skip to main content

Title: Decoding network-mediated retinal response to electrical stimulation: implications for fidelity of prosthetic vision
Abstract

Objective. Patients with photovoltaic subretinal implant PRIMA demonstrated letter acuity ∼0.1 logMAR worse than sampling limit for 100μm pixels (1.3 logMAR) and performed slower than healthy subjects tested with equivalently pixelated images. To explore the underlying differences between natural and prosthetic vision, we compare the fidelity of retinal response to visual and subretinal electrical stimulation through single-cell modeling and ensemble decoding.Approach. Responses of retinal ganglion cells (RGCs) to optical or electrical white noise stimulation in healthy and degenerate rat retinas were recorded via multi-electrode array. Each RGC was fit with linear–nonlinear and convolutional neural network models. To characterize RGC noise, we compared statistics of spike-triggered averages (STAs) in RGCs responding to electrical or visual stimulation of healthy and degenerate retinas. At the population level, we constructed a linear decoder to determine the accuracy of the ensemble of RGCs onN-way discrimination tasks.Main results. Although computational models can match natural visual responses well (correlation ∼0.6), they fit significantly worse to spike timings elicited by electrical stimulation of the healthy retina (correlation ∼0.15). In the degenerate retina, response to electrical stimulation is equally bad. The signal-to-noise ratio of electrical STAs in degenerate retinas matched that of the natural responses when 78 ± more » 6.5% of the spikes were replaced with random timing. However, the noise in RGC responses contributed minimally to errors in ensemble decoding. The determining factor in accuracy of decoding was the number of responding cells. To compensate for fewer responding cells under electrical stimulation than in natural vision, more presentations of the same stimulus are required to deliver sufficient information for image decoding.Significance. Slower-than-natural pattern identification by patients with the PRIMA implant may be explained by the lower number of electrically activated cells than in natural vision, which is compensated by a larger number of the stimulus presentations.

« less
Authors:
; ;
Publication Date:
NSF-PAR ID:
10362256
Journal Name:
Journal of Neural Engineering
Volume:
17
Issue:
6
Page Range or eLocation-ID:
Article No. 066018
ISSN:
1741-2560
Publisher:
IOP Publishing
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    Objective.Biomimetic protein-based artificial retinas offer a new paradigm for restoring vision for patients blinded by retinal degeneration. Artificial retinas, comprised of an ion-permeable membrane and alternating layers of bacteriorhodopsin (BR) and a polycation binder, are assembled using layer-by-layer electrostatic adsorption. Upon light absorption, the oriented BR layers generate a unidirectional proton gradient. The main objective of this investigation is to demonstrate the ability of the ion-mediated subretinal artificial retina to activate retinal ganglion cells (RGCs) of degenerated retinal tissue.Approach. Ex vivoextracellular recording experiments with P23H line 1 rats are used to measure the response of RGCs following selective stimulation of our artificial retina using a pulsed light source. Single-unit recording is used to evaluate the efficiency and latency of activation, while a multielectrode array (MEA) is used to assess the spatial sensitivity of the artificial retina films.Main results.The activation efficiency of the artificial retina increases with increased incident light intensity and demonstrates an activation latency of ∼150 ms. The results suggest that the implant is most efficient with 200 BR layers and can stimulate the retina using light intensities comparable to indoor ambient light. Results from using an MEA show that activation is limited to the targeted receptive field.Significance.Themore »results of this study establish potential effectiveness of using an ion-mediated artificial retina to restore vision for those with degenerative retinal diseases, including retinitis pigmentosa.

