HIGH RESOLUTION PHOTOVOLTAIC RETINAL PROSTHESISjf121qg4677/thesisSubmit... · high resolution...

HIGH RESOLUTION PHOTOVOLTAIC RETINAL PROSTHESIS

A DISSERTATION

SUBMITTED TO THE DEPARTMENT OF APPLIED PHYSICS

AND THE COMMITTEE ON GRADUATE STUDIES

OF STANFORD UNIVERSITY

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS

FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

James Donald Loudin

December 2010

http://creativecommons.org/licenses/by-nc/3.0/us/

This dissertation is online at: http://purl.stanford.edu/jf121qg4677

2011 by James Loudin. All Rights Reserved.

Re-distributed by Stanford University under license with the author.

This work is licensed under a Creative Commons Attribution-Noncommercial 3.0 United States License.

ii

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I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.

Daniel Palanker, Primary Adviser


Sebastian Doniach, Co-Adviser


Stephen Baccus

Approved for the Stanford University Committee on Graduate Studies.

Patricia J. Gumport, Vice Provost Graduate Education

This signature page was generated electronically upon submission of this dissertation in electronic format. An original signed hard copy of the signature page is on file inUniversity Archives.

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ABSTRACT

Age-related blindness has become a critical issue as life expectancies continue to rise.

Age-related Macular Degeneration (AMD) is the leading cause of blindness in the

developed world, with an incidence of 1:500 in patients age 55-64, and 1:8 in patients

over 85. Retinitis Pigmentosa (RP) is the leading cause of inherited blindness, occurring

in about 1 in every 4000 births. This disease afflicts patients starting in their early 20s,

leaving them blind for the most productive period of their lives. Both diseases are

characterized by the degeneration of the image capturing photoreceptor layer of the

retina, while neurons in the image processing inner retinal layers are relatively well

preserved. AMD progression can be delayed, but not prevented, while there is currently

no effective treatment for RP. Visual prostheses seek to restore visual sensation to

patients suffering from these diseases by electrically stimulating surviving retinal nerve

cells via chronically implanted electrode arrays, in the visual analog of the successful

cochlear implant. Several existing technologies have been evaluated in laboratory

settings and in patients, but like the cochlear implant, all are tethered to implanted wire

coil systems which deliver power to the neural stimulators. To date the most successful

prostheses have enabled blind patients to read large fonts. However, the perceptual

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resolution of current systems is quite low - less than 10 pixels/mm2, geometrically

corresponding to visual acuity below 20/1200.

The presented work describes the design and initial testing of a high resolution,

photovoltaic retinal prosthesis in which power and data are directly delivered to

photodiodes within each pixel using pulsed, near-infrared light. This direct optical data

delivery maintains the natural link between eye movements and visual stimulus, while the

lack of a separate power delivery system greatly simplifies the implantation of an array of

such pixels, thereby decreasing the risk of surgical complications. All pixels operate

autonomously, obviating the need for a wiring array and allowing separate arrays to be

independently placed in different areas of the subretinal space. A goggles-mounted

camera captures images of the visual scene, which are processed by a pocket computer

before being projected onto the pixel array by a near-to-eye projection system. This

projection system is similar to commercially available video goggles, but approximately

1000 times brighter, requiring the use of novel laser projection and de-speckling

techniques. The charge injection characteristics of several dozen different circuit designs

and electrode geometries were measured, before were selecting two for fabrication with

16, 64, and 256 pixels/mm2. Initial tests have been performed with both single and three-

diode pixels.

Previous work on blind rats implanted with subretinal photodiode arrays has

recorded neural activity in response to an infrared flash. However, these recordings were

made from electrodes in the superior colliculus region of the brain, and therefore yield

little insight into the retinal stimulation dynamics. To probe this space, we have

measured photovoltaic stimulation responses from ex vivo rat retinas sandwiched between

512-microelectrode recording array and a photovoltaic stimulating array. Stimulated

ganglion cell spikes were observed with latencies in the 1-100ms range, and with peak

irradiance stimulation thresholds varying from 0.1 to 1.0 mW/mm2. The elicited

response disappeared upon the addition of synaptic blockers, indicating that stimulation is

mediated by the inner retina rather than the ganglion cells directly, and raising hopes that

a subretinal photovoltaic prosthesis will preserve some of the retinas natural signal

processing.

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ACKNOWLEDGEMENTS

There are many people who helped bring me to the point where I can write this

dissertation. The graduate students, staff scientists, and professors I have worked with

during my time at Stanford are among those who helped most directly. Daniel Palanker

has been the biggest influence on my time in grad school as my advisor, shaping my

experimental techniques and leading the retinal prosthesis project. His accessibility and

policy of frequent, direct interactions with students have been invaluable in contributing

to my growth as a scientist.

Alex Butterwick was the only other graduate student when I first joined the lab, and it

was with his guidance that I learned my way around the lab. I was fortunate to work

together with him on the tethered implant/rat backpack experiments described in this

thesis, where his fabrication expertise allowed the construction of several different types

of implants.

Ilya Toytman and Chris Sramek joined the lab soon after me. We were all three friends

before joining the same lab, and remain so now, 5 years later. Having the lab culture

dominated by myself and my friends has made all the difference. In addition, their

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willingness to share their optics expertise with me has often sped my results. I am happy

and lucky to call them two of my greatest friends. I would also like to thank the other

graduate students with whom I have interacted with and learned from over the years, of

whom I will specifically mention David Boinagrov, Lele Wang, Rostam Dinyari, and

Karthik Vijayraghaven.

Phil Huie has been a great mentor to me. Whether during 36 hour surgery marathons,

SEM session, or at lunch in Palo Alto, his insights and friendship were always

appreciated. His impressive jack of all trades skillset helped me out on more than one

occasion. I have enjoyed working closely with Keith Mathieson over the past year, and

the electrophysiological results presented in this thesis would not have been possible

without his help during dozens of all-day, late night experiments in Santa Cruz. In no

particular order, I would also like to thank Sasha Sher, Alex Vankov, Susanne Pangratz-

Fuehrer, Ted Kamins, Ludwig Galambos, Dmitri Simanovski, Peter Peumans, Mac

Beasley, Mihai Manu, and Steve Baccus for all contributing to my continuing education.

Finally, and most importantly, I would like to thank my family. My mother and father

gave me the educational opportunities that allowed me to be at Stanford for graduate

school. Along with my brother and sister, they have always been there for me in any and

every situation. Thank you.

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DEDICATION

This dissertation is dedicated to my grandfather Jim Decker, whose engineering sense

and deep curiosity fostered my own.

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TABLE OF CONTENTS

ACKNOWLEDGEMENTS _______________________________________________ vi

TABLE OF CONTENTS _________________________________________________ ix

LIST OF TABLES _____________________________________________________ xiii

LIST OF FIGURES ____________________________________________________ xiv

CHAPTER 1: INTRODUCTION __________________________________________ 1

1.1 Introduction to the Visual System _______________________________________ 2 1.1.1 The Eye as an Imaging System ________________________________________________ 2 1.1.2 The Retina _______________________________________________________________ 3

1.2 Retinal Degenerative Diseases __________________________________________ 6

1.3 Nerve cells __________________________________________________________ 9 1.3.1 Spiking Nerve Cells ________________________________________________________ 9

1.3.1.1 Strength-Duration Relationship _________________________________________ 11 1.3.1.2 Ion Channel Kinetics __________________________________________________ 15

1.3.2 Non-spiking nerve cells ____________________________________________________ 18

1.4 Existing Prosthesis Designs ___________________________________________ 18 1.4.1 Wireline Connection _______________________________________________________ 19 1.4.2 Inductive Coils ___________________________________________________________ 20 1.4.3 Serial Optical Telemetry ____________________________________________________ 23 1.4.4 Photodiode Array-Based Prostheses ___________________________________________ 24 1.4.5 Conclusions: Comparing the Different Approaches _______________________________ 25

1.5 The Stanford Optoelectronic Retinal Prosthesis __________________________ 26

1.6 Conclusions ________________________________________________________ 28

CHAPTER 2: THE NEAR-TO-EYE PROJECTION SYSTEM _________________ 30

2.1 Illumination System _________________________________________________ 32 2.1.1 Brightness Requirements ___________________________________________________ 32 2.1.2 Diffuser/Homogenizer _____________________________________________________ 34

