Optics Express

Spectroscopic single-molecule localization microscopy (sSMLM) enables multiplexed super-resolution imaging by simultaneously acquiring the spatial position and spectral information of individual fluorophores. Dual-wedge prism (DWP)-based implementations provide a compact, alignment-stable approach to spectral dispersion, but trade-offs between localization precision, spectral precision, and experimental complexity remain. We systematically compare five DWP-based sSMLM configurations, including two-dimensional (2D) and three-dimensional (3D) implementations using single DWP (DWP-sSMLM) and symmetrically-dispersed DWP (SDDWP-sSMLM). We evaluate lateral precision, spectral precision, and ease of use. SDDWP configurations acquire spectral images in both channels and utilize both for spatial localization, yielding the highest lateral and spectral precision. However, for applications that do not require axial information, 2D-DWP provides a simple, plug-and-play solution with robust performance. This work offers a guideline for selecting DWP configurations based on experimental needs.

The measurement of light received at the cornea of the eye is a paramount consideration for the understanding of the relation between environmental illumination and the non-image-forming effects of light. The field of view (FOV) at the cornea is less than a full hemisphere, because it is partially occluded by human facial morphology. The International Commission on Illumination (CIE) has defined a standard model of human FOV. A suitably designed physical occluder attached to the sensor (of a light meter) has been proposed as a means of incorporating the effect of human FOV when taking measurements. Similarly, when using simulation to predict light received at the cornea, a geometrical description of the occluder at the eye point(s) can be added to the 3D model of the scene. The first occluder model proposed to represent CIE human FOV was enumerated in terms of: the CIE definition; the radius of the occluder; and, the radius of the light sensor disc. We present a simpler model based only on the CIE definition and the occluder radius. Both models were tested using a virtual goniophotometer. Various sensor response functions describing the spatial sensitivity across the sensor disc, including several we characterized through laboratory measurements, were included in the test. For all functions considered, the performance of the simpler occluder model was equivalent to or better than the model first proposed.

The lifetime of fluorescent molecules provides an orthogonal readout to fluorescence intensity, opening experimental possibilities of measuring changes in local molecular environments, mechanical tension, and metabolism, among other factors. These changes are best studied live and in vivo; however, limitations of slow imaging speeds, high phototoxicity, and increased data size and complexity have significantly impeded progress on this front. Here, we present a complete and transferable pipeline consisting of a light sheet FLIM microscope and an accompanying machine learning model for data processing that renders long-term and/or high-speed volumetric FLIM (vFLIM) tractable in living systems. We benchmark this pipeline across several biological use cases, model systems, lifetime ranges, and spatiotemporal scales, showcasing a suite of possibilities that our workflow enables. This comprehensive pipeline from imaging to analysis is a crucial step forward towards disseminating the power of live vFLIM to the broader bioimaging community.

Ultrathin endoscopy is highly attractive for real-time tissue imaging in narrow and hard-to-reach regions of the body. A single multimode fibre (MMF) is an attractive probe because of its small diameter, flexibility, and diffraction-limited spatial resolution enabled by the large number of transverse modes guided within a single core. Because the distal fibre tip is inaccessible during endoscopy, reflection-mode imaging, in which the same fibre delivers illumination and collects backscattered light, is more practical than transmission-mode imaging. However, image recovery from the resulting speckle pattern is challenging because light undergoes double-pass propagation through the MMF, with mode coupling and dispersion; the backscattered signal is weak, and the camera records intensity only, without phase information. Here, we propose a single-shot reflection-mode MMF imaging framework that combines a reflected real-valued intensity transmission matrix (reflected-RVITM) with an image restoration network. The reflected-RVITM is calibrated using intensity-only measurements, without interferometry or phase retrieval, and provides a physics-guided initial reconstruction from a single backscattered speckle frame. A restoration network then refines this initial reconstruction instead of inverting the raw speckle. Four restoration backbones are evaluated: HPM-Attention-UNet, GAM, MambaIRv2, and CICPNet. On matched datasets, hybrid models outperformed corresponding networks trained to map raw speckle directly to images. For example, HPM-Attention-UNet on MNIST improved mean PCC from 0.572 to 0.944 (+65.1%). Under domain shift, with training only on Fashion-MNIST and tested on unseen CIFAR scenes, hybrid models achieved mean PCC of 0.61-0.65, compared with 0.36-0.50 for direct learning. This framework is further demonstrated using physical objects at the distal fibre tip. These results demonstrate that a reflected-RVITM physics prior combined with a restoration network enables single-shot image recovery after intensity-only calibration, offering a phase-retrieval-free and generalisable route towards minimally invasive reflection-mode MMF endoscopy.

