Contravariance Theory: Strong Alignment for Minimal Solutions to Hard Tasks
A series of results from the NeuroAI over the past fifteen years have raised core questions both about how to compare Deep Neural Network (DNN) models to the brain, and about how much convergent evolution to expect between artificial networks and real brain networks. Here, we show that for any two minimal DNN solutions to a sufficiently hard task: (i) "weak" alignment of network representations based on affine mappings guarantees "strong" alignment of privileged axes, and (ii) alignment "zippers" up the network hierarchy, causing the emergence of privileged axes from end-to-end task optimization. These results formalize the notion of contravariance from Cao and Yamins [2024], and illustra...
| Authors | Dan Yamins, Aran Nayebi |
|---|---|
| Primary category | cs.LG |
| Published | 9 July 2026 |
| Last updated at source | 31 July 2026 |
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