2.2 Light modulator ____________________________________________________ 36

2.3 Near-to-Eye Imaging and the Ocular ___________________________________ 38 2.3.1 Field of View ____________________________________________________________ 39 2.3.2 Eye Tracking ____________________________________________________________ 39

2.4 Beam Despeckling ___________________________________________________ 40

2.5 Power Analysis _____________________________________________________ 41

2.6 Optical Safety Limits ________________________________________________ 43 2.6.1 Retinal Heating ___________________________________________________________ 43 2.6.2 Other Optical Tissues ______________________________________________________ 44 2.6.3 Existing Standards ________________________________________________________ 45

2.7 Conclusions ________________________________________________________ 45

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CHAPTER 3: THE PHOTOCIRCUIT _____________________________________ 46

3.1 Introduction _______________________________________________________ 46

3.2 The Circuits ________________________________________________________ 48 3.2.1 Photodiode Operation ______________________________________________________ 48 3.2.2 Photovoltaic vs. Photoconductive Circuits ______________________________________ 50 3.2.3 The Electrode/Tissue Load __________________________________________________ 51 3.2.4 A Shunt Resistor __________________________________________________________ 52

3.3 Measurement Setup _________________________________________________ 53 3.3.1 Electrolytes ______________________________________________________________ 53 3.3.2 Illumination and Photocircuits _______________________________________________ 53 3.3.3 Active and Counter Electrodes _______________________________________________ 54 3.3.4 Data Recording ___________________________________________________________ 55

3.4 Measurement Results ________________________________________________ 57 3.4.1 Charge Injection Dynamics _________________________________________________ 57

3.4.1.1 Platinum ___________________________________________________________ 57 3.4.1.2 SIROF _____________________________________________________________ 59

3.4.2 Charge Injection __________________________________________________________ 60 3.4.2.1 Factors Determining Maximum Charge Injection____________________________ 60 3.4.2.2 Photovoltaic Charge Injection ___________________________________________ 62 3.4.2.3 Shunt Resistor _______________________________________________________ 64 3.4.2.4 Photoconductive Charge Injection _______________________________________ 65

3.5 Implications for Prosthesis Design _____________________________________ 66 3.5.1 Charge Delivery __________________________________________________________ 66 3.5.2 Safety Considerations ______________________________________________________ 66

3.5.2.1 Compliance Supply-limited Pulses _______________________________________ 66 3.5.2.2 Optical Safety _______________________________________________________ 67

3.5.3 Maximum Charge Injection _________________________________________________ 68 3.5.3.1 SIROF _____________________________________________________________ 68 3.5.3.2 Platinum ___________________________________________________________ 70

3.5.4 Charge Injection vs. Stimulation Thresholds ____________________________________ 70

3.6 Conclusions ________________________________________________________ 71

CHAPTER 4: TOWARDS HIGH RESOLUTION ____________________________ 72

4.1 Current Density and Electrode Geometry _______________________________ 73 4.1.1 Constant Current Density vs. Constant Potential Boundary Conditions________________ 73 4.1.2 Return Electrode Placement _________________________________________________ 74

4.1.2.1 Direct Crosstalk _____________________________________________________ 76 4.1.2.2 Indirect Crosstalk ____________________________________________________ 76 4.1.2.3 Return Location and Load Resistance _____________________________________ 78 4.1.2.4 Comparing the Three Return Locations ___________________________________ 79

4.1.3 Simulated Frontal Return Electrode Geometries _________________________________ 79

4.2 Optical Limitations __________________________________________________ 82

4.3 Electrochemical Limitations __________________________________________ 84

4.4 Overall Pixel Density Limits __________________________________________ 84 4.4.1 Salamander Stimulation Threshold____________________________________________ 84 4.4.2 The Effect of Pixel Geometry ________________________________________________ 86 4.4.3 The Optimal Number of Photodiodes __________________________________________ 87

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4.5 Pillar Array Implants for Enhanced Electrode-Neuron Proximity ___________ 88

4.6 Conclusions ________________________________________________________ 89

CHAPTER 5: POLYMER RAT IMPLANTS ________________________________ 92

5.1 The Animal Model __________________________________________________ 92

5.2 Passive Implants ____________________________________________________ 93 5.2.1 Methods ________________________________________________________________ 94

5.2.1.1 Fabrication Process ___________________________________________________ 94 5.2.1.2 Implantation ________________________________________________________ 94

5.2.2 Histological Results _______________________________________________________ 95 5.2.2.1 Solid Implants _______________________________________________________ 95 5.2.2.2 Perforated Implants ___________________________________________________ 96

5.3 Tethered Implants __________________________________________________ 99 5.3.1 Introduction _____________________________________________________________ 99 5.3.2 The Tethered Implants ____________________________________________________ 100 5.3.3 The Recording/Stimulating Electronics _______________________________________ 101 5.3.4 Implantation Procedure ___________________________________________________ 103 5.3.5 Tethered Implant Results __________________________________________________ 105

5.4 Conclusions _______________________________________________________ 106

CHAPTER 6: MICROELECTRODE ARRAY TESTS _______________________ 108

6.1 The Microelectrode Array Experiment Setup ___________________________ 109 6.1.1 The Microelectrode Array _________________________________________________ 109 6.1.2 The Classical MEA Experiment ___________________________________________ 110 6.1.3 Modified MEA Experiment ________________________________________________ 112

6.1.3.1 Experiment Overview ________________________________________________ 112 6.1.3.2 Projection System ___________________________________________________ 112 6.1.3.3 Photodiode Array ___________________________________________________ 114

6.2 Rabbit Model _____________________________________________________ 115 6.2.1 Rabbit Experiment _______________________________________________________ 115 6.2.2 Stimulation Threshold ____________________________________________________ 115

6.3 Normally Sighted Rat Model _________________________________________ 117 6.3.1 Medium Latency Spikes ___________________________________________________ 117 6.3.2 Long Latency Spikes _____________________________________________________ 120 6.3.3 Pharmacology ___________________________________________________________ 121

6.4 RCS Rat Model of Retinal Degeneration _______________________________ 123

6.5 Conclusions _______________________________________________________ 124

CHAPTER 7: CONCLUSIONS AND FUTURE DIRECTONS ________________ 126

7.1 Future Multielectrode Array Experiments _____________________________ 127 7.1.1 Comparison of Visible and Electrical Stimuli __________________________________ 127 7.1.2 Electrical Stimulation at Various Degeneration Timepoints _______________________ 127

7.1.2.1 Older Degenerated Retinas ____________________________________________ 127 7.1.2.2 Previously Implanted Retinas __________________________________________ 128 7.1.2.3 New Surgical Challenges _____________________________________________ 128

7.2 In Vivo RCS Rat Tests ______________________________________________ 129 7.2.1 Superior Colliculus Recordings _____________________________________________ 129

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7.2.2 Remote Sensing of Electrical Function _______________________________________ 131

7.3 Engulfed Electrodes ______________________________________________ 132

7.4 Final Comments ___________________________________________________ 133

BIBLIOGRAPHY_____________________________________________________ 134

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LIST OF TABLES

Table 1.1. The evolution of the two activation-deactivation parameters n, m and the

inactivation parameter h is controlled by equation 1.9 with these

parameters. V is in units of mV. ................................................................. 17

Table 2.1. Optical power transmission characteristics are compared for (a) commercially

available video goggles and (b) the near-to-eye infrared projection system.

The infrared projection system transmits orders of magnitude more peak

power than the commercial goggles, and as a result must be much more

energy-efficient. For example, the commercial system uses a translucent

plastic homogenizer, while the infrared projection system uses a more

efficient (and costly) microlens array. ......................................................... 42

Table 4.1. Table of optoelectronic properties for three return electrode locations.

Crosstalk-limited dynamic range is the maximum multiple of the

stimulation threshold which may be produced by a pixel without stimulating

cells in front of neighboring pixels. Relative resistance is the tissue

access resistance, relative to the fabricated design (frontal return

electrodes). Absolute resistance values depend on the tissue response to a

subretinal implant, and will likely vary patient-to-patient and pixel-to-pixel

across the implant. ....................................................................................... 77

Table 4.2. Table of geometry-dependent optoelectronic properties. Crosstalk-limited

dynamic range is the largest multiple of the stimulation threshold which

may be applied without stimulating cells in front of neighboring pixels.

Relative resistance is the tissue access resistance, relative to the fabricated

geometry (geometry 3). ............................................................................... 82

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LIST OF FIGURES

Figure 1.1. A simplified diagram of the human eye. Light enters the eye through the

cornea, passing through the lens to be imaged onto the photosensitive

surface of the retina. The ciliary muscles can change the shape of the lens

to focus on objects at a large range of distances from the eye, in a process

known as accommodation. The retina collects the imaged light, processes

it, and converts it to a digital signal which is relayed to the brain via the

optic nerve. .................................................................................................... 3

Figure 1.2. A histological section showing a normal healthy mammalian retina, with the

individual cell layers marked. Light is incident from above, so that it

travels through the transparent retina before being sensed by the rods and

cones in the photoreceptor layer. The information provided by these

photoreceptors is subsequently processed by neurons in the inner retina, and

transmitted to the brain via the retinal ganglion cell axons, which stretch

unbroken all the way from the retina to the lateral geniculate nucleus in the

brain. The histological section is from a Dutch-belted rabbit. ..................... 4

Figure 1.3. Histological section of a Royal College of Surgeons rat retina, 60 days after

birth. The photoreceptors and RPE have all died due to a genetic defect in

the RPE. ......................................................................................................... 6

Figure 1.4. The same scene viewed by a person with (a) normal vision, (b) the tunnel

vision characteristic of retinitis pigmentosa, (c) central vision loss due to

age-related macular degeneration, (d) patchy vision loss and general

blurring from diabetic retinopathy, and (e) advanced vision loss due to

glaucoma. Retinitis pigmentosa and the dry form of age-related macular

degeneration are caused by photoreceptor death. Diabetic retinopathy is

caused by the growth of new retinal blood vessels which displace existing

neural architecture and sometimes leak and/or hemorrhage, causing a

further blurring of vision. Glaucoma is characterized by an increase in

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intraocular pressure which if left untreated can cause the death of retinal

ganglion cells, beginning with the periphery. ............................................... 7

Figure 1.5. An action potential is a very well characterized spike in the intracellular

voltage. It is triggered when an external neurotransmitter or electrical

stimulus causes the intracellular potential to rise to a threshold value, VT.

The cell quickly recovers to resting potential VR after either an action

potential, or subthreshold stimulus. Typically VR = -70 mV, and VT = -55

mV. .............................................................................................................. 10

Figure 1.6. A simple phenomenological model for subthreshold neuron cell dynamics.

Cm is the membrane capacitance, and Rm is the In the absence of a

stimulating current IS the intracellular potential is VR. .............................. 11

Figure 1.7. The equivalent circuit for the Weiss strength-duration relationship (equation

1.7). In this model, the subthreshold leakage current is modeled as an

on/off current source, rather than a battery in series with a resistor. .......... 13

Figure 1.8. The strength-duration relationship for intracellular stimulation as predicted

by the Lapicque (equation 1.5) and Weiss (equation 1.7) formulas. Both

predict an inverse time relationship for 1t and asymptotically approach

Irh for 1t . ............................................................................................... 14

Figure 1.9. A section of squid giant axon membrane in the Hodgkin-Huxley model. In

this model, an action potential is due to the transmembrane movement of

potassium, sodium, and leakage (mostly Chloride) ions. With relatively

minor modifications, the same model has since been used to describe

neuron dynamics throughout neuroscience. ................................................ 16

Figure 1.10. The ion channels can be thought of as having gatekeeper particles, which

must all be open to allow ion movement. (a) The potassium ion channel has

four n particles which control ion flow, while (b) the sodium channel has

three m activation particles and one h inactivation particle. ................ 17

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Figure 1.11. The equivalent circuit for a small section of passive neuron. The

membranes electrical properties are modeled by resistance rm and

capacitance cm, while ri is the internal resistance of the cell[24]. Since this

passive circuit is dissipative, non-spiking cells cannot transmit information

for distances much longer than a millimeter. .............................................. 19

Figure 1.12. Simplified system diagram. A portable computer processes video images

captured by a head-mounted camera. Video goggles then project these

images onto the retina using pulsed infrared (905 nm) illumination. Finally,

pixels in the subretinal photodiode array convert this light into local

stimulation currents. .................................................................................... 27

Figure 2.1. Schematic for a common near-to-eye projection system layout. An LED and

translucent white plastic diffuser illuminate a transmissive LCD panel. An

ocular creates an image of this panel at infinity, which is then viewed by the

eye. .............................................................................................................. 31

Figure 2.2. Both laser diodes and LEDs can produce relatively narrow-spectrum infrared

illumination. The coherent nature of laser light leads to speckling and

interference patterns which appear as high spatial-frequency intensity

modulation. This problem is absent from LEDs; however, they are unable

to produce the brightness achievable with laser diodes. ............................. 33

Figure 2.3. Two types of beam homogenizers/diffusers were investigated. (a) Microlens

arrays convert a collimated beam into an array of point-like sources. Light

from these sources is superimposed onto a single spot using a field lens.

(b) Uncollimated light entering a glass light pipe homogenizer

experiences several total internal reflections. The superposition of these

reflections creates a homogenous square spot at the end of the pipe which

may be imaged onto an imaging panel with a field lens. ............................ 35

Figure 2.4. Diagram of a transmissive liquid crystal display pixel. The voltage

difference across two transparent electrodes controls the degree of twist

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present in liquid crystal molecules at the center of each pixel. This twist

rotates the polarization of incident light, an effect which may combined

with the use of polarizing filters to create pixel-level control of transmitted

light intensity. Modified version of artwork originally by Marvin

Raaijmakers, and used according to the Creative Commons Attribution

ShareAlike 2.5 License (http://creativecommons.org/licenses/by-sa/2.5/). 36

Figure 2.5. A simplified diagram of a near-to-eye projection system utilizing a liquid

crystal on silicon (LCOS) panel. LCOS panels are reflective, and therefore

require more space to operate than transmissive LCD panels. However,

they produce greater light power efficiency. ............................................... 38

Figure 2.6. An eye tracking system combined with a moving field lens can be used to

increase field of view without additional illumination. Such a system

significantly reduces power requirements. .................................................. 40

Figure 2.7. A rendering a of a folded version of the near-to-eye projection system. The

fiber-coupled laser diodes are not shown as they are located with the image

processing computer inside the pocket. ....................................................... 42

Figure 3.1. Examples of current-voltage sweeps for (a) one, two, and three photodiodes

in series operating photovoltaically, (b) a single diode operating

photoconductively, and (c) again three photodiodes in series, with a shunt

resistor to increase conductivity during the light off, recharge phase. The

photocurrent Ip is proportional to the light power incident on each

photodiode. .................................................................................................. 49

Figure 3.2. Cyclic voltammograms of the 50 m platinum and 50 m SIROF disk

electrodes, taken in PBS at a sweep rate of 50 mV/s. The platinum

microelectrode had a charge injection of CSCC = 2.1 mC/cm2, while the

SIROF microelectrode had a CSCC of 57 mC/cm2. .................................... 55

Figure 3.3. Diagram of the experimental setup. Active and reference electrode potentials

are monitored while a laser diode pulses light onto the photocircuit with the

xviii

counter electrode connected to ground. A small series resistor measures

current, while a photo detector measures light power and beam shape. ..... 56

Figure 3.4. (a) Light power (b) current, and (c) active electrode voltage for 50 m

SIROF microelectrodes driven at 25 Hz by the photovoltaic circuit shown

inset in (a). Pulse dynamics can be understood by (d) plotting resistive load

lines on top of photocircuit I-V curves. All pulses begin at the same initial

condition 1. Their 1-2-3-4-1 cyclical movement in potential vs. current

phase space is confined to the photocircuit I-V curve. The plots in the

second column show (e) pulsed bias voltage, (f) current, and (g) active

electrode voltage for 50 m platinum microelectrodes driven at 25 Hz by

the photoconductive circuit show inset in (e), at the light intensities shown

in (a). Again, pulse dynamics can be understood as the 1-2-3-4-1 cyclical

movement in potential vs. current phase space (h). .................................... 58

Figure 3.5. Current as a function of light power per diode for 1-5 photovoltaic series

photodiodes and 1 photoconductive diode, measured at a 25 Hz pulse rate

with (a) a 50 m platinum electrode with cathodal current pulses (asterisks

show where the electrochemical safety limit was reached), and (b) a 50 m

SIROF electrode driven with anodal current pulses. (c) and (d) show the

total light power required for a desired stimulation current for each circuit,

with the optimal photovoltaic circuit (the circuit which minimizes necessary

light power for a given current) indicated by a colored bar beneath the

curves. 5 diodes is not shown in (c), as it is never optimal. Light-to-current

conversion behavior is qualitatively similar for both anodal and cathodal

polarities, and for both electrode materials. ................................................ 61