In materials science, the integrity of scanning electron microscopy (SEM) images is paramount for quality control and validation of research outcomes. However, the introduction of sophisticated generative artificial intelligence, particularly Generative Adversarial Networks (GANs), has introduced a novel vulnerability: the potential for highly realistic, artificially synthesized SEM images to be used fraudulently in scientific literature. To address this challenge, we present a deep learning-based framework capable of distinguishing between authentic SEM images and those synthesized by Generative Adversarial Networks (GANs). Using FastGAN and StyleGAN2-ADA, two state-of-the-art GAN models, we generated synthetic SEM datasets to complement real imaging data. We fine-tuned a pre-trained Contrastive Language-Image Pre-training (CLIP) Vision Transformer (ViT-L-14) for binary classification. By unfreezing the final transformer blocks and appending a custom classification head, the model effectively captures the subtle, high-level artifacts inherent in GAN-generated upsampling. This work highlights the potential of deep learning to safeguard scientific imaging workflows and provides an important step toward detecting and mitigating image forgeries in materials science publications.

Surface phenotyping underpins plant science, preclinical animal research and entomology, yet across all three the measurement is almost always a photograph, which records a projection and not the surface itself. Here we present the Gentschinator3000, an open structured-light platform that brings high-end metric surface measurement within reach of laboratories with no optics expertise, combining documented open hardware, open reconstruction software and analysis workflows for under 4000 Euro in components. It resolves a planar reference to 45 m local flatness, registers full rotations to a loop closure of 156 m, and performs stably across acquisition ranges that we define. Applying one workflow to a leaf before and after desiccation, to murine anatomy and to a spread lepidopteran, we find that projection underestimates surface area by 11 to 41 %. That error grows with the condition under study, with the evaluation scale and with the direction of view, so it can confound phenotype comparisons dramatically. In murine limbs a 15-degree change of viewing direction shifts a projected inter-segment angle by up to 23.2 degrees, while the three-dimensional angle does not move. Projection geometry can therefore contribute as much to a measured phenotype as the biology it is meant to quantify.

High-speed three-dimensional imaging in scattering tissues remains challenging because volumetric microscopy generally requires scanning, whereas snapshot light-field approaches divide limited detector pixels among multiple views. This constraint is particularly severe in the second near-infrared window (NIR-II), where commonly used InGaAs cameras typically have relatively small sensor formats and high detector noise. Here we introduce NIR-II squeezed light-field microscopy (NIR-II SLIM), which optically rotates and compresses multiple perspective views before detection, allowing efficient use of camera pixels while retaining complementary spatial information for three-dimensional reconstruction. NIR-II SLIM acquires volumes at up to 600 volumes s-1 with a reconstructed lateral sampling grid of 512 x 512 pixels. We use the method for label-free four-dimensional imaging of cardiac dynamics in pigmented late-larval zebrafish, resolving chamber deformation and millisecond-scale atrioventricular-valve motion, and for NIR-II fluorescence imaging of vascular and lymphatic transport in mice. NIR-II SLIM provides a detector-efficient approach for high-speed volumetric imaging of rapid biological dynamics in scattering tissues.

