Retinal circuits in silico: A review of modern retina models and a vision for their future

Over the last two decades, models of visual processing in the retina have increased in scale and predictive power, driven by advances in recording technologies and deep learning. Current models span a spectrum from functional, high-performance architectures to detailed biophysical simulations. Here, we survey the state-of-the-art across this spectrum, with a focus on the key developments needed to leverage these models to improve our understanding of retinal processing. We argue for a collective effort to build a scientific ecosystem around a core of shared datasets, modular composable models, and standardised benchmarks. We propose that such an infrastructure would accelerate model-driven discovery and enable the community to focus its efforts on open scientific questions such as the role of neural variability, adaptation, and cell-type diversity in retinal coding.