Figure 3.6. Maximum charge injection measured for 50 m (a) platinum and (b) SIROF

microelectrodes for 1-5 photodiodes in series driven photovoltaically at 25

Hz. In both cases the charge injection initially increases linearly with

increasing number of photodiodes due to an increase in the available

voltage. Series photodiodes slow the inter-pulse discharge of SIROF

xix

electrodes and can inhibit full utilization of their electrochemical charge

capacity. This can be corrected by inserting a shunt resistor in the circuit. A

shunt resistor does not improve a platinum circuit since its lower charge

capacity is completely discharged between the pulses. ............................... 63

Figure 3.7. Charge injection per pixel as a function of pixel density, with SIROF

electrodes. Charge injection per area is constant along these contours, with

the values shown. Photovoltaic contours are plotted for q0=0.5 mC/cm2 per

pixel (the value measured in this study) and for q0=0.1 mC/cm2 per pixel,

which corrects for the roughly ~5x increase in in vivo resistances [83]. 6

diodes is optimal for q0=0.5 mC/cm2 and 13 diodes is optimal for a

prosthesis with q0=0.1 mC/cm2. 3-diode pixels are currently manufactured

for photovoltaic implants[92]. Photoconductive pixels offer the highest

charge injections (7.6 mC/cm2), but require an external bias. .................... 69

Figure 4.1. The current density distributions which result from a distantly located return

electrode, a return electrode on the front of each pixel, and a return

electrode on the side and back of each electrode. Calculated for both (a)

constant current density active electrode boundary conditions and (b)

equipotential active electrode boundary conditions. Current density as a

function of axial electrode-neuron separation is plotted in (c) and (d) for

both boundary conditions. A frontally located return electrode gives the

best current confinement, while a distantly located return electrode give the

best tissue penetration. ................................................................................ 75

Figure 4.2. The normalized potential in front of a 3x3 array of pixels with (a) a distant

return electrode, (b) return electrodes located on the back and sides, and (c)

frontally placed returns. The eight outside pixels are shown activated, while

the central pixel remains inactive. The activated pixels raise the potential in

front of the central one, resulting in decreased stimulation current. This

effect can be reduced from a 40% decrease to 13% or 3% by placing return

electrodes on the sides and front, respectively. ........................................... 77

xx

Figure 4.3. Plot of the current from the central pixel of an NxN array of constant

potential electrodes as a function of N. The IR drop due to surrounding

pixels decreases the current from the central one, resulting in indirect

crosstalk. This effect is partially eliminated by introducing close, local

return electrodes which confine the stimulation current and therefore reduce

the spatial extent of the IR voltage drops. .................................................. 78

Figure 4.4. The three electrode geometries which were investigated, with their

numerically solved current density distribution for both constant current

density and equipotential electrode boundary conditions. (a) Geometry #1

was designed to maximize the current penetration. (b) Geometry #2 was

designed to maximize near-field current density at the expense of

photodiode area. (c) Geometry #3 is a compromise between maximizing

near-field current density and photodiode area fill factor. Geometry #3 was

chosen for fabrication. The displayed current density was saturated at 1.0

A/cm2 to better show the spatial extent of injected current. ........................ 80

Figure 4.5. Plots of axial current density relative to the average density on the

stimulating electrode for (a) constant current density and (b) constant

potential electrode boundary conditions. The constant potential curves have

less than 1.0 current density at z=0 because the edge effect shifts current

away from the electrode center. Simulations performed with Comsol. ..... 81

Figure 4.6. Strength duration curve measured for epiretinally stimulated salamander

retina. Based on this data, the chronaxie stimulation threshold is 6 A at 1

ms, for a total charge per phase of Qinj = 6 nC, or qt = 0.076 mC/cm2 over

the 100 m electrode. .................................................................................. 85

Figure 4.7. Maximum pixel density as a function of electrode-neuron separation for (a)

one-diode pixels with geometry 2, (b) three-diode pixels with geometry 2,

(c) one-diode pixels with geometry 3, and (d) three-diode pixels with

geometry 3. Geometry 1 has the highest single-diode pixel densities, and

delivers 55% more current at any given density. Geometry three has the

xxi

highest three-diode pixel density, and is the only system capable of

operating with a dynamic range of 10. Geometrically equivalent visual

acuity is shown on the right. ........................................................................ 86

Figure 4.8. Maximum pixel densities for one- to six-diode designs, calculated for the

fabricated geometry (geometry 3). The resolution of one, two, and three-

diode designs is limited by electrochemistry, while the resolution of four-

five- and six-diode designs is limited by available light. Three diodes is

optimal, with four diodes a close second. Equivalent geometric acuity is

shown on the right. Calculated for observed device responsivity R = 0.35

A/W. ............................................................................................................ 87

Figure 4.9. Electrically inactive polymer SU-8 implants were fabricated and implanted in

the subretinal space of RCS rats. Numerically calculated pixel current

distributions were overlaid on histological sections taken at 6 weeks post-

implantation. (a) Retinal histology of a flat implant in the subretinal space,

with the current distribution from a 115 mm pixel (pixels drawn on top). (b)

Histology of a pillar array implant, overlaid with the current distribution

from electrodes placed on the pillars. The pillars attain cellular scale

electrode-neuron proximity. Current density is saturated at 1.0 A/cm2 in

order to better show the spatial extend of current (the unsaturated maximum

is 1.6 A/cm2). ............................................................................................... 89

Figure 4.10. Two designs were chosen for fabrication, a (a) single diode pixel design,

with the lithography maskset shown in (b), and and a (c) three diode pixel

design, with the maskset shown in (d). ....................................................... 90

Figure 5.1. Histological sections of retina from (a) a normally-sighted Brown Norway

rat, and (b) a RCS rat, 60 days after birth. Though born with photoreceptors

and RPE, a genetic deficiency in the RCS rats RPE cells cause the

degeneration of these cell layers. Thus, the RCS rat is often studied as a

model of retinitis pigmentosa. ..................................................................... 93

xxii

Figure 5.2. The inset shows a head-on view of the tool as it would appear loaded with the

pillar implants previously described in Chapter 4. The blue shows the

implant itself, while the red shows the implantation tool flanges which hold

it for insertion. Tool designed by Phil Huie. .............................................. 95

Figure 5.3. Example of a histological section from a RCS rat retina implanted with a flat,

30 m SU-8 implant. The implant-neuron proximity varies significantly

along the length of the implant from almost no separation in the middle to

~30 mm separation towards the edges. ....................................................... 96

Figure 5.4. Current density distributions were simulated for flat pixels of 230 m, 115

m, and 62 m in size. All current density simulations have been overlaid

on a histological section of RCS rat retina with a flat subretinal implant 6-

weeks after surgery. ..................................................................................... 97

Figure 5.5. (a) Perforated implants of 125 m periodicity were fabricated from SU-8

polymer and implanted in the subretinal space of RCS rats. Six weeks after

implantation the implanted eyes were enucleated and (b) sectioned for

histology. The histological sections revealed that (c) cells tend to move

through large perforations into the space below the implant. ..................... 98

Figure 5.6. (a) Implants with three different electrode geometries were fabricated,

though all implants had the same overall dimentions (b). (c) Dozens of

implants were fabricated per wafer. With thanks to Alex Butterwick. ...... 100

Figure 5.7. (a) A saddlebag was designed to be worn by implanted RCS rats. This

saddlebag holds two three-volts batteries and a (b) microcontroller-based

circuit which is capable of both constant-voltage and constant-current

stimulation, as well as periodic impedance measurements. (c) An

implanted animal wearing the saddlebag. ................................................. 102

Figure 5.8. A diagram of the surgical procedure used for implanting tethered implants

and connecting them to an electronic stimulator worn in a saddlebag on the

back of the animal. (a) An incision is made in the back right shoulder. (b)

xxiii

A special tool is inserted into this incision and subcutaneously through to

the eye cup. An incision is made in the eye cup to allow the tool to pass

through, where it is tied to the implant wires. (c) The tool is pulled back

through the subcutaneous space, taking the wires with it. (d) The wires are

connected to the saddlebag, and the implant head inserted into the eye. .. 104

Figure 5.9. (a) Mechanical forces exerted by the tether result in a severe scar response,

which was not seen in (b) tetherless implants. This response caused a rise

in impedance. ............................................................................................ 105

Figure 6.1. (a) The classical microelectrode array experiment. A glass substrate

containing 512 platinum black electrodes spaced 60 m apart forms part of

the bottom of a sample dish. An explanted retina is placed on this array

with the ganglion cells facing the recording electrodes, which record neural

responses to visual stimuli delivered from below the transparent substrate.

