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- Title
Convolutional neural networks explain tuning properties of anterior, but not middle, face-processing areas in macaque inferotemporal cortex.
- Authors
Raman, Rajani; Hosoya, Haruo
- 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. Rajani Raman and Haruo Hosoya examined layer-wise similarities between convolutional neural networks and the face-processing areas of the macaque visual cortex. They show that higher model layers exhibit similar tuning properties of anterior areas, such as strong invariance properties in size and view, while intermediate areas were more difficult to model.
- Subjects
CONVOLUTIONAL neural networks; HUMAN facial recognition software; VISUAL cortex physiology; FACE perception; NEURONS
- Publication
Communications Biology, 2020, Vol 3, Issue 1, p1
- ISSN
2399-3642
- Publication type
Article
- DOI
10.1038/s42003-020-0945-x