# Retinal Circuitry

> **NIH NIH R01** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2022 · $298,808

## Abstract

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.

## Key facts

- **NIH application ID:** 10593874
- **Project number:** 3R01EY028927-04S1
- **Recipient organization:** UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH
- **Principal Investigator:** Bryan William Jones
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $298,808
- **Award type:** 3
- **Project period:** 2019-01-01 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10593874

## Citation

> US National Institutes of Health, RePORTER application 10593874, Retinal Circuitry (3R01EY028927-04S1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10593874. Licensed CC0.

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