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Activations of deep convolutional neural networks are aligned with gamma band activity of human visual cortex

Recent advances in the field of artificial intelligence have revealed principles about neural processing, in particular about vision. Previous work demonstrated a direct correspondence between the hierarchy of the human visual areas and layers of deep convolutional neural networks (DCNN) trained on...

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Autores principales: Kuzovkin, Ilya, Vicente, Raul, Petton, Mathilde, Lachaux, Jean-Philippe, Baciu, Monica, Kahane, Philippe, Rheims, Sylvain, Vidal, Juan R., Aru, Jaan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6123818/
https://www.ncbi.nlm.nih.gov/pubmed/30271987
http://dx.doi.org/10.1038/s42003-018-0110-y
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author Kuzovkin, Ilya
Vicente, Raul
Petton, Mathilde
Lachaux, Jean-Philippe
Baciu, Monica
Kahane, Philippe
Rheims, Sylvain
Vidal, Juan R.
Aru, Jaan
author_facet Kuzovkin, Ilya
Vicente, Raul
Petton, Mathilde
Lachaux, Jean-Philippe
Baciu, Monica
Kahane, Philippe
Rheims, Sylvain
Vidal, Juan R.
Aru, Jaan
author_sort Kuzovkin, Ilya
collection PubMed
description Recent advances in the field of artificial intelligence have revealed principles about neural processing, in particular about vision. Previous work demonstrated a direct correspondence between the hierarchy of the human visual areas and layers of deep convolutional neural networks (DCNN) trained on visual object recognition. We use DCNN to investigate which frequency bands correlate with feature transformations of increasing complexity along the ventral visual pathway. By capitalizing on intracranial depth recordings from 100 patients we assess the alignment between the DCNN and signals at different frequency bands. We find that gamma activity (30–70 Hz) matches the increasing complexity of visual feature representations in DCNN. These findings show that the activity of the DCNN captures the essential characteristics of biological object recognition not only in space and time, but also in the frequency domain. These results demonstrate the potential that artificial intelligence algorithms have in advancing our understanding of the brain.
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spelling pubmed-61238182018-09-28 Activations of deep convolutional neural networks are aligned with gamma band activity of human visual cortex Kuzovkin, Ilya Vicente, Raul Petton, Mathilde Lachaux, Jean-Philippe Baciu, Monica Kahane, Philippe Rheims, Sylvain Vidal, Juan R. Aru, Jaan Commun Biol Article Recent advances in the field of artificial intelligence have revealed principles about neural processing, in particular about vision. Previous work demonstrated a direct correspondence between the hierarchy of the human visual areas and layers of deep convolutional neural networks (DCNN) trained on visual object recognition. We use DCNN to investigate which frequency bands correlate with feature transformations of increasing complexity along the ventral visual pathway. By capitalizing on intracranial depth recordings from 100 patients we assess the alignment between the DCNN and signals at different frequency bands. We find that gamma activity (30–70 Hz) matches the increasing complexity of visual feature representations in DCNN. These findings show that the activity of the DCNN captures the essential characteristics of biological object recognition not only in space and time, but also in the frequency domain. These results demonstrate the potential that artificial intelligence algorithms have in advancing our understanding of the brain. Nature Publishing Group UK 2018-08-08 /pmc/articles/PMC6123818/ /pubmed/30271987 http://dx.doi.org/10.1038/s42003-018-0110-y Text en © The Author(s) 2018 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/.
spellingShingle Article
Kuzovkin, Ilya
Vicente, Raul
Petton, Mathilde
Lachaux, Jean-Philippe
Baciu, Monica
Kahane, Philippe
Rheims, Sylvain
Vidal, Juan R.
Aru, Jaan
Activations of deep convolutional neural networks are aligned with gamma band activity of human visual cortex
title Activations of deep convolutional neural networks are aligned with gamma band activity of human visual cortex
title_full Activations of deep convolutional neural networks are aligned with gamma band activity of human visual cortex
title_fullStr Activations of deep convolutional neural networks are aligned with gamma band activity of human visual cortex
title_full_unstemmed Activations of deep convolutional neural networks are aligned with gamma band activity of human visual cortex
title_short Activations of deep convolutional neural networks are aligned with gamma band activity of human visual cortex
title_sort activations of deep convolutional neural networks are aligned with gamma band activity of human visual cortex
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6123818/
https://www.ncbi.nlm.nih.gov/pubmed/30271987
http://dx.doi.org/10.1038/s42003-018-0110-y
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