RI: Small: Visual Cortical Recurrent Circuits for Manifold Learning and Memory Attention — NSF Award to Carnegie Mellon University
The human brain consumes millions of times less energy than artificial intelligence (AI) systems, yet it remains more flexible, versatile, and effective at solving complex problems. A key reason lies in the brain’s ability to learn abstract concepts and their relationships, and to construct internal models of the world
| Award title | RI: Small: Visual Cortical Recurrent Circuits for Manifold Learning and Memory Attention |
|---|---|
| Award ID | 2420348 |
| Awardee | Carnegie Mellon University |
| City | PITTSBURGH |
| State | PA |
| Amount obligated | $600,000 |
| Principal investigator | Tai Sing Lee |
| Program | Robust Intelligence |
| Start date | 06/01/2025 |
| Abstract | The human brain consumes millions of times less energy than artificial intelligence (AI) systems, yet it remains more flexible, versatile, and effective at solving complex problems. A key reason lies in the brain’s ability to learn abstract concepts and their relationships, and to construct internal models of the world--rather than simply memorizing and retrieving patterns from massive datasets. This project aims to investigate the computational mechanisms underlying a recently discovered neural |
| Source | NSF Awards |
$799/mo
Try NSFGrants →