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ZENODO
Dataset . 2018
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2018
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2018
License: CC BY
Data sources: ZENODO
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Characterization of deep neural network features by decodability from human brain activity

Authors: Horikawa, Tomoyasu; Aoki, Shuntaro; Tsukamoto, Mitsuaki; Kamitani, Yukiyasu;

Characterization of deep neural network features by decodability from human brain activity

Abstract

We present a dataset derived through the DNN feature decoding analyses (Horikawa and Kamitani, 2017), including true and decoded feature values of DNNs (AlexNet and VGG19) and decoding accuracies of individual DNN features with their rankings. The decoding accuracies of individual DNN features were highly correlated across subjects, suggesting the systematic differences between the brain and DNNs. The unpreprocessed fMRI data is available from the OpenNeuro (https://openneuro.org/datasets/ds001246). We hope the present dataset will contribute to reveal the gap between the brain and DNNs and provide an opportunity to make use of the decoded features for further applications.

{"references": ["Horikawa, et al., (2018). biorxiv.org:424168, https://www.biorxiv.org/content/early/2018/09/25/424168"]}

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Keywords

fMri, deep neural network

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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