Cognitive Neural Architecture Search Reveals Hierarchical Entailment
Lukas Kuhn, Sari Saba-Sadiya, Gemma Roig

TL;DR
This paper uses neural architecture search to find convolutional models that align with brain hierarchies, revealing that hierarchical structure is fundamental in primate visual processing and offering a new approach for computational neuroscience.
Contribution
It demonstrates that neural architecture search can discover models with brain-like hierarchies, challenging previous assumptions and reducing reliance on manual network design.
Findings
Models with random weights surpass pretrained models in brain alignment.
Optimized architectures become competitive classifiers after supervised training.
Hierarchical structure is confirmed as fundamental in primate visual processing.
Abstract
Recent research has suggested that the brain is more shallow than previously thought, challenging the traditionally assumed hierarchical structure of the ventral visual pathway. Here, we demonstrate that optimizing convolutional network architectures for brain-alignment via evolutionary neural architecture search results in models with clear representational hierarchies. Despite having random weights, the identified models achieve brain-alignment scores surpassing even those of pretrained classification models - as measured by both regression and representational similarity analysis. Furthermore, through traditional supervised training, architectures optimized for alignment with late ventral regions become competitive classification models. These findings suggest that hierarchical structure is a fundamental mechanism of primate visual processing. Finally, this work demonstrates the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNeural Networks and Applications · AI-based Problem Solving and Planning · Cognitive Science and Mapping
