The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.
Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses
Open accessAbstract
In an increasingly data-rich world the need for developing computing systems that cannot only process, but ideally also interpret big data is becoming continuously more pressing. Brain-inspired concepts have shown great promise towards addressing this need. Here we demonstrate unsupervised learning in a probabilistic neural network that utilizes metal-oxide memristive devices as multi-state synapses. Our approach can be exploited for processing unlabelled data and can adapt to time-varying clusters that underlie incoming data by supporting the capability of reversible unsupervised learning. The potential of this work is showcased through the demonstration of successful learning in the presence of corrupted input data and probabilistic neurons, thus paving the way towards robust big-data processors.
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Cites
Strukov, D. B., Snider, G. S., Stewart, D. R., & Williams, R. S. (2008) · Nature · doi:10.1038/nature06932 · Cited by 11,828
Memristor—The Missing Circuit Element
Chua, L. (1971) · IEEE Transactions on Circuit Theory · doi:10.1109/tct.1971.1083337 · Cited by 10,268 · cited 2×
“3 shows the fraction on the right-hand side of equation (8) based on the fitted functions fLTP(g) and fLTD(g) from Fig.”
Redox‐Based Resistive Switching Memories – Nanoionic Mechanisms, Prospects, and Challenges
Waser, R., Dittmann, R., Staikov, G., & Szot, K. (2009) · Advanced Materials · doi:10.1002/adma.200900375 · Cited by 5,080
Nanoionics-based resistive switching memories
Waser, R., & Aono, M. (2007) · Nature Materials · doi:10.1038/nmat2023 · Cited by 4,923
Bi, G.-Q., & Poo, M.-M. (1998) · The Journal of Neuroscience · doi:10.1523/jneurosci.18-24-10464.1998 · Cited by 4,744
Nanoscale Memristor Device as Synapse in Neuromorphic Systems
Jo, S. H., Chang, T., Ebong, I., Bhadviya, B. B., Mazumder, P., & Lu, W. (2010) · Nano Letters · doi:10.1021/nl904092h · Cited by 4,174 · cited 1×
“Such dependence of conductance changes on the actual memristive state has commonly been observed in memristors, including both metal-oxide 25 and phase-change 36 implementations.”
Training and operation of an integrated neuromorphic network based on metal-oxide memristors
Prezioso, M., Merrikh-Bayat, F., Hoskins, B. D., Adam, G. C., Likharev, K. K., & Strukov, D. B. (2015) · Nature · doi:10.1038/nature14441 · Cited by 3,001
Memristive devices and systems
Chua, L. O., & Kang, S. M. (1976) · Proceedings of the IEEE · doi:10.1109/proc.1976.10092 · Cited by 2,604 · cited 1×
“On the basis of the comparison of equations (6) and equation (9), we map memristor conductances g to abstract weights w via a linear function”
Long-term in vivo imaging of experience-dependent synaptic plasticity in adult cortex
Trachtenberg, J. T., Chen, B. E., Knott, G. W., Feng, G., Sanes, J. R., Welker, E., & Svoboda, K. (2002) · Nature · doi:10.1038/nature01273 · Cited by 1,927
NEURONAL CIRCUITS OF THE NEOCORTEX
Douglas, R. J., & Martin, K. A. C. (2004) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.27.070203.144152 · Cited by 1,886
Research works printed but not linked: 9
- Govoreanu, B. et al. in Proc. 2011 IEEE International Electron Devices Meeting (ed. Mahoney, P. W.) 31.6 (IEEE, (2011).
- Ma, X., Strukov, D. B., Lee, J. H. & Likharev, K. K. in Proc. 5th IEEE Conference on Nanotechnology (ed. Arai, F.) 175–178 (IEEE, (2005).
- Mayr, C. et al. in Proc. NIPS 2012 - Advances in Neural Information Processing Systems 25 (ed. Pereira, F. et al.) 1700–1708 (Neural Information Processing Systems Foundation, Inc., 2012).
- Habenschuss, S., Bill, J. & Nessler, B. in Proc. NIPS 2012 - Advances in Neural Information Processing Systems 25 (eds Pereira, F. et al.) 773–781 (Neural Information Processing Systems Foundation, Inc., (2012).
- Nessler, B., Pfeiffer, M. & Maass, W. in Advances in Neural Information Processing Systems, 1357–1365 (2009).
- Makosiej, A., Thomas, O., Amara, A. & Vladimirescu, A. in Proc. ISCAS 2013 - IEEE International Symposium on Circuits and Systems (eds Chen, C. W. et al.) 1460–1463 (IEEE, (2013). · doi:10.1109/iscas.2013.6572132
- Leung, W., Hsu, F.-C. & Jones, M.-E. in Proc. IEEE International ASIC/SOC Conference (eds Buchner, T. et al.) 32–36 (IEEE, (2000).
- Burr, G. W. et al. in Proc. VLSIT 2013 - Symposium on VLSI Technology (ed. Jammy, R.) T66–T67 (IEEE, (2013).
- Kappel, D., Habenschuss, S., Legenstein, R. & Maass, W. in Advances in Neural Information Processing Systems, 370–378 (2015).
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
Fully hardware-implemented memristor convolutional neural network
Yao, P., Wu, H., Gao, B., Tang, J., Zhang, Q., Zhang, W., Yang, J. J., & Qian, H. (2020) · Nature · doi:10.1038/s41586-020-1942-4 · Cited by 2,179
Memristive crossbar arrays for brain-inspired computing
Xia, Q., & Yang, J. J. (2019) · Nature Materials · doi:10.1038/s41563-019-0291-x · Cited by 1,800
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
Metadata
- Authors
- Serb, A., Bill, J., Khiat, A., Berdan, R., Legenstein, R., & Prodromakis, T.
- Venue
- Nature Communications
- Published
- 2016-09-29
- Volume
- 7
- Issue
- 1
- Pages
- 12611-12611
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2525649597
- Cited by
- 339
- References
- 56
- FWCI
- 19.21
- Type
- journal article
- Language
- en
- Open access
- gold
- Licence
- creativecommons.org/licenses/by/4.0 (tdm)
- creativecommons.org/licenses/by/4.0 (vor)
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- Serb, A., Bill, J., Khiat, A., Berdan, R., Legenstein, R., & Prodromakis, T. (2016). Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses. Nature Communications. https://doi.org/10.1038/ncomms12611
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