RhabdoForge: A Modular, Biophysically-Grounded Rendering Framework for Insect Vision Neuroethology
Abstract
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, waveguide optics, intracellular pupil adaptation, and retinal sheet movements, 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 demonstrate the engine through two case studies: a closed-loop optic-flow centring response in a virtual tunnel, and a decomposition of the mechanisms that can contribute to spatial hyperacuity (axial and lateral rhabdomere microsaccades, aperture clipping, and the intracellular pupil). 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.
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BibTeX
@misc{moel2026rhabdoforge,
title = {{RhabdoForge: A Modular, Biophysically-Grounded Rendering Framework for Insect Vision Neuroethology}},
author = {F. Le Moël and B. Webb},
howpublished = {bioRxiv},
year = {2026},
month = aug,
doi = {10.64898/2026.08.25.747007},
url = {https://www.biorxiv.org/content/10.64898/2026.08.25.747007v2},
note = {CC BY},
}
RIS
TY - UNPB
TI - RhabdoForge: A Modular, Biophysically-Grounded Rendering Framework for Insect Vision Neuroethology
AU - F. Le Moël
AU - B. Webb
PY - 2026
DA - 2026/08/25
PB - bioRxiv
DO - 10.64898/2026.08.25.747007
UR - https://www.biorxiv.org/content/10.64898/2026.08.25.747007v2
AB - 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, waveguide optics, intracellular pupil adaptation, and retinal sheet movements, 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 demonstrate the engine through two case studies: a closed-loop optic-flow centring response in a virtual tunnel, and a decomposition of the mechanisms that can contribute to spatial hyperacuity (axial and lateral rhabdomere microsaccades, aperture clipping, and the intracellular pupil). 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.
ER -