Dataset Information

Convolutional neural networks explain tuning properties of anterior, but not middle, face-processing areas in macaque inferotemporal cortex.

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.

SUBMITTER: Raman R

PROVIDER: S-EPMC7210114 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Convolutional neural networks explain tuning properties of anterior, but not middle, face-processing areas in macaque inferotemporal cortex.

Raman Rajani R Hosoya Haruo H

Communications biology 20200508 1

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 ...[more]

PMID: 32385392

Similar Datasets

Project description:When a complexly structured natural image is presented twice in succession, first as adapter and then as test, neurons in area TE of macaque inferotemporal cortex exhibit repetition suppression, responding less strongly to the second presentation than to the first. This phenomenon, which has been studied primarily in TE, might plausibly be argued to arise in TE because TE neurons respond selectively to complex images and thus carry information adequate for determining whether an image is or is not a repeat. However, the idea has never been put to a direct test. To resolve this issue, we monitored neuronal responses to sequences of complex natural images under identical conditions in areas V2 and TE. We found that repetition suppression occurs in both areas. Moreover, in each area, suppression takes the form of a dynamic alteration whereby the initial peak of excitation is followed by a trough and then a rebound of firing rate. To assess whether repetition suppression in either area is transmitted from the other area, we analyzed the timing of the phenomenon and its degree of spatial generalization. Suppression occurs at shorter latency in V2 than in TE. Therefore it is not simply fed back from TE. Suppression occurs in TE but not in V2 under conditions in which the test and adapter are presented in different visual field quadrants. Therefore it is not simply fed forward from V2. We conclude that repetition suppression occurs independently in V2 and TE.NEW & NOTEWORTHY When a complexly structured natural image is presented twice in rapid succession, neurons in inferotemporal area TE exhibit repetition suppression, responding less strongly to the second than to the first presentation. We have explored whether this phenomenon is confined to high-order areas where neurons respond selectively to such images and thus carry information relevant to recognizing a repeat. We have found surprisingly that repetition suppression occurs even in low-order visual area V2.

Dataset Information

Convolutional neural networks explain tuning properties of anterior, but not middle, face-processing areas in macaque inferotemporal cortex.

Publications

Convolutional neural networks explain tuning properties of anterior, but not middle, face-processing areas in macaque inferotemporal cortex.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets