Meta-Learning an In-Context Transformer Model of Human Higher Visual Cortex

Understanding functional representations within higher visual cortex is a fundamental question in computational neuroscience. While artificial neural networks pretrained on large-scale datasets exhibit striking representational alignment with human neural responses, learning image-computable models of visual cortex relies on individual-level, large-scale fMRI datasets. The necessity for expensive, time-intensive, and often impractical data acquisition limits the generalizability of encoders to new subjects and stimuli. BraInCoRL uses incontext learning to predict voxelwise neural responses from few-shot examples without any additional finetuning for novel subjects and stimuli.