Learning to See via Epiretinal Implant Stimulation in silico with Model-Based Deep Reinforcement Learning
arXiv:2606.03118v1 Announce Type: new Abstract: Objective: Diseases such as age-related macular degeneration and retinitis pigmentosa cause the degradation of the photoreceptor layer. One approach to restore vision is to electrically stimulate the surviving retinal ganglion cells with a microelectrode array such as epiretinal implants. Epiretinal implants are known to generate visible anisotropic shapes elongated along the axon fascicles of neighboring retinal ganglion cells. Recent work has demonstrated that to obtain isotropic pixel-like shapes, it is possible to map axon fascicles and avoid
This research is emerging now due to continued advancements in AI, particularly deep reinforcement learning, and increasing sophistication in bio-electronic interface technology, allowing for in silico modeling of complex biological systems.
This work explores a new avenue for restoring human vision using neural implants, indicating a growing convergence of AI and biological systems to address complex medical challenges.
The possibility of using AI to optimize stimulation patterns for epiretinal implants could lead to more effective and "natural" artificial vision for patients, shifting previous limitations of visual perception.
- · Ophthalmology patients with photoreceptor degradation
- · Medical AI research & development
- · Bio-electronic implant manufacturers
- · Neuroprosthetics industry
- · Traditional vision correction methods (long term, highly speculative)
- · Purely biological research approaches lacking computational integration
Improved visual acuity and quality of life for individuals with certain types of blindness through optimized epiretinal implants.
Accelerated development of other neural prosthetics using similar AI-driven optimization techniques, expanding applications beyond vision.
Ethical and societal considerations around human-machine interfaces and altered sensory perception, as artificial senses become increasingly sophisticated.
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Read at arXiv cs.LG