The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Cognitive computing
Abstract
Unite neuroscience, supercomputing, and nanotechnology to discover, demonstrate, and deliver the brain's core algorithms.
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Citations
Cites
A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566
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
A neural substrate of prediction and reward
Schultz, W., Dayan, P., & Montague, P. R. (1997) · Science · doi:10.1126/science.275.5306.1593 · Cited by 9,388
McClelland, J. L., Rumelhart, D. E., & AU (1987) · doi:10.7551/mitpress/5237.001.0001 · Cited by 3,315
Toward a modern theory of adaptive networks: Expectation and prediction.
Sutton, R. S., & Barto, A. G. (1981) · Psychological Review · doi:10.1037/0033-295x.88.2.135 · Cited by 1,450
Modeling Sleep and Wakefulness in the Thalamocortical System
Hill, S., & Tononi, G. (2005) · Journal of Neurophysiology · doi:10.1152/jn.00915.2004 · Cited by 417
Towards a mathematical theory of cortical micro-circuits
George, D., & Hawkins, J. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000532 · Cited by 404
Network architecture of the long-distance pathways in the macaque brain
Modha, D. S., & Singh, R. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1008054107 · Cited by 283
Self-citation
Cited by
Emerging Artificial Intelligence Methods in Structural Engineering
Salehi, H., & Burgueño, R. (2018) · Engineering Structures · doi:10.1016/j.engstruct.2018.05.084 · Cited by 1,068
Dellermann, D., Ebel, P., Söllner, M., & Leimeister, J. M. (2019) · Business & Information Systems Engineering · doi:10.1007/s12599-019-00595-2 · Cited by 826
IBM Watson: How Cognitive Computing Can Be Applied to Big Data Challenges in Life Sciences Research
Chen, Y., Elenee Argentinis, J., & Weber, G. (2016) · Clinical Therapeutics · doi:10.1016/j.clinthera.2015.12.001 · Cited by 466
Big data with cognitive computing: A review for the future
Gupta, S., Kar, A. K., Baabdullah, A., & Al-Khowaiter, W. A. A. (2018) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2018.06.005 · Cited by 285
Data science and AI in FinTech: an overview
Cao, L., Yang, Q., & Yu, P. S. (2021) · International Journal of Data Science and Analytics · doi:10.1007/s41060-021-00278-w · Cited by 238
Metadata
- Authors
- Modha, D. S., Ananthanarayanan, R., Esser, S. K., Ndirango, A., Sherbondy, A. J., & Singh, R.
- Venue
- Communications of the ACM
- Published
- 2011-08
- Volume
- 54
- Issue
- 8
- Pages
- 62-71
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W2338037132
- Cited by
- 326
- References
- 8
- FWCI
- 19.22
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
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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.