(b) This setup has been modified to project infrared illumination instead of

visible. This infrared illumination is invisible to whatever photoreceptors

may or may not be present, but is converted into neural stimulation currents

by a photodiode array placed on top of the retina. The retinal response to

this stimulation is again read using the 512 recording electrodes. ............ 110

Figure 6.2. A cross section of the infrared experiment. The retina is sandwiched between

two electrode arrays. The bottom electrode array faces the ganglion cell

layer of the retina, and is fabricated on a transparent substrate. The upper

electrode array is from a photodiode array. Infrared illumination is

projected through the lower array and onto the photodiodes, which convert

it into electrical currents. These electrical currents stimulate the retinal

neurons. The stimulation signal is then read by reading ganglion cell spike

responses from the electrode array. ........................................................... 111

Figure 6.3. A diagram of an infrared projection system adapted to a camera port of an

inverted microscope. Like the system described in chapter 2, this

arrangement uses collimated laser illumination in conjunction with a

xxiv

microlens array and transmissive LCD display to produce bright, pulsed

infrared images. Unlike the near-to-eye system, these images are

significantly demagnified by a field lens/objective pair which form the

internal optics of the microscope. The result is high resolution control over

the patterned infrared stimulus presented to the sample dish. ................... 113

Figure 6.4. Scanning electron micrograph of the Arificial Silicon Retina (ASR) device

fabricated by Optobionics Corporation. The full implant is 2 mm in

diameter, and contains thousands of 25 m pixels. Each pixel contains a

central 10 m SIROF electrode. All pixels share a common large return

electrode on the back of the array, but are electrically insulated from each

other on the front. ...................................................................................... 114

Figure 6.5. (a) The voltage waveform recorded from one of the electrodes in the

microelectrode array. A large artifact from the light-induced stimulating

currents is followed by an action potential roughly 10 ms later. (b) The

peristimulus histogram of from 240 pulses shows two stimulated action

potentials with a 9-11 ms latency. ............................................................. 116

Figure 6.6. (a) A voltage waveform recorded from one electrode of the MEA, showing

an activated ganglion cell spike in the middle of the stimulus artifact from a

4 ms pulse of 905 nm light delivered at 2 Hz with 2.7 mW/mm2 peak

intensity. (b) The peristimulus time histogram from 400 such pulses,

showing two areas of activated spikes. (c) A mixed Gaussian fit with two

kernels gives a measure of the latency and jitter in each of these activated

events: 2.60.2 ms, and 7.40.8 ms. ......................................................... 118

Figure 6.7. (a) A voltage waveform showing an activated ganglion cell spike burst after

the stimulus artifact (4 ms, 2 Hz, 21.5 mW/mm2 pulses of 905 nm light) (b)

The peristimulus time histogram from 1000 such pulses, showing clear

long-latency burst structure. (c) A mixed Gaussian fit gives a measure of

the latency and jitter in each of these activated events: 14.50.5 ms,

16.80.6 ms, 19.30.6 ms, 21.80.7 ms, 24.50.8 ms, and 27.40.7 ms. 119

xxv

Figure 6.8. The structure of the stimulation response depended strongly one the duration

of the applied stimulus. For pulses of 21.5 mW/mm2, 905 nm light

delivered at 2 Hz the observed peristimulus time histogram distribution was

(a) Gaussian-shaped for 0.5 ms pulses, (b) a skewed shape reminiscent of a

lognormal distribution for 1 ms pulses, (c) a skewed distribution showing

bursting structure for 2 ms light pulses, and (d) a well-defined burst for 4

ms light pulses. .......................................................................................... 120

Figure 6.9. (a) A peristimulus time histogram of a neuron exhibiting both medium and

long-latency responses. (b) Both medium and long latency responses

disappear upon the addition of synaptic transmission blockers, indicating

that the ganglion cell stimulation is mediated by inner retinal neurons. (c)

The responses return after the synaptic blockers are washed out.............. 122

Figure 6.10. A picture of the RCS rat MEA experimental layout. The electrodes and

tracks of the MEA are visible, as is a shard of the ASR photodiode array

used to stimulate the retina. The retina itself is present but not visible as it

is transparent. The red square indicates the 115 m area illuminated by the

880 nm beam using 4 ms, 1.1 mW/mm2 peak power pulses delivered at 2

Hz. The red and black circles indicate the location of stimulated spikes read

from a single neuron in response to these pulses, with the diameter

proportional to the size of the spike detected. The largest spike measured

has been colored red and indicates the location of the cell soma; the other

relatively large black spots surrounding it and extending to the left indicate

the location of the dendrites and the axon, respectively............................ 124

Figure 7.1. A infrared image projection system designed to be attached to the camera

port of a standard slit lamp. This will allow the projection of patterns onto

photodiode arrays implanted in live subjects, facilitating in vivo tests of

implant function. ....................................................................................... 130

Figure 7.2. Measuring the deflection of a recording electrode placed on the cornea of an

implanted animal gives a waveform which is proportional to the current

xxvi

waveform. By measuring the slope of the light power vs. average

deflection on the linear region one may determine the constant of

proportionality, since the slope of the linear part of the light power vs.

stimulation current plot must equal the known diode responsivity R. ...... 131

1

CHAPTER 1: INTRODUCTION

Age-related blindness has become a critical issue as life expectancies continue to rise.

Age Related Macular Degeneration (AMD) is the leading cause of blindness in the

developed world, with an incidence of 1:500 in patients age 55-64, and 1:8 in patients

over 85[1]. Retinitis Pigmentosa (RP) is the leading cause of inherited blindness, and

occurs in about 1 in every 4000 births[2]. Both diseases are characterized by the

degeneration of the image capturing photoreceptor layer of the retina, while neurons in

the image processing inner retinal layers are relatively well preserved. AMD

progression can be delayed, but not prevented, while there is currently no effective

treatment for RP. Visual prostheses seek to restore visual sensation to patients suffering

from these diseases by electrically stimulating surviving nerve cells, in the visual analog

of the successful cochlear implant.

Although visual prosthesis research has been ongoing since the 1960s[3], the field has

greatly expanded since the late 1980s, with the creation of dozens of research groups

spanning the globe. This introductory chapter begins with a description of basic ocular

and retinal anatomy, followed by a phenomenological description of the disease


2

progression of AMD and RP. Next, a discussion of neuron dynamics is followed by a

survey of the visual prosthesis field which seeks to stimulate these neurons, with an in-

depth analysis of the data and power transmission techniques involved. Finally, the

Stanford Optoelectronic Retinal Prosthesis is introduced, and is the subject of the rest of

this thesis.

1.1 Introduction to the Visual System

The human visual system provides us with detailed optical information about our

environment. Our eyes constantly sense details about the color, size, shape, position, and

movement of our surroundings and the objects within. The eye is an extremely versatile

system, sensitive enough to detect single photons[4], yet still able to function well on a

sunny day. For the purposes of this introduction, the eyes visual system will be divided

into two parts: the eyes optical imaging system (consisting of the cornea, lens, and

retinal pigment epithelium), and the thin 100-230 m[5] layer of tissue known as the

retina which lines the back of the eye. This section discusses in detail the anatomy and

function of each of these two parts.

1.1.1 The Eye as an Imaging System

Figure 1 depicts a cross section of the eye. Light enters the eye through the cornea,

which is the most refractive imaging element in the eye, with an optical power of

approximately 43 diopters[6]. Light then passes through the anterior chamber of the eye,

before passing through the lens. When relaxed, the lens has an optical power

approximately 18 diopters. This optical power can be changed by the ciliary muscles,

which can expand and contract to physically change the shape of the lens in a process

known as accommodation. Accommodation can alter the eyes optical power by as much

as 15 diopters in healthy young individuals, allowing the eye to focus on objects both

near and far. After passing through the lens, the light travels through the posterior

chamber, which is filled with a clear, egg-white consistency fluid known as the vitreous


3

Figure 1.1. A simplified diagram of the human eye. Light enters the eye through the

cornea, passing through the lens to be imaged onto the photosensitive surface of the

retina. The ciliary muscles can change the shape of the lens to focus on objects at a large

range of distances from the eye, in a process known as accommodation. The retina

collects the imaged light, processes it, and converts it to a digital signal which is relayed

to the brain via the optic nerve.

humor. Finally, the light creates an image on the retina, a thin layer of tissue covering the

posterior 2/3 of the eyes interior surface.

