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arxiv: 2606.03118 · v1 · pith:CQ73OXEOnew · submitted 2026-06-02 · 💻 cs.LG · cs.CV· q-bio.NC

Learning to See via Epiretinal Implant Stimulation in silico with Model-Based Deep Reinforcement Learning

Jacob Lavoie , Marwan Besrour , William Lemaire , Jean Rouat , R\'ejean Fontaine , Eric Plourde This is my paper

Pith reviewed 2026-06-28 11:40 UTC · model grok-4.3

classification 💻 cs.LG cs.CVq-bio.NC
keywords epiretinal implantsdeep reinforcement learningphosphenesaxon map modelvisual restorationstroke-based renderingmodel-based RLvirtual patients
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The pith

A deep reinforcement learning agent learns to combine isotropic and anisotropic phosphenes to form more intelligible images than naive stimulation for virtual epiretinal implant patients.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that framing epiretinal stimulation as a stroke-based rendering task in a reinforcement learning environment allows an agent to select and place both pixel-like and axon-aligned brushstroke shapes. Training occurs model-based, using the axon map model to simulate perception in different virtual patients and reward the agent with error or perception metrics. The resulting policies produce images that recipients can interpret more clearly than those from standard electrode activation methods. This matters because current implants often generate distorted percepts that limit functional vision restoration for retinal diseases.

Core claim

We train a deep reinforcement learning agent that learns to assemble isotropic and anisotropic shapes to form an image in the rlretina environment. The agent is trained in a model-based data generation fashion using the psychophysically validated axon map model to render images as perceived by different virtual patients. We show that the agent can generate more intelligible images compared to the naive method in different virtual patients.

What carries the argument

The rlretina environment, which formalizes epiretinal stimulation as a stroke-based rendering task that assembles isotropic and anisotropic phosphenes rendered through the axon map model and scored by error-based or perception-based rewards.

If this is right

  • Agents can discover non-obvious combinations of shape types that improve overall image intelligibility.
  • Both error-based and perception-based reward signals are sufficient to train useful policies.
  • Model-based training on multiple virtual patients produces strategies that generalize across different axon maps.
  • Avoiding or incorporating axon fascicle stimulation can be treated as a learnable decision rather than a fixed preprocessing rule.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same environment could support online adaptation if the axon map model were updated from patient feedback during use.
  • The approach might transfer to other sensory prostheses that produce elongated or oriented percepts, such as cochlear implants or visual cortex arrays.
  • Clinical validation would require mapping the learned policies back to safe charge-delivery constraints not fully explored in the virtual setting.

Load-bearing premise

The axon map model accurately predicts how actual patients will perceive the generated combinations of shapes.

What would settle it

Deliver the agent's learned stimulation patterns to real epiretinal implant users and measure whether they recognize the target images more accurately than with naive patterns.

Figures

Figures reproduced from arXiv: 2606.03118 by Eric Plourde, Jacob Lavoie, Jean Rouat, Marwan Besrour, R\'ejean Fontaine, William Lemaire.

Figure 1
Figure 1. Figure 1: Epiretinal and subretinal implan￾tation sites are illustrated in the anatomical context of the retina. referred to as a percept. The main focus of the work presented in this article is to train a reinforcement learning (RL) agent that selects phosphenes to generate a percept similar to a digital image in different virtual patients, thus restoring visual acuity. Before presenting the proposed approach, we g… view at source ↗
Figure 2
Figure 2. Figure 2: Comparison between anisotropic and isotropic phosphenes shapes. (Left) Subject 2 drawings from [12] show anisotropic phosphenes perceived during single-electrode stimulation with an Argus II implant. (Center) Axon map model predicts the anisotropic shaped caused by extracellular axon stimulation. (Right) Scoreboard model does not include extracellular axon stimulation resulting in ideal isotropic phosphene… view at source ↗
Figure 2
Figure 2. Figure 2: figure 2 [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: A model-free environment simulates a virtual patient’s retina and its visual system. The agent is defined as the implant controller and is in charge of transforming a camera image into a single-electrode stimulation pattern. The single-electrode stimulation excite the retina. The signal propagates to the lateral geniculate nucleus (LGN) and through the visual cortex (V1) and subsequent visual areas (Not il… view at source ↗
Figure 4
Figure 4. Figure 4: In contrast to figure 3, the SAC agent receives the percept P as well as the original image I. This model-based data generation scheme makes use of the predictive model of the retina to give access to the internal state Lt of the environment model. Electrical stimulations are transformed into a visual percept using the axon map model from pulse2percept library. The SAC agent is composed of an actor and a c… view at source ↗
Figure 5
Figure 5. Figure 5: shows that the reward estimated by the l2 distance quickly saturates to a reward value after only 1000 episodes, while the reward estimated by the Wasserstein distance slowly progresses. The results with a conventional l2 distance as an estimator of Lt are very limited. Looking at the samples as shown in figure 5 reveals that the l2 distance only taught the agent to represent low spatial frequencies of the… view at source ↗
Figure 6
Figure 6. Figure 6: Two virtual patient’s percepts with different axon map model and single￾electrode stimulation parameters. Columns from left to right are respectively the initial MNIST target image, the NSA generated percept and the SAC agent generated percept. (a) Samples generated with ρ = 315, λ = 500 and N = 32. (b) Samples generated with ρ = 200, λ = 500 and N = 16. Axon map model parameters are influenced by experime… view at source ↗
read the original 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 stimulating them by inactivating electrodes or lowering stimulation current levels. Avoiding axon fascicle stimulation aims to remove brushstroke-like shapes in favor of a more reduced set of pixel-like shapes. Approach: In this study, we propose the use of isotropic and anisotropic shapes to render intelligible images on the retina of a virtual patient in a reinforcement learning environment named rlretina. The environment formalizes the task as using brushstrokes in a stroke-based rendering task. Main Results: We train a deep reinforcement learning agent that learns to assemble isotropic and anisotropic shapes to form an image. We investigate which error-based or perception-based metrics is adequate to reward the agent. The agent is trained in a model-based data generation fashion using the psychophysically validated axon map model to render images as perceived by different virtual patients. We show that the agent can generate more intelligible images compared to the naive method in different virtual patients. Significance: This work shares a new way to address epiretinal stimulation that constitutes a first step towards improving visual acuity in artificially-restored vision using anisotropic phosphenes.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 3 minor

