Brain-Inspired AI Model ‘HoloBrain’ Outperforms Standard Neural Networks in Cognitive Task Prediction
Researchers have developed a brain-inspired AI system called HoloBrain that significantly outperforms conventional graph neural networks in predicting cognitive tasks from brain imaging data. The approach models neural synchronization using principles from physics and holography, achieving up to 15% better accuracy on standard benchmarks. This breakthrough suggests new pathways for developing biologically grounded artificial intelligence systems.
Brain Waves Inspire Next-Generation AI Architecture
In what could signal a major shift in how we approach artificial intelligence, researchers have reportedly developed a new computational framework that draws direct inspiration from the brain’s natural synchronization patterns. Dubbed “HoloBrain,” the system models neural oscillations using principles borrowed from physics and holography, according to recently published findings.