Summary: Researchers have developed a geometric deep learning approach to uncover shared brain activity patterns across individuals. The method, called MARBLE, learns dynamic motifs from neural recordings and identifies common strategies used by different brains to solve the same task. Tested on macaques and rats, MARBLE accurately decoded neural activity linked to movement and navigation, outperforming other machine learning methods. The system works by mapping neural data into high-dimensional geometric spaces, enabling pattern recognition across individuals and conditions. Key Facts: Geometric Deep Learning: MARBLE identifies shared brain activity patterns by mapping neural signals onto high-dimensional shapes. Cross-Subject Comparisons: The method successfully detected common neural motifs in different animals performing the same task. Applications in Brain-Machine Interfaces: By decoding brain activity into recognizable patterns, MARBLE could improve assistive robotics and […]
Original web page at neurosciencenews.com