1.1.2 The Retina

The retina is a thin layer of nerve tissue consisting of several sublayers. Figure 1.2 shows

a histological section of the retina. The outside of the retina is demarcated by the retinal

pigment epithelium (RPE) cell layer. The heavily pigmented cells in this layer create a

black background for the eyes imaging system, reducing light scattering and thereby

improving image contrast. In addition to their optical function, they also serve the

important biological function of delivering nutrients to the neighboring photoreceptors,

which are by weight and volume among the most metabolically active cells in the human

body[7]. Detaching the photoreceptor layer from the RPE for even a few hours results in


4

Figure 1.2. A histological section showing a normal healthy mammalian retina, with the

individual cell layers marked. Light is incident from above, so that it travels through the

transparent retina before being sensed by the rods and cones in the photoreceptor layer.

The information provided by these photoreceptors is subsequently processed by neurons

in the inner retina, and transmitted to the brain via the retinal ganglion cell axons, which

stretch unbroken all the way from the retina to the lateral geniculate nucleus in the brain.

The histological section is from a Dutch-belted rabbit.

photoreceptor death and blindness[8]. In humans retinal vasculature also provides some

nutrients; however, blood vessels are absent in some species of mammals, such as rabbits.

The photoreceptor layer actually senses the incoming light. Two types of photoreceptors

are found in this layer: rods and cones. Rods are more sensitive than cones, and are the

primary source of visual information in low-light conditions, such as at night or in

darkened rooms. However, they cannot sense color. Cones require much more light

input, but are able to encode color. On a sunny day the rods are saturated, and provide

little useful visual information; cones dominate the visual input. As light levels are

inner plexiform

layer

ganglion cell

layer

inner nuclear

layer

outer plexiform

layer

outer nuclear

layer

photoreceptor

layerretinal pigment

epithelium

synapse

synapse

light

neu

ral

pro

pag

atio

n

axonal transmission to visual cortex via the optic nerve

50 m


5

decreased there comes a point where cones no longer receive enough light to adequately

discern color, and rods dominate the visual input. For example, moonlight can often

provide enough illumination to avoid obstacles at night, but insufficient light to

distinguish the colors of the obstacles. For centuries it was believed that rods and cones

were the only photosensitive cells present in the retina. However, a third type of

photosensitive cell was discovered in the 1990s[9, 10]: photosensitive ganglion cells.

These cells are located in the ganglion cell layer, and are non-imaging cells which help

control body and brain functions such as the circadian rhythm. Since retinal prostheses

seek to restore the imaging capabilities of the retina, these non-imaging light pathways

will be neglected throughout the rest of this thesis. Other than these three cell types, all

other cells are believed intrinsically insensitive to incoming light.

Signals detected in the photoreceptor propagate through the retina, away from the RPE

and towards the ganglion cells (upwards in Figure 1.2). Each photoreceptor connects to a

cell body located in a layer the outer nuclear layer. These cell bodies connect

synaptically to neurons in the inner nuclear layer, known as bipolar cells. The area where

axon terminals from the outer nuclear layer meet the bipolar cell dendrites from the inner

nuclear layer is known as the outer plexiform layer. The area where axon terminals

from the inner nuclear layer meet dendrites from the ganglion cell layer is known as the

inner plexiform layer. In addition to these bipolar-ganglion cell connections, the inner

plexiform layer also contains horizontally and vertically oriented amacrine cells which

further integrate signals sent to and from ganglion cells. The ganglion cells have long,

unbroken axons which stretch all the way to neurons in the lateral geniculate nucleus

(LGN) in the brain. From the LGN visual stimuli are relayed to the primary visual

cortex.

Since there are about 1.2 - 1.5 million retinal ganglion cells and a little over 100 million

photoreceptors in human retina[11], each retinal ganglion must transmit information from

an average of 100 rods and cones. This image processing and compression is


6

accomplished by the synaptic connections in the OPL and INL, and to this day is still not

fully understood.

1.2 Retinal Degenerative Diseases

There are many blindness-causing diseases characterized by the progressive death and/or

disruption of retinal cells. Some of the more common ones include retinitis pigmentosa,

age-related macular degeneration, diabetic retinopathy, and glaucoma. Figure 1.4 shows

how the same scene would appear to patients suffering from these diseases.

Retinitis pigmentosa (RP) is a general name for a large number of genetic disorders

which cause the progressive death of photoreceptors. This death starts from the periphery

of the visual field, and gradually works its way towards the center of the field of view, so

that in advanced disease states patients experience tunnel vision (Figure 1.4b) or even are

born fully sighted, but progressively lose their photoreceptors due to a genetic defect in

Figure 1.3. Histological section of a Royal College of Surgeons rat retina, 60 days after

birth. The photoreceptors and RPE have all died due to a genetic defect in the RPE.

ligh

t

inner plexiform

layer

ganglion cell

layer

inner nuclear

layer

choroid

50 m


7

Figure 1.4. The same scene viewed by a person with (a) normal vision, (b) the tunnel

vision characteristic of retinitis pigmentosa, (c) central vision loss due to age-related

macular degeneration, (d) patchy vision loss and general blurring from diabetic

retinopathy, and (e) advanced vision loss due to glaucoma. Retinitis pigmentosa and the

dry form of age-related macular degeneration are caused by photoreceptor death.

Diabetic retinopathy is caused by the growth of new retinal blood vessels which displace

existing neural architecture and sometimes leak and/or hemorrhage, causing a further

blurring of vision. Glaucoma is characterized by an increase in intraocular pressure

which if left untreated can cause the death of retinal ganglion cells, beginning with the

periphery.1

1 Pictures created by the National Institute of Health, and as such are part of the public domain

(a)

(c)

(d)

(b)

(e)


8

their RPE cells. They are fully blind by 45-60 days post-natal. This animal model will

be used extensively in this thesis.

Age-related macular degeneration (AMD) is also characterized by the progressive death

of photoreceptors, but begins in the center of the field-of-view (known at the macula) and

moves towards the periphery (Figure 1.4c). It causes one of the most common forms of

blindness in the developed world, affecting 1:500 in patients age 55-64, and 1:8 in

patients over 85[1]. There are two forms of AMD: wet and dry. Wet AMD is

characterized by the growth of new blood vessels beneath the macula; it is the growth,

leakage, and scarring of these blood vessels which causes eventual blindness. Dry AMD

is characterized by the atrophy of the retinal pigment epithelium cells beneath the

photoreceptors, which quickly leads to deterioration of the rods and cones. Without the

RPE, the photoreceptors quickly die. However, like RP, the dry form of AMD preserves

some of the inner retinal architecture.

Diabetic retinopathy, like wet AMD, is characterized by the formation of new blood

vessels in the retina. However, unlike AMD the blood vessels do not necessarily form in

the macula, but tend to sprout in random locations. The presence of these vessels

displaces normal retinal circuitry and creates large blind spots in the visual field (Figure

1.4d). In addition, the new vessels tend to leak blood and sometime hemorrhage, adding

pigment to the vitreous cavity and causing vision to become blurred. Diabetic

retinopathy is present in 80% of patients who have had diabetes for 10 years or more[12].

Glaucoma is characterized by high intraocular blood pressure (IOP), with the threshold

commonly placed at 21 mmHg[13]. This elevated pressure can lead to ganglion cell

death and optic nerve damage. Ganglion cells typically die starting at the periphery, with

loss of vision progressing inwards over time, as shown in Figure 1.4e. Glaucoma affects

approximately 2% of the population aged 18 and above[14].


9

1.3 Nerve cells

Visual prostheses seek to restore degenerated vision by stimulating surviving nerve cells.

Their ability to do so hinges on the understanding of normal neural function. This section

will discuss the physical principles underlying the operation of individual nerve cells,

from a mostly phenomenological standpoint.

As described in section 1.1.2, the retina contains many different types of neurons, each

with its own specialized function. However, there are only two general types of signal

transmission present in the retina: analog and digital. Rods, cones, bipolar, and some

amacrine cells are all non-spiking cells: their output is analog in nature. Retinal ganglion

cells and some amacrine cells are spiking cells: their output is digital in nature.

Historically, spiking cells were the first to be understood by neuroscientists, so they will

be the first discussed in this section.

