphasic"). The result isconverted from digital to analog (digital-to-analogconversion, DAC), and is transmitted to the

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Fig. 5. Block diagram of functions included in theFPGA implementation of the CA3 prosthetic system.See text for explanation.

D. "Conformal" Multi-Site Electrode Arrays forInterfacing the Biomimetic Model of CA3 withHippocampal Tissue

Bi-directional communication between abiomimetic device and the brain must beaccomplished with one or more multi-site electrodearrays, with the spatial distribution and density ofrecording/stimulating electrodes designed to match(be "conformal" with) the cytoarchitecture andtopography-density of the brain regions providingthe inputs and outputs of the device. To this end,we have designed, fabricated and tested astimulation/recording multi-electrode array capableof stimulating perforant path input to the dentate,recording the output from dentate, interfacing withthe FPGA-based model of CA3 described above,stimulating inputs to CAI, and recording the outputfrom CAl.

This design, shown in Fig. 6, includes twodifferent circular pad sizes: (i) 28 Arm diameterpads with a 50 Aim center-to-center spacing aregrouped in series to form sets of stimulating padsin dentate gyrus (three at a time) and CA1 (two ata time), and (ii) 36 Aim diameter pads also with a

50 gim center-to-center spacing used for recordingin DG, CA3, and CAl. By grouping sets ofstimulating pads in series, we are able to achievesignificantly larger pad surface areas andcorrespondingly larger total stimulating currents.These stimulating pads have been placed only inDG and CAl in order to interface with the FPGAhardware that replaces CA3.

Fig. 6. Conformal multi-site stimulation/recordingarray for bi-directional communication between thedentate and CAl hippocampal subregions and theFPGA CA3 nonlinear model.

E. Restoration ofHippocampal Circuit Dynamicswith the CA3 Prosthesis

We are currently in the process of evaluatingthe output generated in the CAl region by ourCA3 prosthesis system, i.e., comparing CAloutput in response to the FPGA model of CA3with the output of CAl in response to thebiological CA3. For these tests, we are usingrandom impulse train stimulation of the perforant

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Fig. 7. Hippocampal CAl output (population EPSPamplitudes) in response to random impulse trainstimulation in the intact slice ("CAl Trisynaptic") andafter replacement of subregion CA3 with a biomimeticFPGA-based model of CA3 nonlinear dynamics ("CA1Replacement"). See text for explanation.

Each panel illustrates results from one experiment:amplitudes of population EPSPs recorded from theCAl region are shown as a function of 50impulses chosen from among 3,000 impulses ofthe random trains (1,500 administered beforetransecting inputs to CA3; 1,500 administeredafter transection). Time intervals betweenimpulses are not represented in the figures; only"Input Event" number (sequence of sampleimpulses) is shown to "collapse" the x-axis. Datafor the intact slice (CAl trisynaptic) is shown ingray boxes; data for the "hybrid" slice with thesubstituted FPGA model of CA3 (CAlreplacement) is shown in black diamonds. Forwhat is a wide range of intervals captured in this50-impulse sequence, and what is a 3-5 folddifference in population EPSP amplitude, CAIoutput from the "hybrid" slice matches extremelywell the CAl output from the intact slice. Furtherquantification studies are in progress.

III. Extension to the Behaving Animal

The research described here represents the firststep in developing a neural prosthesis for thehippocampus of the behaving animal, and ultimatelyof the human, to restore memory function afterdamage. In animals (and humans) performinglearned behaviors, memory-related information iscoded in a distributed manner among a population(or "ensemble") of neurons, each subpopulationfiring at different times and with different patternsin relation to environmental cues [4]. Thus,extending the approach described here to thebehaving animal will require: (i) multiple input-multiple output models to represent the nonlineardynamics of each of the different subpopulations ofthe ensemble, (ii) a more complex VLSI devicedesign (perhaps involving a multi-chip module) toimplement the multiple input-multiple outputmodel, (iii) vertically-oriented, conformal, multi-siteelectrode arrays capable of penetrating into thehippocampus from the surface of the brain torecord/stimulate target regions, (iv) developmentand application of packaging technologies tointegrate VLSI and electrode sensing/actuatingfunctions, (v) surface patterning of novelchemistries to increase biocompatibility ofmicrofabricated materials/devices. Progress onthese fronts and others is currently proceeding.

[1] Berger TW, Baudry M, Brinton RD, Liaw J-S,Marmarelis VZ, Park Y, Sheu BJ, Tanguay Jr, AR.Brain-implantable biomimetic electronics as the nextera in neural prosthetics. Proceedings of the IEEE2001; 89: 993-1012.[2] Berger TW, Granacki JJ, Marmarelis VZ, Sheu BJ,Tanguay Jr AR. Brain-implantable biomimeticelectronics as neural prosthetics. Proceedings of theIEEE EMBS Conference 2003; 1956-1959.[3] Marnarelis VZ. Identification of nonlinearbiological systems using Laguerre expansions ofkernels. Ann Biomed Eng 1993; 21: 573-589.[4] Hampson RE, Simeral JD, Deadwyler SA.Distribution of spatial and nonspatial information indorsal hippocampus. Nature 1999; 402: 610-614.

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