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Fig. 7.10 Dependence of the chronic retinal damage threshold on pulse duration measured with pipettes of 0.12 (●) and 1.0 mm (○) in diameter. For comparison, we plot stimulation thresholds of the retinal ganglion cells measured by [44] using disk electrodes of similar sizes: 0.12 (+) and 0.5 (×) mm in diameter. Ratios of the damage thresholds to the stimulation thresholds are shown in the insert for both electrodes. Figure reprinted from [10], with permission; © 2009 IEEE
stimulation (the ratio of the damage threshold to stimulation threshold) as a function of pulse duration for both electrodes. The maximum (on the order of 100) of these curves occur near chronaxie for both electrode sizes. It is important to note that although the damage and the stimulation thresholds are dependent on electrode size, their ratio, which determines the dynamic range of safe stimulation, appears to be practically size independent.
Comparison of the recent measurements of the stimulation threshold in humans (electrode size 0.4 mm, 1 ms, 0.01 A/cm2) [36] and the in vivo damage threshold in rabbits (electrode size 0.4 mm, 1 ms, 0.46 A/cm2) [71] results in a slightly lower ratio, 46. A safe dynamic range of 50–100 is sufficiently broad to cover the linear response range of neural cells (typically 10–30 [5, 73]), and is therefore adequate for the purpose of prosthetic vision.
7.5 Concluding Remarks
The development and testing of retinal prostheses by multiple groups throughout the world is rapidly advancing. The delivery of a vast amount of information and sufficient power to the retinal neurons has proved to be technically challenging, and has required the development of new technologies in many disparate fields. Sophisticated coil systems have been developed to transmit and receive power and data; others have developed novel optical approaches for serial and parallel data
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delivery. In both approaches care has been taken to avoid thermal damage to surrounding tissues in the process of power transmission.
Once received by the implanted prosthesis, power and data must be delivered to target neurons, a task which requires close neuron-electrode proximity. Many materials have been tested to characterize tissue response to the implanted devices. In addition, three-dimensional subretinal arrays have been developed to utilize retinal plasticity to achieve intimate proximity between neurons and stimulation sites. Finally, electrical damage thresholds have been carefully measured to characterize the safe dynamic range of stimulation.
Despite the incredible advancements made in recent decades, there is much left to be done. This includes implementation of already proposed ideas, and improvements to the currently used approaches. Higher resolution implants will allow for more sophisticated evaluation of prosthetic vision and will most probably generate a need for development of more advanced signal processing algorithms. The past two decades of research have been very fruitful – several prosthetic technologies are currently being tested in human trials [6, 11, 21, 53]. The results from the current trials are eagerly awaited by researchers around the world, as they will likely dictate the direction of technological development for the next decade.
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Chapter 8
Retinal Cell Excitation Modeling
Carlos J. Cela and Gianluca Lazzi
Abstract As the electrode density of implantable retinal prosthesis increases, simulation becomes a valuable tool to characterize excitation performance, evaluate implant electrical safety, determine optimal geometry and placement of implant current return, and understand charge distribution due to stimulation. To gain an insight into the effectiveness of a retina stimulator, quasi-static numerical electromagnetic methods can help estimate current densities, potentials, and their gradients in retinal layers and neural cells. Detailed discrete three-dimensional models of the retina, implant and surrounding tissue can be developed to account for the anatomical complexity of the human eye and appropriate dielectric properties. This chapter will cover the basics of quasi-static methods that can be used for this purpose. Specifically, authors will focus on the admittance method, the output it produces, and possibilities it offers to determine the potential effectiveness of a retinal stimulator, ranging from evaluating the current density magnitude in the ganglion cell layer, to calculating local activation function in the areas targeted by the electrical stimulation.
Abbreviations
GCL |
Ganglion cell layer |
NFL |
Nerve fiber layer |
SAR |
Specific absorption rate |
C.J. Cela (*)
Department of Electrical and Computer Engineering, University of Utah,
50 S. Central Campus Drive, Room 3280, Salt Lake City, UT 84112-9206, USA e-mail: carlos.cela@utah.edu
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DOI 10.1007/978-1-4419-0754-7_8, © Springer Science+Business Media, LLC 2011 |
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