1.3.1 Spiking Nerve Cells

A nerve cell membrane is a nonconducting lipid bilayer, electrically separating the

interior of a cell from the extracellular electrolyte surrounding it. The potential of the cell

interior is called the intracellular potential Vi, where the potential at infinity is taken as 0.

The membrane contains many pores which open/close based on the presence/absence of

neurotransmitter molecules and the trans-membrane potential drop. The pores are

passively acting devices: when open, they create channels through the cell membrane

which allow ion diffusion. When closed, they prevent this diffusion. In addition to

passive pores, the cell membrane contains large transmembrane proteins which actively

transport ions across the cell membrane, called ion pumps. These pumps accept energy

from ATP molecules and use this energy to transport ions across concentration gradients,

and can create ion concentration differences of many orders of magnitude across the cell

membrane. In an equilibrium state, almost all of the pores are closed, and these pumps

maintain a gradient that generates a polarized state with 70iV mV [15] where the ion


10

Figure 1.5. An action potential is a very well characterized spike in the intracellular

voltage. It is triggered when an external neurotransmitter or electrical stimulus causes the

intracellular potential to rise to a threshold value, VT. The cell quickly recovers to resting

potential VR after either an action potential, or subthreshold stimulus. Typically VR = -70

mV, and VT = -55 mV.

flow through pumps is exactly counteracted by the flow through the pores which remain

open.

Retinal ganglion cells, along with most of the bodys nerve cells, are spiking cells. When

a spiking nerve cell is sufficiently stimulated (what constitutes a sufficient stimulus

will be discussed shortly) it responds with an action potential, a very well-characterized

cascade of ion flows which result in a brief, ~1 ms rise in intracellular potential. After

this potential spike, the cell returns to equilibrium, with a brief hyperpolarizing potential

overshoot. Figure 1.5 shows a simplified diagram of an action potential. The simplest

neural model defines two regions of cell operation: active, and passive. The two regions

subthreshold stimuli

action potential

intr

acel

lula

r p

ote

nti

al

time, ms

threshold, VT

resting potential, VR

0 1 2 3-1

0


11

are separated by the threshold voltage, VT, usually taken as approximately 15 mV greater

than equilibrium, VT = -55 mV. Below the threshold, the cell behaves passively; once

threshold is reached then active cell dynamics take over, and the cell produces an action

potential.

1.3.1.1 Strength-Duration Relationship

In sub-threshold operation, the nerve cell may be modeled as the RC circuit shown in

Figure 1.6. The capacitor C represents the capacitance of the cell membrane, which is

approximately 1 mF/cm2[15]. The resistor R represents the action of the pumps to return

the cell to the equilibrium potential represented by the battery in parallel with the RC

circuit. IS represents an intracellular stimulation current. Such a current can be applied

via a pulled pipette tip which pierces the cell membrane, thereby giving direct electrical

contact to the cell interior. Classically, most early neuroscience research, including the

Figure 1.6. A simple phenomenological model for subthreshold neuron cell dynamics.

Cm is the membrane capacitance, and Rm is the In the absence of a stimulating current IS

the intracellular potential is VR.

Extracellular Medium

Intracellular Medium

Cm

Rm

IS

VR

IR ICVi

-

+


12

Hodgkin-Huxley experiments[15] which yielded the famous model described later,

wasdone by inserting thin wires into the squid giant axon to create such direct

intracellular stimulation.

Action potentials are all-or-nothing responses. Stimuli which fail to bring the

intracellular potential to threshold cause voltage displacements which are quickly

dissipated. Assuming a monophasic current pulse input, the current necessary to bring

the intracellular potential from rest to threshold can be calculated by solving the system

of equations implied by Figure 1.6:

CRS III (Eq. 1.1),

Ci

m Idt

dVC (Eq. 1.2),

RmRi IRVV (Eq. 1.3).

Combining these equations and integrating to find the threshold current Ith required to

bring the potential to threshold VT in time yields

mmCR

mRTth

e

RVVI

1

/)( (Eq. 1.4),

or if we define rheobase current mRTrh RVVI /)( and chronaxie time

)2ln(/mmch CR then this becomes

ch

rhth

II

21

(Eq. 1.5),

which is known as the Lapicque equation, after the French scientist who first carried out

this derivation in 1907[16].

The Lapicque equation is one example of what is known as the strength-duration

relationship. This relationship depends on the geometry and type of both the neuron and

stimulus, and on the assumptions. For example, the ion channels are not well-modeled

by this linear battery and resistor leakage current[17]. As ion pumps carry fixed ratios


13

Figure 1.7. The equivalent circuit for the Weiss strength-duration relationship (equation

1.7). In this model, the subthreshold leakage current is modeled as an on/off current

source, rather than a battery in series with a resistor.

of ions across the membrane and depend primarily on the voltage independent supply of

ATP for their function[18], a voltage-insensitive current-source leak can be more useful

model. A leakage current given by

Ri

Ripump

PVV

VVII

,0

, (Eq. 1.6)

with the equivalent circuit shown in Figure 1.7 results in the Weiss equation[19] strength-

duration relationship:

chrhth II 1 (Eq. 1.7)

where pumprh II and pump

RTch

I

VVC )( . This is plotted in Figure 1.8, along with the

Lapicque equation for comparison. Both give qualitatively similar curves, with

stimulation thresholds asymptotically approaching the rheobase current as t approaches



CmISVi

-

+

Ri

Ripump

PVV

VVII

,0

,

IP


14

Figure 1.8. The strength-duration relationship for intracellular stimulation as predicted

by the Lapicque (equation 1.5) and Weiss (equation 1.7) formulas. Both predict an

inverse time relationship for 1t and asymptotically approach Irh for 1t .

infinity. Both also predict a t-1

dependence (slope of -1 in the log-log plot) for times

much less than chronaxie. However, they differ slightly between these limiting cases. In

experiments, intracellular stimulation thresholds fall somewhere between these two

models[17], with the Weiss equation generally providing a better fit to the data[20, 21].

While intracellular stimulation is rather well-understood, there are few applications of it

outside a laboratory setting. It is relatively difficult to build an apparatus which

chronically pierces a cells membrane to deliver stimulation currents; it is much easier to

simply send electric current through bulk tissue to stimulate cells extracellularly. It is

this approach which is taken by medical devices such as pacemakers, deep brain

stimulators, and cochlear implants.

10-2

10-1

100

101

102

10-1

100

101

102

Irh

10Irh

100Irh

ch ch ch ch

curr

ent

time

Weiss

Lapicque


15

Extracellular stimulation is significantly more difficult to model. Briefly, unlike

intracellular stimulation currents, extracellular stimulation currents do not directly charge

the intracellular space. Rather, they create a potential gradient outside the cell, so that

some parts of the cell become depolarized and other parts become hyperpolarized.

Which parts of the cell become polarized/hyperpolarized depend strongly on the

geometry of both the cell and the stimulating electrodes[22], and also on the ion channel

kinetics.

1.3.1.2 Ion Channel Kinetics

The 1963 Nobel Prize in Physiology or Medicine was awarded to Alan Hodgkin and

Andrew Huxley for their model of the squid giant axon action potential[15]. The same

model (with a few modifications) has since been used to describe action potentials in all

other nerve cells, including retinal ganglion cells.

In the Hodgkin-Huxley model, ion pumps create large ion differences between the

intracellular concentration Ci and extracellular concentration Co. This concentration

difference creates a chemical potential across the membrane, which can be calculated via

the Nernst Equation[23]:

i

o

C

C

zF

RTE ln (Eq. 1.8)

where R is the gas constant, T is temperature, z is the valence of the ion, and F is the

Faraday constant. The non-pump current through a cell membrane is modeled as the sum

of currents through potassium, sodium, and leakage (representing flow due to all other

ions) channels, along with the current from the charging and discharging of the

membrane capacitance C:

Cdt

dVVEgVEhmgVEngI LLNaNaKKm )()()(

34 (Eq. 1.9).

As shown in Figure 1.9, the ion channels are modeled as Ohmic with conductivities

4ngK , hmgNa3 , and Lg for the potassium, sodium, and leakage channels respectively.


16

Figure 1.9. A section of squid giant axon membrane in the Hodgkin-Huxley model. In

this model, an action potential is due to the transmembrane movement of potassium,

sodium, and leakage (mostly Chloride) ions. With relatively minor modifications, the

same model has since been used to describe neuron dynamics throughout neuroscience.

