3 papers
cs.LG2026
Meta-learning In-Context Enables Training-Free Cross Subject Brain Decoding
Mu Nan, Muquan Yu, Weijian Mai +12
Visual decoding from brain signals is a key challenge at the intersection of computer vision and neuroscience, requiring methods that bridge neural representations and computationa…
cs.LG2025
Meta-Learning an In-Context Transformer Model of Human Higher Visual Cortex
Muquan Yu, Mu Nan, Hossein Adeli +6
Understanding functional representations within higher visual cortex is a fundamental question in computational neuroscience. While artificial neural networks pretrained on large-s…
cs.CV2025
Brain Mapping with Dense Features: Grounding Cortical Semantic Selectivity in Natural Images With Vision Transformers
Andrew F. Luo, Jacob Yeung, Rushikesh Zawar +4
We introduce BrainSAIL, a method for linking neural selectivity with spatially distributed semantic visual concepts in natural scenes. BrainSAIL leverages recent advances in large-…