EUPHORIA-RETINA Workflow Overview

• EUPHORIA-RETINA workflow is a modular pipeline that organizes retinal data acquisition, intermediate state construction, output generation, and evaluation through explicit, staged processes.
• It bridges distinct implementations from attention-aware VR design systems to retina prosthesis modeling, employing both deterministic and stochastic computational strategies.
• The workflow integrates hardware emulation, human-in-the-loop experimentation, and system orchestration, enabling practical enhancements in retinal analysis and intervention.

EUPHORIA-RETINA Workflow denotes a staged, retina-centered pipeline in which acquisition, intermediate state construction, output generation, and evaluation are made explicit. In the sources considered here, the label has two distinct usages: it appears as an explicit end-to-end workflow name in an attention-aware design system, where EUPHORIA is “Empathizing User Preferences in a Holodeck Using Real-time Implicit Attention” and RETINA is “Responsive Embodiment Through Iterative visioN-guided Agentic AI” (Sankar et al., 27 Aug 2025); and it also appears, in retina research, as a workflow-oriented abstraction for organizing retinal prosthesis modeling, retinal computation, ophthalmic visual analysis, neuromorphic emulation, image enhancement, organoid experimentation, and image-to-stimulation control (Schmid et al., 2010). In the retinal context, the term is therefore best understood as a modular systems concept rather than a single canonical implementation.

1. Terminology, scope, and conceptual status

The name “EUPHORIA-RETINA Workflow” is not uniform across the literature. In “Attention is also needed for form design” (Sankar et al., 27 Aug 2025), it names a complete workflow in which a designer starts from a problem statement and keywords, explores a VR “moodspace” with eye tracking, and passes ranked images, attention heatmaps, ROI collages, HED edge collages, and color palettes to an agentic AI pipeline that generates sketches and photorealistic renderings. That usage is explicit and system-specific.

In retina-focused work, the expression is usually not the native name of the underlying method. “Towards a Unified User Interface for Visual Analysis of Retinal Data in Ophthalmology” explicitly states that the paper “does not explicitly mention a system named ‘EUPHORIA-RETINA’,” but it provides a workflow, coordination graph, toolchain editor, and unified UI that map directly onto an EUPHORIA-RETINA-style orchestration layer (Röhlig et al., 2023). “Electric Stimulation of the Retina” is similarly positioned as a workflow-relevant front-end modeling framework rather than as a full biological retina simulator: it supports a “coarse field + passive membrane response module,” upstream of more detailed anatomical and biophysical simulation (Schmid et al., 2010).

This dual usage matters because it prevents a common misconception. EUPHORIA-RETINA is not, in the retinal literature gathered here, a single standardized package with fixed inputs and outputs. It is a recurring way of structuring retina-related computation into explicit modules: stimulus or data ingestion, intermediate retinal or perceptual state formation, transformation or prediction, visualization, and human or downstream decision support (Meister, 28 Sep 2025).

2. Recurrent staged architecture

Across the cited work, the workflow is repeatedly reconstructed as an explicit sequence of stages rather than as a monolithic mapping. This suggests a family resemblance among otherwise heterogeneous systems.

Stage family	Representative operations	Representative sources
Input and conditioning	retinal image $S(\vec x,t)$, luminance frames $L$, OCT and clinical records, low-quality and high-quality CFP domains	(Meister, 28 Sep 2025, Philip et al., 15 Jan 2025, Röhlig et al., 2023, Zhu et al., 2023)
State construction	bipolar-channel filtering, amacrine modulation, stochastic thresholding, virtual-charge field solve, extracellular drive extraction	(Meister, 28 Sep 2025, Taranath et al., 30 Jul 2025, Schmid et al., 2010)
Output synthesis	ganglion spikes, passive membrane depolarization, ON/OFF spike maps, enhanced fundus images, prosthetic percepts	(Taranath et al., 30 Jul 2025, Philip et al., 15 Jan 2025, Lavoie et al., 2 Jun 2026)
Evaluation and feedback	cross-talk indicators, DR grading, MSSIM, workflow history, screenshots, summary composition	(Schmid et al., 2010, Zhu et al., 2023, Röhlig et al., 2023)

