The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Cognitron: A self-organizing multilayered neural network
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Citations
Cites
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
Hubel, D. H., & Wiesel, T. N. (1962) · The Journal of Physiology · doi:10.1113/jphysiol.1962.sp006837 · Cited by 13,680
Marr, D. (1969) · The Journal of Physiology · doi:10.1113/jphysiol.1969.sp008820 · Cited by 3,253
Development of the brain depends on the visual environment
BLAKEMORE, C., & COOPER, G. F. (1970) · Nature · doi:10.1038/228477a0 · Cited by 1,005
Cited by
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00986 · Cited by 35,534
“Its complexity is still quadratic to image size, while ours is linear and also operates locally which has proven beneficial in modeling the high correlation in visual signals [32, 22, 37].”
BackgroundFukushima, K. (1980) · Biological Cybernetics · doi:10.1007/bf00344251 · Cited by 5,759
Self-citationDueling Network Architectures for Deep Reinforcement Learning
Wang, Z., Schaul, T., Hessel, M., van Hasselt, H., Lanctot, M., & de Freitas, N. (2015) · arXiv · doi:10.48550/arxiv.1511.06581 · Cited by 3,300
Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Visual Pattern Recognition
Fukushima, K., & Miyake, S. (1982) · Lecture Notes in Biomathematics · doi:10.1007/978-3-642-46466-9_18 · Cited by 991
Self-citationObject Recognition with Gradient-Based Learning
LeCun, Y., Haffner, P., Bottou, L., & Bengio, Y. (1999) · Lecture Notes in Computer Science · doi:10.1007/3-540-46805-6_19 · Cited by 947
Jo, T., Nho, K., & Saykin, A. J. (2019) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2019.00220 · Cited by 724
A Survey of Deep Learning Methods for Cyber Security
Berman, D. S., Buczak, A. L., Chavis, J. S., & Corbett, C. L. (2019) · Information · doi:10.3390/info10040122 · Cited by 569
Yu, L., Guo, Y., Wang, Y., Yu, J., & Chen, P. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2628401 · Cited by 96
Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?
Majumdar, A., Yadav, K., Arnaud, S., Ma, Y. J., Chen, C., Silwal, S., Jain, A., Berges, V.-P., Abbeel, P., Malik, J., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.18240 · Cited by 49
“In this work, we ask the same question that Fukushima [1, 2] asked nearly 50 years ago – how do we design an artificial visual cortex , the module in a computational system that (together with a policy) enables an agent to convert camera input into actions?”
BackgroundWhere are we in the search for an artificial visual cortex for embodied intelligence
Majumdar, A., Yadav, K., Arnaud, S., Ma, J., Chen, C., Silwal, S., Jain, A., Berges, V.-P., Wu, T., Vakil, J., et al. (2023) · Advances in Neural Information Processing Systems 36 · doi:10.52202/075280-0031 · Cited by 49
Lai, D. K.-H., Cheng, E. S.-W., So, B. P.-H., Mao, Y.-J., Cheung, S. M.-Y., Cheung, D. S. K., Wong, D. W.-C., & Cheung, J. C.-W. (2023) · Mathematics · doi:10.3390/math11143081 · Cited by 18
Design and evaluation of a global workspace agent embodied in a realistic multimodal environment
Dossa, R. F. J., Arulkumaran, K., Juliani, A., Sasai, S., & Kanai, R. (2024) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2024.1352685 · Cited by 5
“In practice, each probe is defined as a 2-layer MLP with ReLU activations (Fukushima, 1975).”
Method
Metadata
- Authors
- Fukushima, K.
- Venue
- Biological Cybernetics
- Published
- 1975
- Volume
- 20
- Issue
- 3-4
- Pages
- 121-136
- ISSN
- 0340-1200 (ISSN-L)
- 1432-0770
- OpenAlex
- W2010315761
- Cited by
- 757
- References
- 3
- FWCI
- 2.11
- Type
- journal article
- Language
- en
- Open access
- closed
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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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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.
