Meta-Learning an In-Context Transformer Model of Human Higher Visual
  Cortex
Authors: name: Muquan Yu, name: Mu Nan, name: Hossein Adeli, name: Jacob S. Prince, name: John A. Pyles, name: Leila Wehbe, name: Margaret M. Henderson, name: Michael J. Tarr, name: Andrew F. Luo

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 in-context learning to predict voxelwise neural responses from few-shot
examples without any additional finetuning for novel subjects and stimuli. We
leverage a transformer architecture that can flexibly condition on a variable
number of in-context image stimuli, learning an inductive bias over multiple
subjects. During training, we explicitly optimize the model for in-context
learning. By jointly conditioning on image features and voxel activations, our
model learns to directly generate better performing voxelwise models of higher
visual cortex. We demonstrate that BraInCoRL consistently outperforms existing
voxelwise encoder designs in a low-data regime when evaluated on entirely novel
images, while also exhibiting strong test-time scaling behavior. The model also
generalizes to an entirely new visual fMRI dataset, which uses different
subjects and fMRI data acquisition parameters. Further, BraInCoRL facilitates
better interpretability of neural signals in higher visual cortex by attending
to semantically relevant stimuli. Finally, we show that our framework enables
interpretable mappings from natural language queries to voxel selectivity.