The batteries driving these conductances are simply the Nernst potentials for each ion.

The n, m, and h variables are functions of both time and voltage, and vary between 0 and

1 to modulate the maximum conductances Kg and Nag . Their behavior is determined

by the equation

xxdt

dxxx )1( , for x = m, h, n (Eq. 1.9)

where the s and s are functions of voltage as given in Table 1.1. The variables m and n

are known as activation parameters. A useful (though not completely accurate) mental

picture is provided by thinking about these parameters as describing the probability that

individual gatekeepers are open, as shown in Figure 1.10. The h parameter is an

inactivation parameter, and describes a gatekeeper which is likely to be open at



Cm

VL

IL ICVi

-

+

VNa

INa

VK

IK

4ngK hmgNa3

Lg


17

Parameter Ion Channel Type Formula for Formula for

n Potassium activation-

deactivation 1

6001.0

610

V

e

V

8

7

80125.0

V

e

m Sodium activation-

deactivation 1

451.0

5.410

V

e

V

9

35

184

V

e

h Sodium inactivation 5.32007.0

V

e 1

1

410

V

e

Table 1.1. The evolution of the two activation-deactivation parameters n, m and the

inactivation parameter h is controlled by equation 1.9 with these parameters. V is in units

of mV.

Figure 1.10. The ion channels can be thought of as having gatekeeper particles, which

must all be open to allow ion movement. (a) The potassium ion channel has four n

particles which control ion flow, while (b) the sodium channel has three m activation

particles and one h inactivation particle.

(a)

closed state openclosed state

(b)


18

equilibrium, but closes when the cell depolarizes. It is the behavior of these gatekeepers

which explains the shape of the action potential.

At equilibrium (Vi = -70 mV), most potassium and sodium channels are closed, and the

equilibrium mostly maintained by the leakage channel and ion pumps. In this state, the m

and n gatekeepers are mostly closed, while the h particles are mostly open. When the cell

is stimulated to threshold the m particles are first to react, opening and allowing Na+ ions

to rush in. This sodium influx raises the intracellular potential, and is responsible for the

spiking action potential behavior. As the potential rises, the n particles begin to open,

counteracting the Na+ inflow with K

+ outflow; simultaneously the slow h particles begin

to close, slowing the Na+

influx and allowing the cell to return to resting potential.

1.3.2 Non-spiking nerve cells

Not all neurons have this spiking behavior. In particular, the bipolar cells in the inner

nuclear layer are non-spiking cells. As these cells are the primary targets of subretinal

stimulating arrays, their behavior is of particular importance. Whereas spiking cells

actively propagate action potentials, non-spiking cells are modeled as passive cables.

Figure 1.11 shows the equivalent circuit for a section of neuron in this model. Since non-

spiking cells are passive and inherently dissipative from both internal and membrane

resistances, they can only effectively transmit information over distances less than

approximately one millimeter.

1.4 Existing Prosthesis Designs

Section 1.4 is reprinted from Delivery of Information and Power to the Implant,

Integration of the Electrode Array with the Retina, and Safety of Chronic Stimulation. J.

Loudin, A. Butterwick, P. Huie, and D. Palanker. Chapter 7 in VISUAL PROSTHETICS:

Physiology, Bioengineering, Rehabilitation. G. Dagnelie (Editor), Springer 2010.

Reprinted with the kind permission of Springer Science and Business Media.


19

Figure 1.11. The equivalent circuit for a small section of passive neuron. The

membranes electrical properties are modeled by resistance rm and capacitance cm, while

ri is the internal resistance of the cell[24]. Since this passive circuit is dissipative, non-

spiking cells cannot transmit information for distances much longer than a millimeter.

One of the fundamental challenges for a visual prosthesis is to efficiently deliver visual

stimuli from the external world to target neurons in the retina, optic nerve, or visual

cortex. Power and visual information must be transmitted and subsequently distributed

over an electrode array while ideally not interfering with residual vision, and keeping the

natural association between visual information and eye movements. Four basic methods

have been used to achieve this: direct wireline connection to implanted stimulators, radio

frequency (RF) telemetry, serial optical telemetry, and parallel optical telemetry.

1.4.1 Wireline Connection

William Dobelle led one of the earliest attempts at constructing a visual prosthesis. In a

series of studies begun in 1968, he used direct wireline connections to link electrodes

placed in the visual cortex with a stimulator worn externally to the body[25], a device

known as the Dobelle Eye. Subsequent electrical stimulation successfully evoked

Intracellular Potential, Vin(x)

cmVi

-

+

cmrm rm

ri

Extracellular Potential, Vout(x)

x


20

visual responses in nineteen blind patients, offering hope that future prostheses would

one day restore some degree of useful sight.

Direct percutaneous connections are far from ideal, as they can provide pathogens with a

direct pathway through the skin and are prone to severe scarring[26]. Despite this,

transdermal cables have often been used in short-term human trials of various visual

prostheses[27-30], because of the unrivalled electrical versatility which they offer. For

example, a group at the Naval Research Lab has developed a 3200 electrode epiretinal

prosthesis which is driven with a cable containing 10 wires[31]. This prosthesis is

intended for acute experiments; a future version under development is wireless. In at

least one case, percutaneous cables driving a retinal prosthesis have been left in place for

a period exceeding one year[30]. Though direct connections will likely continue to be

used in research settings for years to come, any future commercial prosthesis will be

wireless.

1.4.2 Inductive Coils

Inductively coupled coils are used for wireless data and power transmission in a wide

variety of applications, including medical implants such as cardiac pacemakers[32] and

cochlear prostheses[33]. More recently, the unique power and data requirements of

visual prostheses have spurred much research in the field, with inductive coil systems

currently developed for epiretinal[34, 35], subretinal[36, 37], visual cortex[38], and optic

nerve stimulators[29].

In all of these designs, an AC current driven through an external transmitting coil induces

an AC voltage on an implanted coil, which is converted to DC power by implanted

circuitry. Sometimes the transmitter encodes data onto this signal, which is also

recovered by the implanted circuitry. Since the coils are only weakly coupled to each

other (typical values for coupling coefficient k are in the range 0.08-0.24[39], compared

to ~0.9 for standard transformers), great care must be taken to optimize the receiving


21

circuitry. With this in mind, a capacitor is added in series with the receiving coil to

create a tuned resonance at the transmitter frequency, tf . The resulting circuit amplifies

the received voltage by the quality factor Q , typically in the range 10-100. High Q

values yield more efficient power transfer thus helping to decrease the bodys exposure to

radiation. The optimization of coil geometry and receiving circuitry to maximize Q has

been the subject of numerous studies[39-47]. Since Q is proportional to the transmission

frequency, high frequency operation yields higher Q values; however, tissues RF

absorption increases exponentially beyond a few MHz[48] limiting transmission

frequency tf to 1-10 MHz.

Inductive coils have been used to deliver data to visual prostheses for over half a century.

In the 1960s, a team led by Giles Brindley of the Medical Research Council in London

implanted an array of 80 coils beneath the pericranium of a blind patient[3]. The 80 coils

were connected through separate rectifying circuits[49] to 80 platinum electrodes placed

onto the surface of the patients visual cortex. Individual electrodes were activated by

placing a transmitting coil on the scalp directly above the electrodes receiver.

Interference was minimized by tuning adjacent receivers to different frequencies.

Though this scheme was rather successful (of the 80 electrode placements, 39 elicited

phosphenes), it is hardly scalable. With the goal of scaling visual prostheses to hundreds

and eventually thousands of pixels, higher data rates must be extracted from fewer coils.

Ironically, while high- Q coils are efficient power receivers, they are rather poor data

receivers. According to the Shannon-Hartley theorem[50], the data capacity C of a coil

may be expressed as

SNRQ

fSNRBC t 1log1log 22 (1)

where C is in bits per second, B is the bandwidth of the receiving circuit, tf is the

transmission frequency in Hz, and SNR is the signal to noise power ratio. Thus, while


22

received power is directly proportional to coil Q , the attainable data rate is inversely

proportional to Q . For this reason, many visual prosthesis designs use two coil pairs:

one for power, and one for data, where data transmission is accomplished at a higher

frequency[39, 42] or with a lower- Q coil[36]. In addition, complex single-coil systems

capable of delivering both power and data over one coil pair have also been

developed[40, 51, 52], in one case achieving a data rate in excess of 1 Mb/s[34].

Ignoring the time involved in implant monitoring feedback signals, transmitting control

signals, and other housek

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