EY028927 Project/Supplement Summary This supplement for EY028927 responds to NOT-OD-22-067 and is crafted around assembly, annotation and analysis of ultrastructural data from non-human primate, and human retina, providing a normal circuit topology framework as well as disease frameworks allowing comparison of normal to pathological retinal networks that emerge in disease. Connectomes are Rosetta Stones for discovering how retinas are wired and reveal how retinal structure and function are altered by remodeling in retinitis pigmentosa (RP) and age-related macular degeneration (AMD). Prior research efforts have unmasked unexpected, pervasive complexities in mammalian retinal networks, informing neuronal modeling and modeling of retinal prosthetics. This supplement will fund tool development to enable sharing of specific aspects of our datasets in great demand from the AI/ML community. Our work has built not only connectomics infrastructure for datasets, but the annotation work within the datasets themselves provides a valuable ground truth to feed AI/ML approaches as training data. While our annotated databases have been the highest resolution connectomics databases yet available, allowing discrimination of synapses and gap junctions as well as organelle data, we do not have good tools to subset these connectomes to feed AI/ML approaches allowing feature detection of synaptic features or sub-cellular features desired by the AI/ML community. The tools deriving from this supplement will interface with our open-source datasets, providing the entire connectomics community access to rich, validated, ground-truth data for AI/ML training and mining.

EY028927 Project/Supplement narrative This supplement proposal will fund the creation of new tools to facilitate data export and collaboration with AI/ML approaches and research groups. We've identified two distinct mechanisms that will facilitate sharing of our connectomics datasets and annotations: (1) through modernizing our public facing web application programming interface (API) and (2) by creating shippable datasets. These tools will facilitate AI/ML collaborations and development of additional resources to understand retinal neural networks.