Texture encoding in the brain and artificial neural networks
Official title: CRCNS: Texture encoding in nervous systems and deep neural networks
Funded at University of Washington · NIH-11538472 · Data updated 2026-11-12
Researchers are studying how texture is represented in both the primate brain and advanced AI to understand how texture helps us recognize scenes and objects.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Washington NIH-funded |
| Lab location | 1 site (Seattle, United States) |
| Project start | September 15, 2026 |
| Project end | May 31, 2031 |
| Funding year | 2026 |
| Award (this year) | $418,614 |
| NIH project number | 1R01EY040101-01 |
| Project ID | NIH-11538472 on NIH RePORTER |
Who could benefit from this research
✓Could help
Not applicable; the study involves non-human primates and AI models.
✕Less likely to help
There is no direct patient participation in this project.
What this research studies
This project measures how texture is encoded from early to mid-brain areas in macaque monkeys while they perform tasks, and compares the findings to representations found in deep neural networks. By linking neural data with AI features, the study aims to reveal universal principles of texture processing that could improve our understanding of vision. The team will use parallel experiments, analyzing how local image features become texture representations and how feedback from higher areas influences early processing. They will also test multiple high-performing AI models to identify common computation patterns and design stimulus sets optimized for in vivo experiments. The ultimate goal is to bridge neuroscience and AI to better understand texture in vision.
Why it matters
Potential benefit: Gains in understanding how texture is represented could inform treatments for visual disorders and guide the design of more robust AI vision systems.
How similar studies have gone: Previous work shows that texture processing involves early visual areas and can be reflected in AI models, with this project aiming to extend those insights.
Where this research is happening
-
University of Washington
Seattle, United States Active
Researchers
- Principal investigator: Pasupathy, Anitha — University of Washington
- Study coordinator: Pasupathy, Anitha
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.