The sea anemone Aiptasia is a model system for understanding cnidarian loss of symbiotic algae under heat stress (bleaching). While Aiptasia polyps have been widely used to study this process, accurate symbiosis phenotyping grapples with discordant length scales: fine spatial resolution (~100 um) is needed across a whole organism (~5 mm). To address this, we consider small (~100 um), optically transparent Aiptasia larvae as a bleaching model suitable for whole-organism phenotyping by fluorescence microscopy with larvae classified as symbiotic when algae are localized within gastrodermal cells. To expedite phenotyping, we introduce a machine-learning (ML) image-analysis pipeline (SYMPHONY) designed for single-larva resolution analysis of intact larvae. SYMPHONY efficiently identifies the cellular location of internalized algae (accuracy: 79%, precision: 82%, recall: 79%, F1 score: 79%; training dataset composed of 1611 total objects). Additionally, SYMPHONY reports statistically significant larval bleaching under heat stress and corroborates manual phenotyping results, while significantly reducing operator labor from hours to minutes. The combination of the Aiptasia larvae model and the SYMPHONY pipeline aims to accelerate our understanding of symbiosis breakdown.

While light-sheet microscopy is emerging as a robust method for volumetric imaging with improved axial resolution, its capability regarding two-dimensional, surface-level mapping is often hindered by limitations in data redundancy and reconstruction efficiency stemming from volumetric registration methods. We demonstrate that a multiview imaging approach in an axially-swept, dithered light-sheet microscope paired with computational image reconstruction of view projections is able to address these trade-offs to enable large-scale mapping of surface structural features, leveraging the advantages of multiview light-sheet in scalable field of view, working distance, and near isotropic resolution across the entire imaging depth. To aid in the acquisition and analysis of two-dimensional surface structures, we present a tailored surface mapping workflow and a Fiji plugin for computational reconstruction, promoting robust and comprehensive visualization of surface features of uncleared volumetric samples. Our strategy, termed projection reconstruction for imaging surface morphology (PRISM), integrates axially swept dithered light-sheet microscopy and post-processing software for multiview imaging. The imaging hardware enables near-isotropic resolution across its entire field of view, while the software implementation leverages rigid and affine transformations to align two-dimensional projections of multiview samples. It is designed to work with the BigStitcher pipeline, leveraging its robust algorithm to provide support for two-dimensional image alignment and stitching. We demonstrate the capability of PRISM in studies of lymphatic network mapping in the epicardial layer of intact mouse hearts, as well as surface profiles of FaDu spheroids labeled with antibody-nanodiamond conjugates. This method allows us to quantify cardiac lymphatic branch numbers, diameters, and lengths of a Prox1-tdTomato mouse cardiac model, as well as cluster number and diameters of epidermal growth factor receptor within a FaDu spheroid labeled with a nanodiamond-antibody conjugate, with a significant reduction of post-processing data size. PRISM leverages multiview image projections to promote studies of cardiac lymphatics in mouse models and surface receptor distributions within spheroid models, enabling efficient surface mapping of large, intact, and uncleared biological samples across a variety of scales.

Diffusion within the cytoplasm is fundamental to numerous biological processes. Fluorescence recovery after photobleaching (FRAP) is one of the most common method for quantifying molecular diffusivity in living cells using standard laser scanning confocal microscopy (LSCM). However, accurately measuring fast cytoplasmic diffusion (typically >10 m^2/s) is challenging due to rapid recovery kinetics, weak signal-to-noise ratios, post-bleach signal artifacts, and spatial restrictions affecting normalization. While individual challenges have been addressed in specific contexts, a simple and robust framework to quantify cytoplasmic diffusivity remains elusive. Here, we present a FRAP methodology specifically designed to overcome these obstacles. By utilizing the Gaussian function -- the impulse response (ImpRes) of the diffusion equation in an infinite medium -- our approach leverages the full spatiotemporal dataset through a single-equation three-parameter fitting procedure, thus releasing restrictions to small regions of interest and arbitrary initial time-points. The methodology was validated on three datasets of increasing complexity: in silico simulated recovery profiles, in vitro data from FITC-dextran in glycerol solution, and live-cell imaging of free cytoplasmic GFP. Systematic comparison with existing models demonstrates that the ImpRes approach significantly reduces sensitivity to noise and imperfect fluorescence normalization, while remaining robust against short-term biases, such as transient probe photo-activation. Given its robustness under realistic experimental conditions and its ease of implementation, the proposed FRAP methodology provides a reliable tool for quantitative cytoplasmic analysis.