Summary. The manuscript introduces the rlretina reinforcement-learning environment for epiretinal implant stimulation. It models virtual patients via the psychophysically validated axon-map model, frames image formation as a stroke-based rendering task that combines isotropic and anisotropic phosphenes, and trains a model-based deep RL agent to select stimulation patterns. The central claim is that the learned policy produces more intelligible renderings than a naive baseline across multiple virtual patients when evaluated under error-based or perception-based reward metrics.

Significance. If the in-silico superiority holds under the chosen metrics and the axon-map model is accepted as the ground truth for the virtual patients, the work demonstrates a novel application of model-based DRL to prosthetic vision and shows that explicitly modeling both phosphene shapes can improve rendering quality inside the simulator. The purely simulated setting allows controlled ablation of reward functions and patient variability, which is a methodological strength, but downstream clinical significance remains conditional on future transfer or psychophysical validation.

major comments (2)
  1. [Abstract] Abstract (Main Results paragraph): the claim that the agent 'can generate more intelligible images compared to the naive method' is presented without any quantitative metrics, error bars, statistical tests, or even the identity of the winning reward function, rendering the central empirical claim impossible to evaluate.
  2. [Approach] Approach section: the reward functions are described only as 'error-based or perception-based' without explicit mathematical definitions or ablation results showing which metric actually drives learning; this is load-bearing because the paper states it investigates 'which ... metric is adequate to reward the agent.'
minor comments (3)
  1. [Abstract] Abstract: grammatical error ('which error-based or perception-based metrics is adequate') and awkward phrasing ('in a model-based data generation fashion').
  2. [Main Results] The manuscript should include a table or figure that directly compares the final intelligibility scores (with standard deviations) of the RL agent versus the naive baseline for each virtual patient.
  3. [Methods] No description is given of the state representation, action space discretization, or network architecture used by the DRL agent.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We agree that the abstract requires quantitative support for its central claim and that the reward functions need explicit definitions plus ablation results. We address each major comment below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract] Abstract (Main Results paragraph): the claim that the agent 'can generate more intelligible images compared to the naive method' is presented without any quantitative metrics, error bars, statistical tests, or even the identity of the winning reward function, rendering the central empirical claim impossible to evaluate.

    Authors: We acknowledge that the abstract presents the main result only qualitatively. The full manuscript contains the supporting quantitative comparisons across virtual patients, but these are not summarized in the abstract. We will revise the abstract to report the key performance metrics (including means and variability) under the best reward function, along with the identity of that function, so the claim can be evaluated directly from the abstract. revision: yes

  2. Referee: [Approach] Approach section: the reward functions are described only as 'error-based or perception-based' without explicit mathematical definitions or ablation results showing which metric actually drives learning; this is load-bearing because the paper states it investigates 'which ... metric is adequate to reward the agent.'

    Authors: We agree that the current description is insufficient given the paper's explicit goal of investigating reward adequacy. We will add the precise mathematical formulations of both the error-based and perception-based reward functions in the Approach section. We will also include ablation results that compare the metrics' effects on learning dynamics and final rendering quality across the virtual patients. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper's derivation chain is self-contained and non-circular. It trains an RL agent inside the rlretina environment whose perception model is an externally validated axon-map model (psychophysically validated, not derived inside the paper). The central claim is an in-silico performance comparison (agent vs. naive baseline) under chosen reward metrics; this comparison does not reduce to a fitted parameter being renamed as a prediction, nor to any self-citation chain, self-definition, or ansatz smuggled from prior author work. No equations or steps in the abstract or described method exhibit the enumerated circular patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Review is abstract-only; no explicit free parameters, additional axioms, or invented entities are identifiable beyond the core modeling assumption stated in the abstract.

axioms (1)
  • domain assumption The axon map model accurately renders perceived images for virtual patients.
    Invoked to generate training data and evaluate agent performance across different virtual patients.

pith-pipeline@v0.9.1-grok · 5841 in / 1079 out tokens · 27702 ms · 2026-06-28T11:40:06.103058+00:00 · methodology

discussion (0)

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