Federico D'Agostino

We present the ALL-GCL dataset, a large-scale resource of functional two-photon Ca2+-imaging recordings with rich metadata information from more than 80,000 cells in the ganglion cell layer (GCL) of the ex vivo mouse retina. Collected over nine years across more than 139 experimental sessions, the dataset provides recordings of light-evoked responses to various stimuli, including a shared set of core stimuli. To enable cell-type-specific analyses, cells are probabilistically assigned to 46 previously characterized functional groups, including retinal ganglion cells and displaced amacrine cells. Further, we assessed the influence of experimental and biological factors on the functional responses and found only small batch effects across experimenters, setups, and recording sessions, highlighting the dataset's consistency. The ALL-GCL dataset offers a comprehensive and standardised reference for studying retinal computation at scale. It supports population-level analyses, computational modelling, and the development of machine learning approaches for biological time-series data. Future releases will expand the dataset with additional mouse lines and light stimuli, creating a growing resource for the vision science community.

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 platform’s 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.

High-density probes allow electrophysiological recordings from many neurons simultaneously across entire brain circuits but don’t reveal cell type. Here, we develop a strategy to identify cell types from extracellular recordings in awake animals, revealing the computational roles of neurons with distinct functional, molecular, and anatomical properties. We combine optogenetic activation and pharmacology using the cerebellum as a testbed to generate a curated ground-truth library of electrophysiological properties for Purkinje cells, molecular layer interneurons, Golgi cells, and mossy fibers. We train a semi-supervised deep-learning classifier that predicts cell types with greater than 95% accuracy based on waveform, discharge statistics, and layer of the recorded neuron. The classifier’s predictions agree with expert classification on recordings using different probes, in different laboratories, from functionally distinct cerebellar regions, and across animal species. Our classifier extends the power of modern dynamical systems analyses by revealing the unique contributions of simultaneously-recorded cell types during behavior.