# Investigating invariant motion encoding in mouse visual cortex

> **NIH NIH F31** · DUKE UNIVERSITY · 2024 · $41,901

## Abstract

PROJECT SUMMARY
A fundamental feature of perception is the ability to recognize objects or object features despite changes in
presentation, such as size, position, or context. Elucidating the mechanisms by which cortical populations and
circuits build representations that are invariant to these manipulations will significantly advance our
understanding of the biological underpinnings of perception. Based on limited biological work examining invariant
tuning properties of neurons in visual cortex, studies in the field of object recognition hypothesize that invariance,
or tolerance to identity-preserving transformations, increases gradually as signals ascend the visual processing
hierarchy. In contrast, studies investigating how invariance is computed for perception of motion across different
object types have hypothesized that different types of invariances are computed in discrete, subsequent steps.
For example, humans and nonhuman animals alike perceive the pattern direction of motion of drifting plaids
despite the fact that plaids are composed of two overlaid gratings that drift in divergent directions. This perception
requires direction invariance (tolerance to the component grating directions of motion) and spatial invariance
(tolerance to differences in spatial features across different types of plaids). Previously published work proposes
that direction and spatial invariance are computed in series. First, spatial invariance is achieved by complex,
direction selective cells in V1 and next, direction invariance is computed upon integration of the signals encoding
component directions of motion in higher order areas of cortex. However, data from other studies and my own
preliminary data do not agree with this two-stage model. We find direction invariance in V1 prior to the
representations achieving spatial invariance. These data suggest that the computation for spatial invariance for
pattern motion encoding may occur gradually, as is proposed in object recognition pathways. Therefore, my
central hypothesis is that motion encoding circuits compute spatial invariance gradually over multiple
transformations to build an invariant percept of pattern motion. Using multiphoton imaging and optogenetic
manipulations in mouse visual cortex, I will test this hypothesis with two specific aims. In Aim 1, I will test if spatial
invariance increases gradually throughout the processing hierarchy and, if so, whether the increase is inherited
by receiving biased inputs that come from the most invariant neurons in the preceding population, or if spatial
invariance is computed de novo upon integration of heterogenous inputs. In Aim 2, I will investigate how single
cell and population level pattern motion representations inform pattern motion perception by training mice in a
direction discrimination task. Overall, the significance of this work will be to advance our understanding of how
invariance is built for pattern motion perception, resolving current controver...

## Key facts

- **NIH application ID:** 10994831
- **Project number:** 1F31EY036693-01
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Sara Marie Gannon
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $41,901
- **Award type:** 1
- **Project period:** 2024-09-01 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10994831

## Citation

> US National Institutes of Health, RePORTER application 10994831, Investigating invariant motion encoding in mouse visual cortex (1F31EY036693-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10994831. Licensed CC0.

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