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Face identity coding in the deep neural network and primate brain
A central challenge in face perception research is to understand how neurons encode face identities. This challenge has not been met largely due to the lack of simultaneous access to the entire face processing neural network and the lack of a comprehensive multifaceted model capable of characterizin...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9209415/ https://www.ncbi.nlm.nih.gov/pubmed/35725902 http://dx.doi.org/10.1038/s42003-022-03557-9 |
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author | Wang, Jinge Cao, Runnan Brandmeir, Nicholas J. Li, Xin Wang, Shuo |
author_facet | Wang, Jinge Cao, Runnan Brandmeir, Nicholas J. Li, Xin Wang, Shuo |
author_sort | Wang, Jinge |
collection | PubMed |
description | A central challenge in face perception research is to understand how neurons encode face identities. This challenge has not been met largely due to the lack of simultaneous access to the entire face processing neural network and the lack of a comprehensive multifaceted model capable of characterizing a large number of facial features. Here, we addressed this challenge by conducting in silico experiments using a pre-trained face recognition deep neural network (DNN) with a diverse array of stimuli. We identified a subset of DNN units selective to face identities, and these identity-selective units demonstrated generalized discriminability to novel faces. Visualization and manipulation of the network revealed the importance of identity-selective units in face recognition. Importantly, using our monkey and human single-neuron recordings, we directly compared the response of artificial units with real primate neurons to the same stimuli and found that artificial units shared a similar representation of facial features as primate neurons. We also observed a region-based feature coding mechanism in DNN units as in human neurons. Together, by directly linking between artificial and primate neural systems, our results shed light on how the primate brain performs face recognition tasks. |
format | Online Article Text |
id | pubmed-9209415 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-92094152022-06-22 Face identity coding in the deep neural network and primate brain Wang, Jinge Cao, Runnan Brandmeir, Nicholas J. Li, Xin Wang, Shuo Commun Biol Article A central challenge in face perception research is to understand how neurons encode face identities. This challenge has not been met largely due to the lack of simultaneous access to the entire face processing neural network and the lack of a comprehensive multifaceted model capable of characterizing a large number of facial features. Here, we addressed this challenge by conducting in silico experiments using a pre-trained face recognition deep neural network (DNN) with a diverse array of stimuli. We identified a subset of DNN units selective to face identities, and these identity-selective units demonstrated generalized discriminability to novel faces. Visualization and manipulation of the network revealed the importance of identity-selective units in face recognition. Importantly, using our monkey and human single-neuron recordings, we directly compared the response of artificial units with real primate neurons to the same stimuli and found that artificial units shared a similar representation of facial features as primate neurons. We also observed a region-based feature coding mechanism in DNN units as in human neurons. Together, by directly linking between artificial and primate neural systems, our results shed light on how the primate brain performs face recognition tasks. Nature Publishing Group UK 2022-06-20 /pmc/articles/PMC9209415/ /pubmed/35725902 http://dx.doi.org/10.1038/s42003-022-03557-9 Text en © The Author(s) 2022 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Wang, Jinge Cao, Runnan Brandmeir, Nicholas J. Li, Xin Wang, Shuo Face identity coding in the deep neural network and primate brain |
title | Face identity coding in the deep neural network and primate brain |
title_full | Face identity coding in the deep neural network and primate brain |
title_fullStr | Face identity coding in the deep neural network and primate brain |
title_full_unstemmed | Face identity coding in the deep neural network and primate brain |
title_short | Face identity coding in the deep neural network and primate brain |
title_sort | face identity coding in the deep neural network and primate brain |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9209415/ https://www.ncbi.nlm.nih.gov/pubmed/35725902 http://dx.doi.org/10.1038/s42003-022-03557-9 |
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