The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
Single-neuron encoding of rapidly learned visual information reshapes human perception
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Citations
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · cited 2×
“Current instantiations of such networks are typically trained via backpropagation through millions of examples when learning from scratch but also when fine tuning an existing pre-trained network 7–10 .”
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
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Memory and the hippocampus: A synthesis from findings with rats, monkeys, and humans.
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Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636
Hierarchical models of object recognition in cortex
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Priming and Human Memory Systems
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Invariant visual representation by single neurons in the human brain
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Self-citationTop-down facilitation of visual recognition
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Metadata
- Authors
- Armendariz, M., Blumberg, J., Singer, J., Aiple, F., Kim, J., Domínguez-Iturza, N., Brandt, A., Reinacher, P., Schulze-Bonhage, A., & Kreiman, G.
- Venue
- Nature Neuroscience
- Published
- 2026-11-04
- ISSN
- 1097-6256
- 1546-1726
- Cited by
- 0
- References
- 61
- Language
- en
- Licence
- creativecommons.org/licenses/by-nc-nd/4.0 (tdm)
- creativecommons.org/licenses/by-nc-nd/4.0 (vor)
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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- The paper
- Armendariz, M., Blumberg, J., Singer, J., Aiple, F., Kim, J., Domínguez-Iturza, N., Brandt, A., Reinacher, P., Schulze-Bonhage, A., & Kreiman, G. (2026). Single-neuron encoding of rapidly learned visual information reshapes human perception. Nature Neuroscience. https://doi.org/10.1038/s41593-026-02496-6
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
