# Neural coding of interneuron populations in the retina

> **NIH NIH R01** · STANFORD UNIVERSITY · 2020 · $385,215

## Abstract

Abstract
The vertebrate retina translates visual images into electrical signals in the optic nerve, initiating the
basis of all visual perception. This process is accomplished by dozens of diverse types of interneurons,
each of which comprises a population of many thousands of cells. Each of these populations cover the
visual field, acting together to process different aspects of visual images. Although many informative
studies of retinal neural function have used single cell recordings, understanding the coordinated
actions of many cells requires the recording and analysis of cell populations. This proposal focuses on
amacrine cells, a diverse population of inhibitory interneurons. In particular we study wide-field
amacrine cells, a prominent class of cells that make long distance connections across the retina, acting
to combine visual signals from distant locations in the image. We have little information assigning
computations to specific cells of this type. Using genetically identified populations of wide-field
amacrine cells in the mouse retina, we will record neural activity from these populations optically, along
with simultaneously recording electrically from populations of retinal ganglion cells. Neural responses to
complex stimuli including natural scenes will be interpreted using advanced computational models. The
primary goals of these studies are to 1) perform the first population scale measurements of sparse
wide-field amacrine cells, in particular to measure how their selectivity for visual features varies
dynamically during natural scenes, 2) Analyze the neural code of these cells under natural scenes
using state-of-the-art computational models that can capture retinal responses to arbitrarily complex
stimuli, 3) Test the hypothesis that sparse wide-field amacrine cells perform similar computations on
different channels of information, acting to remove correlations from the ganglion cell population during
natural scenes. These results will have immediate applicability to the emerging field of retinal
prostheses, as is used to treat prevalent diseases such as age-related macular degeneration and
retinitis pigmentosa by replacing the function of the damaged retina with a high resolution electronic
circuit. Measurements of the retinal neural code and the computations that are performed will be
directly useful for incorporation into retinal prosthesis systems.

## Key facts

- **NIH application ID:** 10052264
- **Project number:** 2R01EY025087-06A1
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** STEPHEN A BACCUS
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $385,215
- **Award type:** 2
- **Project period:** 2014-12-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10052264

## Citation

> US National Institutes of Health, RePORTER application 10052264, Neural coding of interneuron populations in the retina (2R01EY025087-06A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10052264. Licensed CC0.

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