ArticleScientific data2019
Characterization of deep neural network features by decodability from human brain activity.
Article in Scientific data, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 22 citations in OpenAlex.
- Inter-individual and inter-site neural code conversion without shared stimuli.Nature computational science · 2025Article
- Zero-Shot Neural Decoding with Semi-Supervised Multi-View Embedding.Sensors (Basel, Switzerland) · 2023Article
- Attention modulates neural representation to render reconstructions according to subjective appearance.Communications biology · 2022Article
- You won't believe what this guy is doing with the potato: The ObjAct stimulus-set depicting human actions on congruent and incongruent objects.Behavior research methods · 2021Article
- Which deep learning model can best explain object representations of within-category exemplars?Journal of vision · 2021Article
- Modeling NeurodegenerationFrontiers in neuroinformatics · 2021Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Achievements of near human-level performance in object recognition by deep neural networks (DNNs) have triggered a flood of comparative studies between the brain and DNNs. Using a DNN as a proxy for hierarchical visual representations, our recent study found that human brain activity patterns measured by functional magnetic resonance imaging (fMRI) can be decoded (translated) into DNN feature values given the same inputs. However, not all DNN features are equally decoded, indicating a gap between the DNN and human vision. Here, we present a dataset derived from DNN feature decoding analyses, which includes fMRI signals of five human subjects during image viewing, decoded feature values of DNNs (AlexNet and VGG19), and decoding accuracies of individual DNN features with their rankings. The decoding accuracies of individual features were highly correlated between subjects, suggesting the systematic differences between the brain and DNNs. We hope the present dataset will contribute to revealing the gap between the brain and DNNs and provide an opportunity to make use of the decoded features for further applications.
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Registered trials
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