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John A. Pyles

2 accepted papers

2026

Meta-Learning In-Context Enables Training-Free Cross Subject Brain Decoding

CVPR 2026

Visual decoding from brain signals is a key challenge at the intersection of computer vision and neuroscience, requiring methods that bridge neural representations and computational models of vision. A field-wide goal is to achieve generalizable, cross-subject models. A major obstacle towards this g

Cited by 0SourcecodeScholar
2025

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

NeurIPS 2025poster

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…

Cited by 0SourceScholar