One canonical formulation of this staged architecture appears in “The Standard Model of the Retina” (Meister, 28 Sep 2025). There, the stimulus is first encoded by bipolar-type-specific spatiotemporal kernels,

$$B_i(\vec x,t)=\int_{\vec x',t'} K_i(\vec x'-\vec x, t'-t)\, S(\vec x',t')\, d^2x' \, dt',$$

and then pooled through ganglion-type-specific nonlinear subunits,

$$G_j(\vec x,t)=\sum_i \sum_{\vec x'} w_{ji}(\vec x'-\vec x)\, N_{ji}\!\left(B_i(\vec x',t)\right).$$

The paper’s broader claim is that center-surround antagonism, nonlinear spatial integration, texture sensitivity, direction selectivity, motion anticipation, spike-latency coding, and several forms of adaptation can be organized within one common scaffold of linear filtering, nonlinear subunits, pooling, and inhibition (Meister, 28 Sep 2025).

A stochastic variant of the same staging appears in “An Uncertainty Principle for Probabilistic Computation in the Retina,” which explicitly writes the workflow as optical input $\rightarrow$ deterministic wavefront intensity field $\rightarrow$ stochastic photon arrival process $\rightarrow$ receptor-level probabilistic threshold crossing $\rightarrow$ horizontal spatial inhibition $\rightarrow$ bipolar center-surround filtering $\rightarrow$ amacrine temporal filtering $L$0 ganglion-cell stochastic integration $L$1 Bernoulli spike generation $L$2 symbolic neural code (Taranath et al., 30 Jul 2025). The transition

$L$3

is its compact statement of analog uncertain state becoming symbolic spike output (Taranath et al., 30 Jul 2025).

A hardware-oriented variant appears in “Neuromorphic Retina: An FPGA-based Emulator,” where grayscale luminance frames pass through OPL center and surround paths, bipolar contrast gain control, ganglion temporal shaping, and leaky integrate-and-fire spiking to produce $L$4, $L$5, $L$6, $L$7, $L$8, and ON/OFF spike maps (Philip et al., 15 Jan 2025). The workflow is thus preserved even when the substrate changes from simulation to streaming fixed-point hardware.

3. Prosthetic stimulation and percept-generation branch

One major branch of the workflow concerns retinal prostheses and electrically induced perception. “Electric Stimulation of the Retina” formulates the front end as two deliberately simplified but coupled models: a quasi-static extracellular electric-field model for a multi-electrode array in a continuum retina, and a passive membrane depolarization model based on the antenna form of the cable equation (Schmid et al., 2010). The field computation reduces to

$L$9

outside electrode capsules, with virtual charges used to reproduce boundary conditions on the electrode surfaces (Schmid et al., 2010).

That framework is explicitly intended for early-stage exploration of electrode layout, field spread, simultaneous versus sequential activation, monopolar versus dipolar or multipolar driving, and pulse-duration effects (Schmid et al., 2010). Several engineering results are central. Simultaneous unipolar activation causes severe cross-talk: in the 39×39 array example with all 1521 electrodes active, the center electrode requires only about 2% of the virtual charge needed in the isolated-electrode case to maintain the same $$B_i(\vec x,t)=\int_{\vec x',t'} K_i(\vec x'-\vec x, t'-t)\, S(\vec x',t')\, d^2x' \, dt',$$0 V summit potential, implying that 98% of the local boundary potential is supplied by neighboring electrodes (Schmid et al., 2010). Dipoles reduce long-range spread but can lose current to near-chip shunting; with a $$B_i(\vec x,t)=\int_{\vec x',t'} K_i(\vec x'-\vec x, t'-t)\, S(\vec x',t')\, d^2x' \, dt',$$1 saline layer of 10× conductivity, adjacent-electrode dipoles show about 40% current loss (Schmid et al., 2010). The passive membrane model further yields the paper’s explicit pulse-design conclusion: “shorter signals stimulate better, as long as the current does not change sign during stimulation” (Schmid et al., 2010).

