# Integrating visual counterevidence to detect self-motion in a small visual circuit

> **NIH NIH R01** · YALE UNIVERSITY · 2023 · $401,323

## Abstract

Animals perceive optic flow and use it to guide a range of navigational behaviors, such as stabilizing body
trajectories and eye movements. In this project, we propose to dissect how different visual cues are combined
to guide stabilizing behaviors, and how these cues serve as evidence for and against an animal’s self-motion.
We will investigate these computations in the fruit fly Drosophila, which allows us to use powerful genetic tools
to define the roles of individual neurons in neural computations. With these tools, our research will (a)
characterize the algorithms that combine types of visual evidence, (b) identify the circuits that encode and
combine these cues, and (c) determine how neurons perform these computations. This work is significant for
two reasons. First, our studies will examine how the fly integrates visual evidence both for and against its own
self-motion. This form of counterevidence integration cannot be accounted for by current models of optic flow
detection. Thus, this project will establish new approaches to studying how animals estimate their own self-motion. Second, the types of visual evidence for and against self-motion are constrained by the geometry of
the visual world. Because of this common geometry and parallels in visual processing between flies and
mammals, it is likely that mammalian visual systems make use of algorithms similar to those we will find in the
fly. Thus, analysis in the compact fly brain will uncover principles for understanding these computations in the
brains of larger animals. In our research, we will combine genetic tools and behavioral measurements to
investigate three questions: How do different visual cues interact in stabilizing the fly’s heading? What circuitry
is required for the different types of cues? And how do the circuits process and integrate these cues to
generate behavior? These questions lead to the three aims of our research. Aim 1 characterizes the algorithm
that integrates evidence for and against the fly’s self-motion and guides its turning behavior. Behavioral
measurements with targeted visual stimuli will constrain or rule out potential models. Aim 2 identifies neurons
required to integrate visual counterevidence into turning behavior. We will use genetic tools to silence specific
neurons in the visual system in order to identify neurons required for this computation. Aim 3 measures
functional response properties of visual neurons in the circuits that encode and integrate these visual cues. We
will use measurements of neuron responses to stimuli to test hypotheses for how these neurons combine
signals to generate the observed motion signals and behaviors. On completion, these studies will result in a
detailed understanding of the algorithms and neural mechanisms that integrate different visual cues to stabilize
heading in flies. This will provide a template for understanding how visual evidence is combined to estimate
self-motion in other animals as well.

## Key facts

- **NIH application ID:** 10604346
- **Project number:** 5R01EY026555-08
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Damon Alistair Clark
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $401,323
- **Award type:** 5
- **Project period:** 2016-04-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10604346

## Citation

> US National Institutes of Health, RePORTER application 10604346, Integrating visual counterevidence to detect self-motion in a small visual circuit (5R01EY026555-08). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10604346. Licensed CC0.

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