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Analysis of convolutional neural networks reveals the computational properties essential for subcortical processing of facial expression

Perception of facial expression is crucial for primate social interactions. This visual information is processed through the ventral cortical pathway and the subcortical pathway. However, the subcortical pathway exhibits inaccurate processing, and the responsible architectural and physiological prop...

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Autores principales: Lim, Chanseok, Inagaki, Mikio, Shinozaki, Takashi, Fujita, Ichiro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10322994/
https://www.ncbi.nlm.nih.gov/pubmed/37407668
http://dx.doi.org/10.1038/s41598-023-37995-0
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author Lim, Chanseok
Inagaki, Mikio
Shinozaki, Takashi
Fujita, Ichiro
author_facet Lim, Chanseok
Inagaki, Mikio
Shinozaki, Takashi
Fujita, Ichiro
author_sort Lim, Chanseok
collection PubMed
description Perception of facial expression is crucial for primate social interactions. This visual information is processed through the ventral cortical pathway and the subcortical pathway. However, the subcortical pathway exhibits inaccurate processing, and the responsible architectural and physiological properties remain unclear. To investigate this, we constructed and examined convolutional neural networks with three key properties of the subcortical pathway: a shallow layer architecture, concentric receptive fields at the initial processing stage, and a greater degree of spatial pooling. These neural networks achieved modest accuracy in classifying facial expressions. By replacing these properties, individually or in combination, with corresponding cortical features, performance gradually improved. Similar to amygdala neurons, some units in the final processing layer exhibited sensitivity to retina-based spatial frequencies (SFs), while others were sensitive to object-based SFs. Replacement of any of these properties affected the coordinates of the SF encoding. Therefore, all three properties limit the accuracy of facial expression information and are essential for determining the SF representation coordinate. These findings characterize the role of the subcortical computational processes in facial expression recognition.
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spelling pubmed-103229942023-07-07 Analysis of convolutional neural networks reveals the computational properties essential for subcortical processing of facial expression Lim, Chanseok Inagaki, Mikio Shinozaki, Takashi Fujita, Ichiro Sci Rep Article Perception of facial expression is crucial for primate social interactions. This visual information is processed through the ventral cortical pathway and the subcortical pathway. However, the subcortical pathway exhibits inaccurate processing, and the responsible architectural and physiological properties remain unclear. To investigate this, we constructed and examined convolutional neural networks with three key properties of the subcortical pathway: a shallow layer architecture, concentric receptive fields at the initial processing stage, and a greater degree of spatial pooling. These neural networks achieved modest accuracy in classifying facial expressions. By replacing these properties, individually or in combination, with corresponding cortical features, performance gradually improved. Similar to amygdala neurons, some units in the final processing layer exhibited sensitivity to retina-based spatial frequencies (SFs), while others were sensitive to object-based SFs. Replacement of any of these properties affected the coordinates of the SF encoding. Therefore, all three properties limit the accuracy of facial expression information and are essential for determining the SF representation coordinate. These findings characterize the role of the subcortical computational processes in facial expression recognition. Nature Publishing Group UK 2023-07-05 /pmc/articles/PMC10322994/ /pubmed/37407668 http://dx.doi.org/10.1038/s41598-023-37995-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Lim, Chanseok
Inagaki, Mikio
Shinozaki, Takashi
Fujita, Ichiro
Analysis of convolutional neural networks reveals the computational properties essential for subcortical processing of facial expression
title Analysis of convolutional neural networks reveals the computational properties essential for subcortical processing of facial expression
title_full Analysis of convolutional neural networks reveals the computational properties essential for subcortical processing of facial expression
title_fullStr Analysis of convolutional neural networks reveals the computational properties essential for subcortical processing of facial expression
title_full_unstemmed Analysis of convolutional neural networks reveals the computational properties essential for subcortical processing of facial expression
title_short Analysis of convolutional neural networks reveals the computational properties essential for subcortical processing of facial expression
title_sort analysis of convolutional neural networks reveals the computational properties essential for subcortical processing of facial expression
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10322994/
https://www.ncbi.nlm.nih.gov/pubmed/37407668
http://dx.doi.org/10.1038/s41598-023-37995-0
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