A later prosthetic-control workflow extends this branch from field calculation to percept synthesis and control. “Learning to See via Epiretinal Implant Stimulation in silico with Model-Based Deep Reinforcement Learning” formalizes image-to-stimulation conversion as a sequential rendering problem in the rlretina environment (Lavoie et al., 2 Jun 2026). The state is

$$B_i(\vec x,t)=\int_{\vec x',t'} K_i(\vec x'-\vec x, t'-t)\, S(\vec x',t')\, d^2x' \, dt',$$2

with $$B_i(\vec x,t)=\int_{\vec x',t'} K_i(\vec x'-\vec x, t'-t)\, S(\vec x',t')\, d^2x' \, dt',$$3 the target image and $$B_i(\vec x,t)=\int_{\vec x',t'} K_i(\vec x'-\vec x, t'-t)\, S(\vec x',t')\, d^2x' \, dt',$$4 the percept assembled so far; actions are single-electrode stimulations; and the environment uses the psychophysically validated axon map model to render anisotropic and isotropic phosphenes (Lavoie et al., 2 Jun 2026). The phosphene model is written as

$$B_i(\vec x,t)=\int_{\vec x',t'} K_i(\vec x'-\vec x, t'-t)\, S(\vec x',t')\, d^2x' \, dt',$$5

$$B_i(\vec x,t)=\int_{\vec x',t'} K_i(\vec x'-\vec x, t'-t)\, S(\vec x',t')\, d^2x' \, dt',$$6

$$B_i(\vec x,t)=\int_{\vec x',t'} K_i(\vec x'-\vec x, t'-t)\, S(\vec x',t')\, d^2x' \, dt',$$7

with anisotropy ensured when $$B_i(\vec x,t)=\int_{\vec x',t'} K_i(\vec x'-\vec x, t'-t)\, S(\vec x',t')\, d^2x' \, dt',$$8 (Lavoie et al., 2 Jun 2026).

This branch reframes prosthetic rendering as stroke-based rendering. The current percept is the canvas, single-electrode phosphenes are the brushstrokes, and the policy learns to exploit rather than merely suppress anisotropic shapes (Lavoie et al., 2 Jun 2026). In the reported easy setting $$B_i(\vec x,t)=\int_{\vec x',t'} K_i(\vec x'-\vec x, t'-t)\, S(\vec x',t')\, d^2x' \, dt',$$9, the SAC agent underperforms the naive stimulation algorithm on $$G_j(\vec x,t)=\sum_i \sum_{\vec x'} w_{ji}(\vec x'-\vec x)\, N_{ji}\!\left(B_i(\vec x',t)\right).$$0 and MSE but outperforms it on MSSIM, reaching 0.35 versus 0.28, which the paper interprets as better intelligibility (Lavoie et al., 2 Jun 2026). A common misconception is therefore that “pixel-like” phosphenes are always preferable. The reported results support a narrower claim: anisotropic phosphenes can function as useful rendering primitives when the control policy is trained against perceptual rather than purely pixelwise objectives (Lavoie et al., 2 Jun 2026).

4. Circuit, stochastic, and dynamical-systems formulations

A second branch of the workflow treats the retina as an internal computational system rather than as a prosthetic interface. “The Standard Model of the Retina” makes the strongest unifying claim: a relatively compact circuit architecture built from photoreceptors, bipolar cells, horizontal cells, amacrine cells, and ganglion cells, with ON/OFF pathway splitting and subunit nonlinearities, explains a wide range of early visual computations (Meister, 28 Sep 2025). Its workflow implication is explicit: retinal processing should be represented as a staged, biologically constrained cascade rather than a black-box image-to-spike predictor (Meister, 28 Sep 2025).

“An Uncertainty Principle for Probabilistic Computation in the Retina” modifies this picture by insisting that early retinal processing should be treated as intrinsically probabilistic rather than deterministically encoding a fixed stimulus into a fixed response (Taranath et al., 30 Jul 2025). Photon arrivals are assumed Poisson with rate proportional to

$$G_j(\vec x,t)=\sum_i \sum_{\vec x'} w_{ji}(\vec x'-\vec x)\, N_{ji}\!\left(B_i(\vec x',t)\right).$$1

photoreceptor thresholds satisfy

$$G_j(\vec x,t)=\sum_i \sum_{\vec x'} w_{ji}(\vec x'-\vec x)\, N_{ji}\!\left(B_i(\vec x',t)\right).$$2

and ganglion spikes are sampled as

$$G_j(\vec x,t)=\sum_i \sum_{\vec x'} w_{ji}(\vec x'-\vec x)\, N_{ji}\!\left(B_i(\vec x',t)\right).$$3

after layered stochastic thresholding and filtering (Taranath et al., 30 Jul 2025). Its signature relation,

$$G_j(\vec x,t)=\sum_i \sum_{\vec x'} w_{ji}(\vec x'-\vec x)\, N_{ji}\!\left(B_i(\vec x',t)\right).$$4

is not derived from first principles and is explicitly presented as a phenomenological, empirically testable hypothesis rather than a theorem (Taranath et al., 30 Jul 2025). That distinction is important because the evidence directly supports intrinsic variability under repeated identical stimulation, but does not directly establish the uncertainty law as a measured invariant (Taranath et al., 30 Jul 2025).