Deep-learning-based segmentation algorithms have gained considerable accuracy for processing biological images. In particular, the introduction of large foundation models, novel architectures, and semantically varied datasets now allows for deployment of state-of-the-art models for clean image cohorts with limited re-training or, in the best of cases, in an out-of-the-box fashion. Biological imaging, however, is liable to corruptions that can hinder their deployment. While some methods document their robustness to the most common corruptions, a systematic robustness analysis of the state of the art to the expansive gamut of corruptions in biological imaging remains to be done. We perform this benchmarking by simulating 36 corruption types with varying degradation severity on images sampled from 30 different datasets. Our benchmark accounts both for the variety in biological images and the nature of corruptions. Among other things, our study reveals that performance on clean images does not correlate with overall robustness to image corruptions. In fact, we find that a decade-old method, StarDist, is more robust than many of its more recent foundation-model-based counterparts. We also show in a dedicated representation analysis that the performance of segmentation models collapses in the early layers of the encoding phase.

Light reaching the retina is a primary regulator of human circadian physiology, acting largely through melanopsin-expressing retinal ganglion cells with peak short-wavelength sensitivity. Delivering known, repeatable retinal doses outside the laboratory is difficult because conventional light sources leave viewing geometry, gaze, and ambient conditions uncontrolled. Consumer extended-reality (XR) glasses fix a bright binocular display in constant geometry relative to the eye, but their suitability as calibrated photic stimulators has not been established. Here we validate a commercial micro-OLED XR display (VITURE Luma Ultra) for controlled retinal photostimulation. A purpose-built host application renders exact 8-bit RGB stimuli while independently controlling hardware brightness and logging all intensity-determining state; spectral radiance was measured at the retinal position of a 3D-printed phantom head with an open-source miniature spectroradiometer, anchored to absolute units by a luminance transfer calibration. The blue primary peaks at 461 nm (FWHM 43 nm), is spectrally invariant across a >10-fold intensity range, and at maximum output delivers an estimated 299 lx melanopic equivalent daylight illuminance, above consensus daytime recommendations, while remaining roughly two orders of magnitude below photobiological safety limits. The red primary is visually effective with minimal melanopic drive (melanopic DER 0.10), enabling spectrally shifted evening stimulation. Unlike the immersive virtual-reality headsets previously used for calibrated light delivery, the see-through form factor preserves the wearer's view of the surroundings--relevant for clinical monitoring in supervised settings such as the intensive care unit. These results show that consumer XR glasses can serve as a dose-calibrated platform for wearable photostimulation using an open-source measurement chain, and provide groundwork for application-layer dose-response studies.

Label-free Raman imaging of living cells is photon limited: at exposures compatible with cellular dynamics, single-pixel spectra carry about one count per channel on a dominant smooth background. We present an unmixing framework in which the decoder of a physics-constrained autoencoder is restricted to a data-driven spectroscopic dictionary: band centers,widths, and pseudo-Voigt shapes are measured from the dataset and fixed, and the network learns only nonnegative band amplitudes, a smooth B-spline background, and a per-pixel gain.First, on slit-scanning images of HeLa cells (532 nm) the dictionary yields spike-free component spectra that read as band tables, including a resonance-enhanced cytochrome-c-associated component matching literature spectra, and the most stable decomposition against the component number. Second, the dictionary and initialization calibrated at 1 s exposure perline transfer to 100 ms per line (12 s sweeps): cytochrome-c spectral identity survives a single sweep (correlation 0.92) while its map remains photon limited; the dictionary provides spectral physicality, and the transferred initialization prevents a structural collapse that global map correlations miss; in a measurement-derived phantom the dictionary estimator holds thecytochrome-c spectrum to 17-19{degrees} spectral angle at 100 ms, where classical factorizations and free decoders lose it (55-64{degrees}). Estimation on the count-equivalent detector output uses a calibrated shifted-Poisson quasi-likelihood. Third, evaluation must be time matched:correlation against a separately acquired reference saturates through slow specimen drift and acquisition mismatch rather than photon noise, and the self-consistency of learned denoisers is inflated by shared bias; time-matched self-consistency and independent cross-checks areproposed.

