The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Associative memory realized by a reconfigurable memristive Hopfield neural network
Open accessAbstract
Although synaptic behaviours of memristors have been widely demonstrated, implementation of an even simple artificial neural network is still a great challenge. In this work, we demonstrate the associative memory on the basis of a memristive Hopfield network. Different patterns can be stored into the memristive Hopfield network by tuning the resistance of the memristors, and the pre-stored patterns can be successfully retrieved directly or through some associative intermediate states, being analogous to the associative memory behaviour. Both single-associative memory and multi-associative memories can be realized with the memristive Hopfield network. Memristors are passive electrical components that can act like simple memories. Here, the authors use an array of hafnium oxide memristors to create a type of artificial neural network, known as a Hopfield network, that is capable of retrieving data from partial information
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Cites
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Memristor—The Missing Circuit Element
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“In 1971, Chua predicted the fourth basic circuit element, namely, memristor 12,13 , which was later demonstrated in the laboratory by Williams et al. in 2008 (ref.”
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“Subsequently, many studies demonstrated that a memristor can be used as an electronic synapse with its conductance representing the synaptic weight 15–25 .”
BackgroundMemristive devices for computing
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Memristive devices and systems
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Experimental demonstration of associative memory with memristive neural networks
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Cited by
Memory devices and applications for in-memory computing
Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R., & Eleftheriou, E. (2020) · Nature Nanotechnology · doi:10.1038/s41565-020-0655-z · Cited by 2,289
Resistive switching materials for information processing
Wang, Z., Wu, H., Burr, G. W., Hwang, C. S., Wang, K. L., Xia, Q., & Yang, J. J. (2020) · Nature Reviews Materials · doi:10.1038/s41578-019-0159-3 · Cited by 1,214
Fully memristive neural networks for pattern classification with unsupervised learning
Wang, Z., Joshi, S., Savel’ev, S., Song, W., Midya, R., Li, Y., Rao, M., Yan, P., Asapu, S., Zhuo, Y., et al. (2018) · Nature Electronics · doi:10.1038/s41928-018-0023-2 · Cited by 1,106
Brain-inspired computing with memristors: Challenges in devices, circuits, and systems
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Bayat, F. M., Prezioso, M., Chakrabarti, B., Nili, H., Kataeva, I., & Strukov, D. (2018) · Nature Communications · doi:10.1038/s41467-018-04482-4 · Cited by 377
Metadata
- Authors
- Hu, S. G., Liu, Y., Liu, Z., Chen, T. P., Wang, J. J., Yu, Q., Deng, L. J., Yin, Y., & Hosaka, S.
- Venue
- Nature Communications
- Published
- 2015-06-25
- Volume
- 6
- Issue
- 1
- Pages
- 7522-7522
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2260028708
- Cited by
- 258
- References
- 15
- FWCI
- 9.94
- Type
- journal article
- Language
- en
- Open access
- gold
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