“The Retina as a Dynamical System” places the same processing pipeline in a non-autonomous state-space setting, emphasizing that the retina is “a high dimensional, non autonomous dynamical system, layered and structured, with non stationary and spatially inhomogeneous entries (visual scenes)” (Cessac, 2020). Its workflow contribution is a multiscale decomposition into stimulus $$G_j(\vec x,t)=\sum_i \sum_{\vec x'} w_{ji}(\vec x'-\vec x)\, N_{ji}\!\left(B_i(\vec x',t)\right).$$5, graded continuous dynamics, and spike-train statistics, together with slow-fast analysis, bifurcation structure, and Gibbs-process descriptions. One compact statement of its statistical-response layer is

$$G_j(\vec x,t)=\sum_i \sum_{\vec x'} w_{ji}(\vec x'-\vec x)\, N_{ji}\!\left(B_i(\vec x',t)\right).$$6

linking changes in observables under stimulus drive to spontaneous correlation structure (Cessac, 2020).

“Anticipation and Negative Group Delay in a Retina” adds a mechanistic module for anticipatory coding under correlated stimulation (Chou et al., 2020). Its two-state delayed-feedback model,

$$G_j(\vec x,t)=\sum_i \sum_{\vec x'} w_{ji}(\vec x'-\vec x)\, N_{ji}\!\left(B_i(\vec x',t)\right).$$7

implements adaptation-like negative feedback that can generate negative group delay for sufficiently low frequencies (Chou et al., 2020). The paper reports that the prediction horizon depends on stimulus temporal correlation and that dark Gaussian pulses, but not bright Gaussian pulses, support the NGD interpretation, suggesting that the effect “might originate from its OFF response” (Chou et al., 2020). This suggests that predictive behavior in a retina-centered workflow need not always imply explicit internal scene modeling; it can also emerge from delayed adaptive feedback dynamics (Chou et al., 2020).

5. Data, enhancement, orchestration, and hardware realization

A third branch of the workflow concerns how retinal data and retinal-like computation are organized operationally. “Towards a Unified User Interface for Visual Analysis of Retinal Data in Ophthalmology” is centered on cross-sectional ophthalmic research studies using OCT, segmented layer boundaries, retinal thickness measurements, and clinical records (Röhlig et al., 2023). It defines a three-stage workflow—data preparation, data analysis, and summarization of results—implemented through a coordination graph linking workflow steps, tools, data, and layouts (Röhlig et al., 2023). Once a step is selected, the relevant tools are automatically activated, their views are arranged on screen, and data exchange is handled automatically; step status, notes, screenshots, and a summary-composition interface provide lightweight provenance (Röhlig et al., 2023). In workflow terms, this paper is not a retinal model but an orchestration layer.

For color fundus photography, “OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing” adds a quality-normalization stage before downstream analysis (Zhu et al., 2023). It frames enhancement as transport from a low-quality CFP domain $$G_j(\vec x,t)=\sum_i \sum_{\vec x'} w_{ji}(\vec x'-\vec x)\, N_{ji}\!\left(B_i(\vec x',t)\right).$$8 to a high-quality domain $$G_j(\vec x,t)=\sum_i \sum_{\vec x'} w_{ji}(\vec x'-\vec x)\, N_{ji}\!\left(B_i(\vec x',t)\right).$$9, regularized by structural preservation through multi-scale SSIM and identity consistency (Zhu et al., 2023). In the no-reference evaluation, enhancement with OTRE followed by a ResNet-50 DR grader yielded accuracy 0.7767, kappa 0.6814, and ROC 0.9403, outperforming CycleGAN and OTTGAN on that downstream task (Zhu et al., 2023). The paper also reports better vessel-segmentation ROC, PR, F1, and sensitivity on DRIVE after OTRE preprocessing (Zhu et al., 2023). Its workflow position is therefore clear: image enhancement becomes a preprocessing and harmonization module rather than an isolated image-restoration endpoint.

