openretina: Collaborative Retina Modelling Across Datasets and Species

Studying the retina plays a crucial role in understanding how the visual world is translated into the brain’s language. As a stand-alone neural circuit with easily controllable input, the retina provides a unique opportunity to develop a complete and quantitatively precise model of a computational module in the brain. However, decades of data and models remain fragmented across labs and approaches. To address this, we have launched an open-source retina modelling platform on a shared GitHub repository, aiming to provide a unified data and modelling framework across species, recording techniques, stimulus conditions, and use cases. Our initial release consists of a Python package, openretina , a modelling framework based on PyTorch, which we designed for optimal accessibility and extensibility. The package includes different variations on a basic “Core + Readout” model architecture, easily adaptable dataloaders, integration with modern deep learning libraries, and methods for performing in-silico experiments and analyses on the models. We illustrate the versatility of the package by providing dataloaders and pre-trained models for data from several laboratories and studies across species. With this starter pack in place, openretina can be used within minutes. Through step-by-step examples, we here provide retina researchers of diverse backgrounds a hands-on introduction to modelling, including using models as tools for visualising retinal computations, generating and testing hypotheses, and guiding experimental design.