The development of a new integrated operant system was driven by two challenges in behavioral neuroscience: the high cost and technical complexity of commercial rigs, and their limited adaptability across experiments. We developed the NeuroHab, an integrated behavioral arena for high-fidelity operant conditioning and automated data collection in a single unified system. Food and water reward, conditioned-stimulus presentation, and event recording are tied together programmatically with easy-to-install open-source code to facilitate throughput and reproducibility. All behavioral events are processed by internal microcontrollers and logged with <1 ms latency (typical range 56-728 s). This precise timing is critical for integrating the system with two-photon imaging and electrophysiology, enabling real-time alignment of behavior with brain activity. The NeuroHab uses solenoid-actuated, capacitive-sensing Lickports that let an untethered mouse drink from an automated port, and delivers food via the Kravitz Lab FED3. Conditioned stimuli are presented by dedicated buzzer/LED modules. A central controller (the Core) coordinates all modules and logs event timestamps using TTL-low signaling between two microcontrollers, at a maximum recording rate of 16.67 Hz for single-pulse events. We have deployed the NeuroHab in over 50 behavior trials and over 20 sessions alongside a Mini two-photon microscope. At approximately $1,400, easily modified, and compatible with existing analysis tools, the NeuroHab lowers barriers to multimodal behavioral neuroscience. Significance StatementThe study of how neural activity gives rise to behavior depends on operant systems that are both temporally precise and affordable, yet commercial rigs are costly and difficult to adapt across experiments. We introduce the NeuroHab, an integrated, open-source operant platform that unifies reward delivery, conditioned-stimulus presentation, and event logging with sub-millisecond timing (typical latency 56-728 s). Built for approximately $1,400, the system forwards all behavioral timestamps to external acquisition hardware, enabling millisecond-scale alignment of behavior with two-photon imaging and electrophysiology. By lowering the cost and technical barriers to synchronized behavioral and neural recording, the NeuroHab makes multimodal, reproducible operant neuroscience accessible to a broad range of laboratories and adaptable to diverse experimental paradigms.

Quantitative fluorescence imaging techniques such as fluorescence lifetime imaging microscopy and hyperspectral imaging infer molecular contrast from photons distributed across spatial pixels and temporal or spectral channels. In the few-photon regime, however, conventional pixel-wise analysis discards the spatial relationships imposed across neighboring pixels by the microscope point-spread function (PSF). Here we show that this spatially distributed information can be recovered without prior knowledge of emitter positions, spatial support or component assignments. We introduce SPOOL (Spatially Pooled Optical Observation Likelihood), a training-free Poisson inverse framework that jointly recovers source-space amplitudes and quantitative contrast by combining the PSF with temporal-decay or spectral-response dictionaries. For an isolated source, the attainable precision gain is governed by a dimensionless optical quantity: the PSF width expressed in detector pixels. The predicted gain therefore scales with optical sampling rather than with the physical origin of the contrast. The model predicts that lifetime-precision gain scales approximately linearly with the number of pixels spanning the PSF full width at half maximum, a scaling reproduced by Monte Carlo simulations. At one detected photon per foreground pixel, the reconstruction reduces lifetime dispersion sixfold in fluorescent-bead experiments and decreases the lifetime root-mean-square error relative to a high-photon reference from 1.19 to 0.45 ns in dual-labeled cells. The same framework transfers unchanged to hyperspectral imaging, recovering spectral contrast from generic emission bands without prior fluorophore spectra.

