# Novel experimental and machine learning - assisted techniques to assess receptive field functionality in the retina

> **NIH NIH R01** · UNIVERSITY OF COLORADO DENVER · 2024 · $532,806

## Abstract

PROJECT SUMMARY
In the mouse retina, about 40 types of retinal ganglion cells (RGCs) communicate visual information to the rest
of the brain. A great deal of processing takes place before RGCs send their output downstream. Some RGCs
respond selectively to a narrow range of shapes, contrasts, and directions of motion or prefer localized stimuli
that move differentially from their surroundings. These computations are supported by interactions between
more than a hundred interneurons whose interactions give rise to the receptive fields (RFs) that describe the
relationship between the stimulus to the response of the RGC.
However, despite significant recent advances in the field, we still do not know what visual features are detected
by the majority of RGC types. One obstacle to progress is current techniques to study RF composition, which
either require prolonged recording sessions, challenging experimental techniques, or fail to detect crucial RF
components. We are also limited in the conceptual understanding of how neural circuit organization translates
to function and what RF motifs give rise to specific visual computations.
In this proposal, we will take an innovative approach that combines machine learning techniques, biophysically
realistic modeling, electrophysiology, and glutamate / calcium imaging to develop a comprehensive description
of the visual abilities of multiple RGC types in complex visual scenes that is grounded in empirical data.
The proposed research will substantially advance our understanding of basic and advanced response
characteristics of visually active cells, opening new horizons in the examination of neuronal function in and
beyond the retina.

## Key facts

- **NIH application ID:** 10913479
- **Project number:** 5R01EY035293-02
- **Recipient organization:** UNIVERSITY OF COLORADO DENVER
- **Principal Investigator:** Alon Poleg-Polsky
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $532,806
- **Award type:** 5
- **Project period:** 2023-09-01 → 2027-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10913479

## Citation

> US National Institutes of Health, RePORTER application 10913479, Novel experimental and machine learning - assisted techniques to assess receptive field functionality in the retina (5R01EY035293-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10913479. Licensed CC0.

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