# Neural circuits for visual feature detection

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2022 · $380,464

## Abstract

A significant percentage of people in the US suffer from disabilities resulting from traumatic
injury, stroke, or degenerative disease which cause deficits in visual perception. Therefore, an
understanding of the basic circuit neurobiology and neuromodulation of feature-based visual
perception will sharpen our understanding of the mechanism of visual processing, and should
facilitate the development of treatments for these disabilities.
 One form of visual attention is targeted to salient features of the visual scene, during
which a subject detects optical disparities that distinguish a salient object from the cluttered
visual surroundings. The cell-circuit mechanism for how this occurs is not well understood. This
renewal project will capitalize on recent discoveries and significant experimental advantages of
the fruit fly Drosophila to explore the elementary neural circuitry required for feature-based
visual attention. The fly has a numerically simple nervous system, with which highly advanced
genetic techniques can be used to identify, manipulate, and repeatedly record the activity of
individual neurons, as well as their upstream and downstream network partners. The fly also
displays robust feature-based visual perception, even under stimulus conditions that defeat
classical models of motion vision, for which similar processes have been localized to cortical
pathways in humans and non-human primates. The PI hypothesizes that flies detect and
discriminate the higher-order features of visual objects with specialized circuits that integrate
first-order elementary motion signals retinotopically, which are further enhanced by the action of
inhibitory neurotransmitters. The PI will perform two-photon Ca2+ imaging of candidate cellular
pathways in response to stimuli the PI has discovered to elicit robust feature detection by flies
within a virtual reality flight simulator. Armed with physiological receptive fields, the PI will use
live imaging to ‘read’ and optogenetics to ‘write’ activity patterns in a behaving fly to directly
observe input-output functions of feature detection processing on visual behavior. Finally, the PI
will study how biogenic amines modulate the functional properties of feature detecting neurons
to enable plasticity required for visual feature detection in switching behavioral contexts such as
the transition from stationary quiescence to active locomotion.

## Key facts

- **NIH application ID:** 10369404
- **Project number:** 2R01EY026031-06A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Mark Arthur Frye
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $380,464
- **Award type:** 2
- **Project period:** 2016-05-01 → 2025-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10369404

## Citation

> US National Institutes of Health, RePORTER application 10369404, Neural circuits for visual feature detection (2R01EY026031-06A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10369404. Licensed CC0.

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