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Turing computability with neural nets
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Research works printed but not linked: 5
- Sun, 1991, Turing equivalence of neural networks with second order connection weights, Proc. Int. Joint. Conf. Neural Networks
- Franklin, 1990, Neural computability, Vol. 1, 128
- Marcus, 1989, Dynamics of iterated-map neural networks, Phys. Rev. Ser. A, 40, 3355, 10.1103/PhysRevA.40.501 · DOI
- Maass, 1991, On the computational power of sigmoid versus Boolean threshold circuits, Proc. of the 32nd Annual Symp. on Foundations of Computer Science, 10.1109/SFCS.1991.185447 · DOI
- Blum, 1989, On a theory of computation and complexity over the real numbers: NP completeness, recursive functions, and universal machines, Bull. A.M.S., 21, 1, 10.1090/S0273-0979-1989-15750-9 · DOI · Paper page
Cited by
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“These types of networks have Turing capabilities [18] and, thus, are in principle suited for learning sequences.”
BackgroundLSTM recurrent networks learn simple context-free and context-sensitive languages
Gers, F. A., & Schmidhuber, E. (2001) · IEEE Transactions on Neural Networks · doi:10.1109/72.963769 · Cited by 722
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Computational capabilities of recurrent NARX neural networks
Siegelmann, H. T., Horne, B. G., & Giles, C. L. (1997) · IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) · doi:10.1109/3477.558801 · Cited by 455
Self-citation
Metadata
- Authors
- Siegelmann, H. T., & Sontag, E. D.
- Venue
- Applied Mathematics Letters
- Publisher
- Elsevier BV
- Published
- 1991
- Volume
- 4
- Issue
- 6
- Pages
- 77-80
- ISSN
- 0893-9659 (ISSN-L)
- OpenAlex
- W2094149843
- Cited by
- 377
- References
- 0
- FWCI
- 4.18
- Type
- journal article
- Language
- en
- Open access
- bronze
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 8233 d)
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- CoChat. (2026). Citation check: Turing computability with neural nets. https://app.cochat.ai/papers/10.1016/0893-9659(91)90080-f
- The paper
- Siegelmann, H. T., & Sontag, E. D. (1991). Turing computability with neural nets. Applied Mathematics Letters. https://doi.org/10.1016/0893-9659(91)90080-f
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.
