# Neural circuit computations for visual motion during natural primate behaviors

> **NIH NIH UF1** · UNIVERSITY OF TEXAS AT AUSTIN · 2020 · $3,344,560

## Abstract

PROJECT SUMMARY / ABSTRACT
Our current understanding of primate motion perception is often lauded as one of the great achievements of
computational systems neuroscience. Due to its early successes in explicating the fundamentals of neural
coding and relations between brain activity and perception, and further constrained by the practical limitations
of the macaque model, it has remained rooted in conventional experimental approaches. Despite the
important, influential concepts that this approach has produced, here, we step outside this paradigm and
highlight that this knowledge needs to be aligned with the major challenges actually posed to the brain during
real motion perception. We propose a comprehensive, data-driven, quantitatively-rooted research program that
exploits unique opportunities provided by the marmoset model system. The proposed framework and
experiments leverage the smooth brains and relatively small body size of marmosets, as well as their
amenability to naturalistic testing paradigms in laboratory environments. This work lays the experimental,
conceptual, and technical groundwork for a computational neuro-ethology framework in which real-time closed
loop recording and perturbation of both neural activity and behavior can be used to probe the neural
computations underlying natural primate behaviors.
 Specific Aim 1. Acquisition and characterization of real, binocular visual motion (including both
optic flow and object motion) in freely moving marmosets and humans, performing natural visually-
guided navigation and foraging in real and augmented reality (AR) environments. We will place
marmosets in realistic, natural environments and allow them to hunt moving prey and to forage for stationary,
partially hidden food items. We will measure their body and eye movements, as well as their views of the visual
scene, to build a quantitative model of natural image/movie statistics that captures the effects of self-motion
and object motion in the real world, and to assess the task-specific nature of these statistics. We will also
acquire and analyze eye, head, body, and natural movie data from humans performing behaviors that map on
to the studied marmoset behaviors to assess the commonalities and differences across primates.
 Specific Aim 2. Large scale, multi-area recording of activity in V1, MT, and MST in marmosets
viewing realistic, complex visual motion and optic flow patterns in both freely moving conditions and
head-fixed virtual reality (VR). We will record activity in the visual areas of freely-moving marmosets
performing natural tasks and behaviors, and apply intuitive but sophisticated statistical analyses to link the
neural activity to the visual inputs and the detailed body movements. This will provide the first characterization
of primate visual system activity during natural vision and action. We will use a head-fixed VR environment to
perform detailed tests of how self-motion modulates visual responses, as well as to perf...

## Key facts

- **NIH application ID:** 9964469
- **Project number:** 1UF1NS116377-01
- **Recipient organization:** UNIVERSITY OF TEXAS AT AUSTIN
- **Principal Investigator:** LAWRENCE Kevin CORMACK
- **Activity code:** UF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $3,344,560
- **Award type:** 1
- **Project period:** 2020-09-30 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9964469

## Citation

> US National Institutes of Health, RePORTER application 9964469, Neural circuit computations for visual motion during natural primate behaviors (1UF1NS116377-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9964469. Licensed CC0.

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