The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Using neuroscience to develop artificial intelligence
Abstract
Combining deep learning with brain-like innate structures may guide network models toward human-like learning
Overview
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Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“DNNs use a limited set of highly simplified homogeneous artificial 'neurons', whereas biological neuronal architecture displays a heterogeneity of morphologies and functional connections [17, 18].”
From Anthropomorphism in AI: hype and fallacy (2024) · cites this paper as [18]
In the citing paper
“Shimon Ullman [18] argues that almost everything we know about neurons (e.g., structure, types, interconnectivity) has not been incorporated in these networks [17, 18].”
From Anthropomorphism in AI: hype and fallacy (2024) · cites this paper as [18]
In the citing paper
“NLP, a branch of AI that helps computers understand and process human language, can automate workflows for administrative documentation, including the creation of transcripts and patient-case summaries [39].”
From Artificial Intelligence Revolutionizing the Field of Medical Education (2023) · cites this paper as [39]
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268
The perceptron: A probabilistic model for information storage and organization in the brain.
Rosenblatt, F. (1958) · Psychological Review · doi:10.1037/h0042519 · Cited by 12,280
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TURING, A. M. (1950) · Mind · doi:10.1093/mind/lix.236.433 · Cited by 10,290
Mastering the game of Go without human knowledge
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al. (2017) · Nature · doi:10.1038/nature24270 · Cited by 9,084
Building machines that learn and think like people
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017) · Behavioral and Brain Sciences · doi:10.1017/s0140525x16001837 · Cited by 2,550
Carey, S. E. (2000) · Oxford University Press eBooks · doi:10.1037/e513702014-002 · Cited by 2,324
Neuroscience-Inspired Artificial Intelligence
Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017) · Neuron · doi:10.1016/j.neuron.2017.06.011 · Cited by 1,557
Spelke, E. S., & Kinzler, K. D. (2007) · Developmental Science · doi:10.1111/j.1467-7687.2007.00569.x · Cited by 1,089
Research works printed but not linked: 1
- E. Real ., Proc. 34th Int. Conf. Machine Learning, PMLR 70, 2902 (2017).
Cited by
Sorted by citations
Towards artificial general intelligence with hybrid Tianjic chip architecture
Pei, J., Deng, L., Song, S., Zhao, M., Zhang, Y., Wu, S., Wang, G., Zou, Z., Wu, Z., He, W., et al. (2019) · Nature · doi:10.1038/s41586-019-1424-8 · Cited by 1,082
Big data analytics for intelligent manufacturing systems: A review
Wang, J., Xu, C., Zhang, J., & Zhong, R. Y. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2021.03.005 · Cited by 669
Artificial Intelligence Applied to Battery Research: Hype or Reality?
Lombardo, T., Duquesnoy, M., El-Bouysidy, H., Årén, F., Gallo‐Bueno, A., Jørgensen, P. B., Bhowmik, A., Demortière, A., Ayerbe, E., Alcaide, F., et al. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00108 · Cited by 536
Salles, A., Evers, K., & Farisco, M. (2020) · AJOB Neuroscience · doi:10.1080/21507740.2020.1740350 · Cited by 335
Optoelectronic Perovskite Synapses for Neuromorphic Computing
Ma, F., Zhu, Y., Xu, Z., Liu, Y., Zheng, X., Ju, S., Li, Q., Ni, Z., Hu, H., Chai, Y., et al. (2020) · Advanced Functional Materials · doi:10.1002/adfm.201908901 · Cited by 240
Metadata
- Authors
- Ullman, S.
- Affiliations
- Weizmann Institute of Science (IL)
- Corresponding author
- Ullman, Shimon
- ORCID
- Ullman, Shimon
- Venue
- Science
- Published
- 2019-02-15
- Volume
- 363
- Issue
- 6428
- Pages
- 692-693
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2914361849
- Cited by
- 231
- References
- 15
- Citations by year
- 2026: 19 · 2025: 22 · 2024: 44 · 2023: 38 · 2022: 30 · 2021: 31 · 2020: 26 · 2019: 21
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Cognitive Computing and Networks
- Physical Sciences › Computer Science › Artificial Intelligence › Psychiatry, Mental Health, Neuroscience
- Keywords
- deep learning, human-like learning, neuromorphic computing
- MeSH terms
- Deep Learning, Artificial Intelligence, Brain, Humans, Learning, Neurosciences, Neural Networks, Computer
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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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