Global activity shaping strategies for a retinal implant
Open accessAbstract
OBJECTIVE: Retinal prostheses provide visual perception via electrical stimulation of the retina using an implanted array of electrodes. The retinal activation resulting from each electrode is not point-like; instead each electrode introduces a spread of retinal activation that may overlap with activations from other electrodes. With most conventional stimulation strategies this overlap leads to image blur. Here we propose a 'shaping' algorithm that uses multiple electrodes to manipulate the current between electrodes in a desired way. APPROACH: We assume a forward model for the conversion of electrode strengths to retinal activation. Three alternative global shaping algorithms are developed by calculating reverse models under different assumptions: linear inversion using singular value decomposition to produce the pseudoinverse, a linearly constrained quadratic program, and a binary quadratic program to partition the target pattern. The algorithms were assessed using both the mean squared error between the resulting images and desired images, as well as their adherence to the maximum allowed electrode currents. MAIN RESULTS: Under wide activation spreads the linear inversion algorithm gave improved solutions but faced two limitations: under low-noise conditions the electrode amplitudes exceeded their set limit; the set of solutions did not include the possibility of using negative local currents to induce retinal activation. The linearly constrained quadratic program and binary quadratic program respectively addressed these problems, but required much greater computation time. SIGNIFICANCE: This provides a framework for improving the resolution of future retinal implants, especially those with high density electrode arrays.
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Several studies have proposed more sophisticated stimulus encoding strategies to recreate a desired neural activity pattern over a given temporal window, but none of these approaches are able to predict the perceptual consequences of the resulting neural activity.
“Several studies have already proposed more sophisticated stimulus encoding strategies to recreate a desired neural activity pattern over a given temporal window (Shah et al., 2019; Spencer et al., 2019; Ghaffari et al., 2021). However, none of these approaches are able to predict the perceptual consequences of the resulting neural activity.”
From Towards a <i>Smart Bionic Eye</i> : AI-powered artificial vision for the treatment of incurable blindness (2022) · cites this paper as Spencer et al., 2019
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredTo find the stimulus that will elicit a desired response, one essentially needs to find the inverse of the forward model, which can be achieved in a number of ways.
“To find the stimulus that will elicit a desired response, one essentially needs to find the inverse of the forward model, which can be achieved in a number of ways (Spencer et al., 2019; Fauvel and Chalk, 2022; Granley et al., 2022).”
From Towards a <i>Smart Bionic Eye</i> : AI-powered artificial vision for the treatment of incurable blindness (2022) · cites this paper as Spencer et al., 2019
Alternative stimulation strategies have not been validated in vivo in humans using perceptually relevant pulse-train stimulation, and most do not address the substantial day-to-day variability in cortical responses that limits clinical reliability.
“While alternative stimulation strategies have been proposed [25–39], none have been validated in vivo in humans using perceptually relevant pulse-train stimulation, and most do not address the substantial day-to-day variability in cortical responses that limits clinical reliability.”
From Deep Learning–Based Control of Electrically Evoked Activity in Human Visual Cortex (2025) · cites this paper as [32]
Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredIn retinal prosthesis, research on closed-loop stimulation has been done to optimize the stimulation parameters to obtain the desired retinal ganglion cells output in response to a given visual input.
“In retinal prosthesis, research on closed-loop stimulation have been done to optimize the stimulation parameters to obtain the desired retinal ganglion cells output in response to a given visual input (Guo et al., 2018; Spencer et al., 2019; Shah and Chichilnisky, 2020).”
From Toward a personalized closed-loop stimulation of the visual cortex: Advances and challenges (2022) · cites this paper as Spencer et al., 2019
Recent computational models of prosthetic vision assume linear summation of phosphenes.
“Moreover, recent computational models of prosthetic vision assume linear summation of phosphenes (Spencer et al 2019, de Ruyter van Steveninck et al 2022), but this has yet to be demonstrated empirically.”
From Axonal stimulation affects the linear summation of single-point perception in three Argus II users (2024) · cites this paper as Spencer et al 2019
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Research works printed but not linked: 1
- Spencer, M. J., Kameneva, T., Grayden, D. B., Meffin, H. & Burkitt, A. N. (2019). Global activity shaping strategies for a retinal implant. Journal of Neural Engineering, 16 (2), https://doi.org/10.1088/1741-2552/aaf071. Persistent Link: http://hdl.handle.net/11343/227757 File Description: Accepted version
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Stimulation strategies for improving the resolution of retinal prostheses
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Deep Learning–Based Control of Electrically Evoked Activity in Human Visual Cortex
Moure, P., Granley, J., Grani, F., Soo, L., Lozano, A., López-Peco, R., Villamarin-Ortiz, A., Soto-Sanchez, C., Liu, S.-C., Beyeler, M., et al. (2025) · bioRxiv · doi:10.1101/2025.09.24.678361
- Authors
- Spencer, M. J., Kameneva, T., Grayden, D. B., Meffin, H., & Burkitt, A. N.
- Affiliations
- The University of Melbourne (AU)
- Corresponding author
- Spencer, Martin J
- Kameneva, Tatiana
- Grayden, David B
- Burkitt, Anthony N
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2019-01-23
- Volume
- 16
- Issue
- 2
- Pages
- 026008
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W2900966502
- Cited by
- 28
- References
- 41
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 4 · 2023: 5 · 2022: 7 · 2021: 3 · 2020: 5
- FWCI
- 0.91
- Type
- journal article
- Language
- en
- Update notices
- correction (2019-09-18)
- Open access
- Licence
- Locations
- Journal of Neural Engineering (publishedVersion)
- PubMed (publishedVersion)
- Minerva Access (University of Melbourne) (submittedVersion)
- Swinburne figshare (Swinburne University of Technology) (submittedVersion)
- Funders
- National Health and Medical Research Council (1106390)
- Australian Research Council (CE140100007)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Retinal Development and Disorders
- Keywords
- retinal prosthesis, image blurring, forward model, linear inversion, singular value decomposition, image resolution
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