    « less
  2. Abstract

    Objective. Retinal prostheses aim to restore sight by electrically stimulating the surviving retinal neurons. In clinical trials of the current retinal implants, prosthetic visual acuity does not exceed 20/550. However, to provide meaningful restoration of central vision in patients blinded by age-related macular degeneration (AMD), prosthetic acuity should be at least 20/200, necessitating a pixel pitch of about 50µm or lower. With such small pixels, stimulation thresholds are high due to limited penetration of electric field into tissue. Here, we address this challenge with our latest photovoltaic arrays and evaluate their performancein vivo.Approach. We fabricated photovoltaic arrays with 55 and 40µm pixels (a) in flat geometry, and (b) with active electrodes on 10µm tall pillars. The arrays were implanted subretinally into rats with degenerate retina. Stimulation thresholds and grating acuity were evaluated using measurements of the visually evoked potentials (VEP).Main results. With 55µm pixels, we measured grating acuity of 48  ±  11µm, which matches the linear pixel pitch of the hexagonal array. This geometrically corresponds to a visual acuity of 20/192 in a human eye, matching the threshold of legal blindness in the US (20/200). With pillar electrodes, the irradiance threshold was nearly halved, and duration threshold reduced by more thanmore »three-fold, compared to flat pixels. With 40µm pixels, VEP was too low for reliable measurements of the grating acuity, even with pillar electrodes.Significance. While being helpful for treating a complete loss of sight, current prosthetic technologies are insufficient for addressing the leading cause of untreatable visual impairment—AMD. Subretinal photovoltaic arrays may provide sufficient visual acuity for restoration of central vision in patients blinded by AMD.

    « less
  3. Abstract

    Objective.Our laboratory has proposed chemical stimulation of retinal neurons using exogenous glutamate as a biomimetic strategy for treating vision loss caused by photoreceptor (PR) degenerative diseases. Although our previousin-vitrostudies using pneumatic actuation indicate that chemical retinal stimulation is achievable, an actuation technology that is amenable to microfabrication, as needed for anin-vivoimplantable device, has yet to be realized. In this study, we sought to evaluate electroosmotic flow (EOF) as a mechanism for delivering small quantities of glutamate to the retina. EOF has great potential for miniaturization.Approach.An EOF device to dispense small quantities of glutamate was constructed and its ability to drive retinal output tested in anin-vitropreparation of PR degenerate rat retina.Main results.We built and tested an EOF microfluidic system, with 3D printed and off-the-shelf components, capable of injecting small volumes of glutamate in a pulsatile fashion when a low voltage control signal was applied. With this device, we produced excitatory and inhibitory spike rate responses in PR degenerate rat retinae. Glutamate evoked spike rate responses were also observed to be voltage-dependent and localized to the site of injection.Significance.The EOF device performed similarly to a previously tested conventional pneumatic microinjector as a means of chemically stimulating the retina while eliminating themore »moving plunger of the pneumatic microinjector that would be difficult to miniaturize and parallelize. Although not implantable, the prototype device presented here as a proof of concept indicates that a retinal prosthetic based on EOF-driven chemical stimulation is a viable and worthwhile goal. EOF should have similar advantages for controlled dispensing of charged neurochemicals at any neural interface.

    « less
  4. Abstract

    Epiretinal prostheses aim at electrically stimulating the inner most surviving retinal cells—retinal ganglion cells (RGCs)—to restore partial sight to the blind. Recent tests in patients with epiretinal implants have revealed that electrical stimulation of the retina results in the percept of color of the elicited phosphenes, which depends on the frequency of stimulation. This paper presents computational results that are predictive of this finding and further support our understanding of the mechanisms of color encoding in electrical stimulation of retina, which could prove pivotal for the design of advanced retinal prosthetics that elicit both percept and color. This provides, for the first time, a directly applicable “amplitude-frequency” stimulation strategy to “encode color” in future retinal prosthetics through a predictive computational tool to selectively target small bistratified cells, which have been shown to contribute to “blue-yellow” color opponency in the retinal circuitry. The presented results are validated with experimental data reported in the literature and correlated with findings in blind patients with a retinal prosthetic implant collected by our group.

  5. Abstract Decoding sensory stimuli from neural activity can provide insight into how the nervous system might interpret the physical environment, and facilitates the development of brain-machine interfaces. Nevertheless, the neural decoding problem remains a significant open challenge. Here, we present an efficient nonlinear decoding approach for inferring natural scene stimuli from the spiking activities of retinal ganglion cells (RGCs). Our approach uses neural networks to improve on existing decoders in both accuracy and scalability. Trained and validated on real retinal spike data from more than 1000 simultaneously recorded macaque RGC units, the decoder demonstrates the necessity of nonlinear computations for accurate decoding of the fine structures of visual stimuli. Specifically, high-pass spatial features of natural images can only be decoded using nonlinear techniques, while low-pass features can be extracted equally well by linear and nonlinear methods. Together, these results advance the state of the art in decoding natural stimuli from large populations of neurons.