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Review
. 2015 Sep 19;370(1677):20140208.
doi: 10.1098/rstb.2014.0208.

Pulse trains to percepts: the challenge of creating a perceptually intelligible world with sight recovery technologies

Ione Fine  1 , Geoffrey M Boynton  2
Affiliations
Review

Pulse trains to percepts: the challenge of creating a perceptually intelligible world with sight recovery technologies

Ione Fine et al. Philos Trans R Soc Lond B Biol Sci. .

Abstract

An extraordinary variety of sight recovery therapies are either about to begin clinical trials, have begun clinical trials, or are currently being implanted in patients. However, as yet we have little insight into the perceptual experience likely to be produced by these implants. This review focuses on methodologies, such as optogenetics, small molecule photoswitches and electrical prostheses, which use artificial stimulation of the retina to elicit percepts. For each of these technologies, the interplay between the stimulating technology and the underlying neurophysiology is likely to result in distortions of the perceptual experience. Here, we describe some of these potential distortions and discuss how they might be minimized either through changes in the encoding model or through cortical plasticity.

Keywords: macular degeneration; neural coding; neural prosthetic; optogenetic; retinal degeneration; vision restoration.

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Figures

Figure 1.
Figure 1.
A simulation of the perceptual information carried by ON- and OFF-centre pathways. (a) Two example images: a white-on-black eye-chart and a frame of a natural scene movie of a child scooting. Both subtend 24° of visual angle. (b) These two images were convolved by a bank of difference of Gaussian filters followed by rectification to provide a rough approximation of the information carried by ON-centre pathways. (c) Images filtered by an inverted version of the same bank of Gaussian filters followed by rectification provide a rough approximation of the information carried by OFF-centre pathways. (d) The difference of the images (b,c) restore a band-pass version of the original image. (e) The sum of the images (b,c) represents an approximation of one potential perceptual outcome of simultaneously stimulating both ON-centre and OFF-centre pathways. Movie versions are shown for (a–e) in the electronic supplementary material.
Figure 2.
Figure 2.
An example of how a model of retinal axonal pathways can predict patient percepts. (a) Schematic of why axonal stimulation results in axon comets. Each image of the retina is flipped so that the upper region of the retina represents the upper visual field. The modelled axon trajectories (green lines) are based on a computational model of axon fibre trajectories developed using traced nerve fibre bundle trajectories extracted from fundus photographs of 55 human subjects [50]. The red circles represent ganglion cell bodies whose axon fibres pass underneath the electrode on their way to the optic nerve, yellow shading represents a perceptual ‘axon-comet’. The dotted red box outlines the retinal regions shown in (b–d). (b,c) A subject implanted with the Argus 1 prosthesis (this array contained interleaved 250 and 500 μm electrodes) was simultaneously stimulated on two different pairs of electrodes. Each pair of stimulated electrodes are shown outlined in red. Predicted percepts generated using the model described above are shown in white. (d,e) Subject drawings (averaged over five trials) of the percepts induced by these stimulation patterns. (Online version in colour.)
Figure 3.
Figure 3.
Simulations of perceptual distortions as a result of axonal stimulation. (a,b) Two images (the same as in figure 1) are overlaid on the retinal surface. The position of the electrode array (subtending 12°) is shown by a red dotted box. (c,d) The predicted effect of axonal stimulation on the two images of figure 1b,d for λ = 0.5, 1 and 2. Movie versions are shown in the electronic supplementary material. (Online version in colour.)
Figure 4.
Figure 4.
Example of one test within a standardized ‘Activities of daily living’ test proposed by Stingl et al. [53] (reprinted with their permission). The subject is asked to identify, describe and localize objects while sitting at a table. (Online version in colour.)
Figure 5.
Figure 5.
The perceptual effects of sluggish response kinetics. (a) Response kinetics for LiGluR-MAG0460 and normal phototransduction. LiGluR-MAG0460 cells are activated by visible light (time-course shown as a thick horizontal outlined bar; yellow online) and relax spontaneously in the dark. Circles (blue online) show human embryonic kidney cell recordings in voltage-clamp configuration at −75 mV (replotted with permission from [7]). Our simulated approximation (thin solid line; blue online) used an exponential onset of time-constant of 20 ms and an offset time-constant of 200 ms. For comparison, a crude simulation of primate phototransduction dynamics (using an exponential time-constant of 20 ms for both onset and offset) is also plotted (dashed line; red online). For our purposes, a crude approximation of time-courses was adequate; more accurate modelling of these time-courses can be found in [7] and [60], respectively. (b) The image of a child scooting (same as figure 1a). (c) The image of figure 1a was in fact taken from a movie sequence. Here, we show the image frame of figure 1a, with the movie filtered using the temporal dynamics of LiGluR-MAG0460. Rather dramatically, the sluggish temporal dynamics causes the scooting child to almost completely disappear. The movie version of (c) is shown in the electronic supplementary material. (Online version in colour.)
Figure 6.
Figure 6.
Three example models of the potential perceptual experience of sight recovery. All images subtend 12° of visual angle. (a) Scoreboard model. The luminance of the apparent percept is linearly related to the strength of current on the retina. (b) Simulation of electrical stimulation. This particular simulation is based on the model of simultaneously stimulating ON- and OFF-pathways as described in figure 1e, followed by the model of axonal stimulation as described in figure 3. (c) Simulation of small molecule photoswitch stimulation. This simulation is based on the model of simulating ON-centre pathways in isolation as described in figure 1b, followed by the effects of sluggish temporal dynamics as described in figure 5. Movie versions are shown in the electronic supplementary material.
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