At the hardware level, “Neuromorphic Retina: An FPGA-based Emulator” turns a retina-like pipeline into a streaming implementation on a Xilinx Artix-7 device (Philip et al., 15 Jan 2025). Its stages are OPL center and surround filtering, bipolar contrast gain control, ganglion temporal shaping, and LIF spiking, operating on 128×128 luminance frames at 200 fps (Philip et al., 15 Jan 2025). The implementation exposes intermediate maps $\rightarrow$0, $\rightarrow$1, $\rightarrow$2, $\rightarrow$3, and $\rightarrow$4, as well as ON/OFF spike maps, and consumes 1720 slices, approximately 3.7k LUTs, and flip-flops (Philip et al., 15 Jan 2025). This anchors the workflow in a real-time, reconfigurable substrate.

A related implementation motif appears in “Streaming an image through the eye: The retina seen as a dithered scalable image coder,” where the retina is treated as a time-evolving transform coder plus analog-to-digital converter (Masmoudi et al., 2012). Its workflow replaces the full Virtual Retina outer stage with spatial DoG analysis plus explicit subband delays, turning observation time $\rightarrow$5 into the primary scalability axis (Masmoudi et al., 2012). The optional dither stage whitens reconstruction error, decorrelates it from the input, and allows faster recognition of fine details during progressive decoding (Masmoudi et al., 2012). This suggests that retina-centered workflows can also be designed as progressive coding systems, not only as recognition or simulation stacks.

6. Human-in-the-loop experimentation, validation, and limits

A persistent feature of the EUPHORIA-RETINA idea is that human interpretation remains embedded in the loop. “A Parametric Perceptual Deficit Modeling and Diagnostics Framework for Retina Damage using Mixed Reality” gives a direct example for AMD-related central vision loss (Aniruddha et al., 2019). Its patient-specific perceptual model is

$\rightarrow$6

where $\rightarrow$7 is a luminance-degradation field, $\rightarrow$8 is a significant-impairment region, $\rightarrow$9 is a rotational distortion model, and $\rightarrow$0 is a spatial distortion vector field (Aniruddha et al., 2019). In VR diagnostic mode, the patient views an Amsler grid and interactively adjusts the parameters until the rendered impairment matches subjective perception; in AR compensation mode, the inverse of the model is applied to stereoscopic camera video for the affected eye (Aniruddha et al., 2019). This is a workflow in which diagnosis, simulation, and intervention share the same parameterization.

“Retina organoids: Window into the biophysics of neuronal systems” extends the human-in-the-loop idea to developmental and mechanobiological experimentation (Salbaum et al., 2022). The implied sequence is stem-cell line qualification, eye-field induction, NR/RPE patterning, self-organization, photoreceptor-supportive maturation, functional validation, mechanical interrogation, and mathematical modeling (Salbaum et al., 2022). The review makes two constraints explicit: first, retina organoid workflows must integrate biochemical and mechanical variables; second, “major neuronal circuits in ROs has not yet been reported,” even though light-sensitive photoreceptors and early synaptic connectivity are already measurable (Salbaum et al., 2022). The explicit model equations

$\rightarrow$1

for overdamped vertex dynamics and

$\rightarrow$2

for shell-model wrinkling show how experimental biophysics and workflow design are linked quantitatively (Salbaum et al., 2022).

The literature does not support treating EUPHORIA-RETINA as a fully automated or biologically complete pipeline. Some components are explicitly exploratory front ends rather than final mechanistic models: the field-and-cable prosthesis framework is “not sufficient alone” for cell-type-selective activation prediction or realistic phosphene-shape prediction (Schmid et al., 2010). Some formalisms are explicitly phenomenological rather than derived laws, as with $\rightarrow$3 (Taranath et al., 30 Jul 2025). Some orchestration layers acknowledge that “not all steps could be fully automated” because of tool heterogeneity (Röhlig et al., 2023). Some enhancement systems assume that the input image is still usable, rather than completely corrupted (Zhu et al., 2023). These limits are not peripheral; they define the present boundary of the workflow concept.

A plausible implication is that future EUPHORIA-RETINA workflows will be increasingly hybrid. They will likely combine patient- or specimen-specific calibration, explicit uncertainty-bearing latent states, hardware-conscious front ends, orchestration layers that preserve provenance, and validation modules that compare mechanistic predictions against biological readouts rather than against a single scalar performance metric. The cited work does not yet collapse into one standard implementation, but it already delineates a coherent encyclopedic object: a modular retina-centered workflow in which acquisition, transformation, interpretation, and intervention are made explicit, testable, and progressively more integrated.