The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Neuromorphic, Digital, and Quantum Computation With Memory Circuit Elements
Open accessAbstract
Memory effects are ubiquitous in nature and the class of memory circuit elements—which includes memristive, memcapacitive, and meminductive systems—shows great potential to understand and simulate the associated physical processes. Here, we show that such elements can also be used in electronic schemes mimicking biologically inspired computer architectures, performing digital logic and arithmetic operations, and can expand the capabilities of certain quantum computation schemes. In particular, we will discuss some examples where the concept of memory elements is relevant to the realization of associative memory in neuronal circuits, spike-timing-dependent plasticity (STDP) of synapses, and digital and field-programmable quantum computing.
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Citations
Cites
Advances in Neural Information Processing Systems 19
(2007) · The MIT Press eBooks · doi:10.52202/085713 · Cited by 15,539
Memristor—The Missing Circuit Element
Chua, L. (1971) · IEEE Transactions on Circuit Theory · doi:10.1109/tct.1971.1083337 · Cited by 10,268 · cited 3×
“Moreover, we note that memristors, memcapacitors, and meminductors are ideal (and rare) instances of memristive, memcapacitive, and meminductive systems, respectively (see [1], [3], and [7]).”
BackgroundAdvances in Neural Information Processing Systems 14: Proceedings of the 2001 Conference
(2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.001.0001 · Cited by 8,890
Quantum computation with quantum dots
Loss, D., & DiVincenzo, D. P. (1998) · Physical Review A · doi:10.1103/physreva.57.120 · Cited by 6,840
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
Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs
Markram, H., Lübke, J., Frotscher, M., & Sakmann, B. (1997) · Science · doi:10.1126/science.275.5297.213 · Cited by 3,832
Strong coupling of a single photon to a superconducting qubit using circuit quantum electrodynamics
Wallraff, A., Schuster, D. I., Blais, A., Frunzio, L., Huang, R. S., Majer, J., Kumar, S., Girvin, S. M., & Schoelkopf, R. J. (2004) · Nature · doi:10.1038/nature02851 · Cited by 3,796
Blais, A., Huang, R.-S., Wallraff, A., Girvin, S. M., & Schoelkopf, R. J. (2004) · Physical Review A · doi:10.1103/physreva.69.062320 · Cited by 2,989
Memristive switching mechanism for metal/oxide/metal nanodevices
Yang, J. J., Pickett, M. D., Li, X., Ohlberg, D. A. A., Stewart, D. R., & Williams, R. S. (2008) · Nature Nanotechnology · doi:10.1038/nnano.2008.160 · Cited by 2,987
Cited by
Synaptic electronics: materials, devices and applications
Kuzum, D., Yu, S., & Philip Wong, H.-S. (2013) · Nanotechnology · doi:10.1088/0957-4484/24/38/382001 · Cited by 1,258
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
Reservoir computing using dynamic memristors for temporal information processing
Du, C., Cai, F., Zidan, M. A., Ma, W., Lee, S. H., & Lu, W. D. (2017) · Nature Communications · doi:10.1038/s41467-017-02337-y · Cited by 1,015
A fully integrated reprogrammable memristor–CMOS system for efficient multiply–accumulate operations
Cai, F., Correll, J. M., Lee, S. H., Lim, Y., Bothra, V., Zhang, Z., Flynn, M. P., & Lu, W. D. (2019) · Nature Electronics · doi:10.1038/s41928-019-0270-x · Cited by 728
If it's pinched it's a memristor
Chua, L. (2014) · Semiconductor Science and Technology · doi:10.1088/0268-1242/29/10/104001 · Cited by 685
Metadata
- Authors
- Pershin, Y. V., & Di Ventra, M.
- Venue
- Proceedings of the IEEE
- Published
- 2012-06
- Volume
- 100
- Issue
- 6
- Pages
- 2071-2080
- ISSN
- 0018-9219 (ISSN-L)
- 1558-2256
- OpenAlex
- W2073273918
- Cited by
- 261
- References
- 60
- FWCI
- 16.71
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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