openretina: Collaborative Retina Modelling Across Datasets and Species

The retina provides a unique opportunity to develop a complete and precise model of a computational module in the central nervous system. Deep learning has recently vastly advanced efforts towards this goal, yet decades of data, code, and analysis practices remain fragmented between labs -- limiting reproducibility, comparison, and cumulative progress. We argue that an open, collaborative modelling ecosystem is now essential to move the field from isolated studies toward a unified, quantitative account of retinal computation. To this end, we present openretina, a modular Python package built on PyTorch that provides a standardised framework for training, evaluating, and interpreting neural network models of the retina. The package implements a shared "Core + Readout" model architecture with a reproducible training pipeline, a common data format based on HDF5, unified evaluation metrics, and in silico analysis techniques from the literature. In its initial release, openretina integrates five publicly available datasets spanning various species and recording modalities. For each dataset, we provide curated preprocessing, standardised data loaders, and pre-trained model checkpoints that serve as reproducible baselines for benchmarking new approaches. We demonstrate the platforms utility through example use cases: first, a gradient field analysis linking the instability of optimal stimuli to spatial contrast encoding in ON-OFF retinal ganglion cells; second, systematic benchmarking of architectures within and across datasets, revealing that substantial explainable variance remains uncaptured by current models. By making research tools interoperable across laboratories, openretina lays the groundwork for closing this gap collectively.