Abstract Introduction: Current brain-based visual prostheses pose significant challenges impeding adoption such as the necessarily complex surgeries and occurrence of more substantial side effects due to the sensitivity of the brain. This has led to much effort toward vision restoration being focused on the more approachable part of the brain - the retina. Here we introduce a novel, parameterized simulation platform that enables study of human retinal degeneration and optimization of stimulation strategies. The platform bears immense potential for patient-specific tailoring and serves to enhance artificial vision solutions for individuals with visual impairments. Material and method: Our virtual retina is developed using the software package, NEURON. This virtual retina platform supports large-scale simulations of over 10,000 neurons whilst upholding strong biological plausibility with multiple important visual pathways and detailed network properties. The comprehensive three-dimensional model includes photoreceptors, horizontal cells, bipolar cells, amacrine cells, and midget and parasol retinal ganglion cells, with comprehensive network connectivity across various eccentricities (1 mm to 5 mm from the fovea) in the human retina. The model is constructed using electrophysiology, immunohistology, and optical coherence tomography imaging data from healthy and degenerate human retinas. We validated our model by replicating numerous experimental observations from human and primate retina, with a particular focus on retinal degeneration. Result: We simulated interactions between diseased retinas and state-of-the-art retinal implants, shedding light on the limitations of commercial retinal prostheses. Our results suggested that appropriate stimulation settings with intraretinal prototype devices could leverage network-mediated activation to achieve activation mosaics more alike that of the retina's response to natural light, promoting the prospect of more naturalistic vision. Our study additionally highlights the importance of controlling inhibitory circuits in the retinal network to induce functionally relevant retinal activity. Conclusion: This study demonstrates the potential of this software package and highlights its utility as a valuable tool for engineers, scientists, and clinicians in the design and optimisation of retinal stimulation devices for both research and educational applications. Keywords: Virtual retina; bionic vision; data-driven model; discrete neuronal network model; electrical stimulation; human retina; mechanistic model; retinal degeneration.
Experimental motivation: The results suggest that compared to direct RGC activation, network-mediated stimulation produces neural activation mosaics more akin to that of a naturalistic spotlight pulse. It was found that the rectification of the OFF pathway was preserved in subretinal, intraretinal INL, and intraretinal IPL stimulation, resulting in activation of ON RGCs alongside inhibition of OFF RGCs. These results suggests that emerging strategies for prosthetic vision should a) incorporate inhibitory circuitry mechanisms into consideration when devising electrical stimulation strategies; b) better preserve naturalistic firing mosaics by avoiding indiscriminate RGC activation, ideally sending less conflicting information to the visual cortex; and c) further consider the impacts of functional remodelling and degeneration in the retina on the efficacy of artificial stimulation.
Region(s) or Organism(s): Retina
Cell Type(s): Retina ganglion GLU cell; Retina amacrine cell; Retina bipolar GLU cell; Retina horizontal cell; Retina photoreceptor cone GLU cell; Retina photoreceptor rod GLU cell
Receptors: Gaba; mGluR; Glycine; NMDA; Kainate
Transmitters: Gaba; Glycine; Glutamate
Model Concept(s): Detailed Neuronal Models
Simulation Environment: NEURON
Implementer(s): Ly, Keith
References:
Ly K et al. (2025). Virtual Human Retina: Simulating Neural Signalling, Degeneration, and Responses to Electrical Stimulation. Brain stimulation. [PubMed]