Motion artifacts are almost unavoidable in functional time-lapse and structural volumetric multiphoton microscopy. They arise from respiration, heartbeat, locomotion, awake behavior, instrument heating, mechanical vibration, and slow drift, while the recorded signal is often photon-limited, blurred by scattering, and biologically time varying. Consequently, motion correction is frequently an essential prerequisite for quantitative bioimage analysis rather than a merely cosmetic preprocessing operation. Edge- and landmark-centric registration strategies are often poorly matched to these data because useful structures may be sparse, diffuse, out-of-focus, or changing in fluorescence intensity. We present ZenReg, an open-source Python platform that formulates common 2D+t, 3D, and 3D+t microscopy registration tasks as a modular family of geometry-preserving alignment problems. ZenReg combines Fourier phase correlation, intensity-based StackReg-style alignment, NoRMCorre-style piecewise translation fields, projection-based rotation estimates, dense SimpleITK-based six-degree-of-freedom volume registration, and sparse point-based rigid-volume registration within one canonical microscopy stack model. The platform uses OMIO to normalize heterogeneous microscope files and to preserve metadata, while optional disk-backed Zarr arrays support chunked, memory-efficient processing of image stacks that exceed available memory or reside on remote storage. ZenReg writes registered images together with shift tables, correlation metrics, summary plots, and machine-readable settings. In synthetic benchmarks with known ground truth, ZenReg recovered global 2D and 3D translations with subpixel accuracy across moderate noise and drift regimes, while high-noise and large-drift tests separated the backend behavior: FFT-based methods failed abruptly once image information or shared support became insufficient, StackReg degraded more gradually under severe noise, and piecewise NoRMCorre improved spatially varying local-motion correction where a single global transform was inadequate. Parallel execution reduced runtime for large time series, and ZenReg provided practical full-volume rigid correction for dense and puncta-rich 3D+t stacks. By coupling a modular, extensible backend architecture to transparent sidecar outputs, ZenReg makes motion correction easier to extend, inspect, share, reproduce, and reuse as part of scientific image analysis.

Visual neuroscience requires precise spatiotemporal projection of optical stimulation onto the retina, especially in experimental mouse models. However, in vivo patterned stimulation in mice is profoundly hindered by the extreme optical power and severe anatomical aberrations of the eye. Consequently, visual stimulation relies mainly on unverifiable, open-loop approximations that often lack spatial precision. Here, we introduce a closed-loop, spatially modulated stimulation platform that overcomes these barriers. By integrating a digital micromirror device (DMD) with electronically tunable lenses (ETLs) and a real-time, fundus camera-guided focus optimization module, we directly verify the location of patterned stimuli on the retina while dynamically correcting for chromatic and geometric defocus. This platform delivers quantitatively verified static and dynamic patterned stimuli to the living retina with lateral resolutions as fine as 6.7 {micro}m. Guided by ray-tracing optical analysis, our work establishes a technological foundation that enables highly reproducible, cellular-scale interrogations of the visual pathway.

Understanding how the brain supports complex cognition in real-world environments requires neuroimaging systems that impose minimal constraints on natural behavior. Existing high-fidelity modalities, such as functional magnetic resonance imaging (fMRI), confine participants to the scanner, while wearable alternatives sacrifice spatial resolution or cortical coverage. Here, we developed a fully untethered whole-head optical neuroimaging system that achieves high-fidelity tomographic reconstruction through dense spatial sampling, configurable source multiplexing, and high dynamic range detection. This wearable high-density diffuse optical tomography (WHD-DOT) system achieves 151 dB effective dynamic range and nearly 3000 source-detector measurements, comparable to the highest-performing fiber-based DOT systems, while maintaining wireless, battery-powered mobility. We validate WHD-DOT across three paradigms of increasing ecological complexity, ranging from standard functional localizers to naturalistic movie viewing and live piano performance. Across all paradigms, WHD-DOT produces robust, well-localized encoding, repeatable single-trial responses, and above-chance decoding of stimulus-specific dynamics. Piano performance, which requires continuous bimanual movement and an unconstrained posture, is a rigorous real-world test for a wearable neuroimaging system. By decoding song-segment identity of free piano performance at 71.1% accuracy (chance 12.5%), WHD-DOT shows that brain activity from unconstrained, real-world behavior, previously beyond reach of high-fidelity imaging, is now both measurable and decodable.

