Subject-specific fMRI embeddings learned unsupervised from the Natural Scenes Dataset can be aligned across individuals via orthogonal rotations, supporting a shared neural geometry in visual cortex.
Representational models: A common framework for understanding encoding, pattern-component, and representational-similarity analysis.PLOS Computational Biology, 13(4):1–33
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Task-aligned supervised geometric stability predicts linear steerability with high accuracy while unsupervised stability detects representational drift earlier and with lower false alarms than CKA or Procrustes.
citing papers explorer
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Platonic Representations in the Human Brain: Unsupervised Recovery of Universal Geometry
Subject-specific fMRI embeddings learned unsupervised from the Natural Scenes Dataset can be aligned across individuals via orthogonal rotations, supporting a shared neural geometry in visual cortex.
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The Geometric Canary: Predicting Steerability and Detecting Drift via Representational Stability
Task-aligned supervised geometric stability predicts linear steerability with high accuracy while unsupervised stability detects representational drift earlier and with lower false alarms than CKA or Procrustes.