ArticleCommunications biology2020
Convolutional neural networks explain tuning properties of anterior, but not middle, face-processing areas in macaque inferotemporal cortex.
Article in Communications biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Hybrid deep learning framework MedFusionNet assists multilabel biomedical risk stratification from imaging and tabular data.Communications medicine · 2026Article
- Emergence of brain-like mirror-symmetric viewpoint tuning in convolutional neural networks.eLife · 2024Article
- Bodies in motion: Unraveling the distinct roles of motion and shape in dynamic body responses in the temporal cortex.Cell reports · 2023Article
- Modeling naturalistic face processing in humans with deep convolutional neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Article
- Equivalent processing of facial expression and identity by macaque visual system and task-optimized neural network.NeuroImage · 2023Article
- Local features drive identity responses in macaque anterior face patches.Nature communications · 2022Article
- Explaining face representation in the primate brain using different computational models.Current biology : CB · 2021Article
- Overall Survival Prediction for Gliomas Using a Novel Compound Approach.Frontiers in oncology · 2021Article
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2 authors.
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Abstract
Recent computational studies have emphasized layer-wise quantitative similarity between convolutional neural networks (CNNs) and the primate visual ventral stream. However, whether such similarity holds for the face-selective areas, a subsystem of the higher visual cortex, is not clear. Here, we extensively investigate whether CNNs exhibit tuning properties as previously observed in different macaque face areas. While simulating four past experiments on a variety of CNN models, we sought for the model layer that quantitatively matches the multiple tuning properties of each face area. Our results show that higher model layers explain reasonably well the properties of anterior areas, while no layer simultaneously explains the properties of middle areas, consistently across the model variation. Thus, some similarity may exist between CNNs and the primate face-processing system in the near-goal representation, but much less clearly in the intermediate stages, thus requiring alternative modeling such as non-layer-wise correspondence or different computational principles.
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