PurposePupil diameter in daily life depends on both the light reaching the eye and the observers age, but established prediction formulas require laboratory quantities that are rarely measured in natural environments. We developed a compact age-corrected model that predicts pupil diameter from melanopic equivalent daylight illuminance (mEDI). MethodsWe used an existing field dataset in which binocular pupil diameter and near-corneal spectral irradiance were recorded while 83 adults aged 18-87 years moved through indoor and outdoor environments. The analysis included 10,082 valid paired observations. We fitted a bounded sigmoid relating pupil diameter to mEDI and age, with each participant given equal influence, and assessed prediction in participants excluded from model fitting. Performance was compared with simpler models, a flexible generalised additive model (GAM), and Watson-Yellott predictions based on assumed field geometry. ResultsPupil diameter decreased smoothly as mEDI increased. Age primarily reduced the difference between pupils in dim and bright conditions, by 0.768 mm per decade, while the predicted bright-light diameter changed little with age. In held-out participants, the bounded model had a participant-balanced root mean squared error (RMSE) of 0.630 mm and mean absolute error of 0.537 mm. The GAM had a slightly lower point-estimate RMSE of 0.610 mm, but the difference was small and uncertain. The bounded model outperformed the tested log-linear, reduced, age-only, and Watson-Yellott alternatives. ConclusionAge and mEDI are sufficient to provide useful population-average pupil predictions across the observed adult age and real-world light range. The model is transparent, physiologically bounded, and nearly as accurate as a flexible GAM, but predictions approaching darkness remain uncertain because valid mEDI measurements were not available in that range. Key pointsO_LIA compact equation predicts population-average pupil diameter from age and mEDI alone. C_LIO_LIAge mainly compresses the pupils response range by reducing pupil diameter under dimmer conditions. C_LIO_LIPrediction error in unseen participants was close to that of a flexible GAM, without requiring a fitted smooth object. C_LIO_LIThe model is intended for the observed adult age and field-light range, not for extrapolation into darkness. C_LI

Insects solve complex behavioural tasks with remarkable efficiency, using minimal neural hardware tuned to the specific requirements of their ecological niches. To truly understand or replicate these behaviours, it is insufficient to model the brain in isolation: one must account for the dynamic, closed-loop interactions between the environment, the physical organisation of the sensory periphery, and internal biophysical dynamics. To address these issues for visually controlled behaviours, we present RhabdoForge, a modular, hardware-agnostic and high-performance rendering framework specifically designed for insect neuroethology and neuromorphic research. Designed for seamless integration into Python-based workflows, RhabdoForge implements both real-time ray-tracing and stochastic path-tracing using hardware-agnostic GPU pipelines. Crucially, the engine moves beyond the static "ommatidium-as-a-pixel" paradigm by introducing a fully parametrisable model where every layer of the compound eye (from the geometric shape and the topological lattice to the internal rhabdomere blueprint) is a discrete, swappable component. The engine is capable of simulating the high-frequency, sub-ommatidial rhabdomere photomechanical actuation, allowing for the investigation of a variety of active sensing phenomena within a real-time closed-loop environment. The framework also includes an automated morphological pipeline that allows transforming 2D anatomical data into faithful 3D sensory models. We validate the engine through two case studies: a closed-loop optic-flow centring response in a virtual tunnel, and the recovery of spatial hyperacuity via rhabdomere microsaccades. By providing a bridge between high-fidelity visual ecology and neuromorphic modelling, RhabdoForge enables researchers to explore how the interplay of sensory optics and neural processing can generate complex behaviour in both biological and artificial agents.