The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Plasticity in memristive devices for spiking neural networks
Open accessAbstract
Memristive devices present a new device technology allowing for the realization of compact non-volatile memories. Some of them are already in the process of industrialization. Additionally, they exhibit complex multilevel and plastic behaviors, which make them good candidates for the implementation of artificial synapses in neuromorphic engineering. However, memristive effects rely on diverse physical mechanisms, and their plastic behaviors differ strongly from one technology to another. Here, we present measurements performed on different memristive devices and the opportunities that they provide. We show that they can be used to implement different learning rules whose properties emerge directly from device physics: real time or accelerated operation, deterministic or stochastic behavior, long term or short term plasticity. We then discuss how such devices might be integrated into a complete architecture. These results highlight that there is no unique way to exploit memristive devices in neuromorphic systems. Understanding and embracing device physics is the key for their optimal use.
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Cites
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A synaptic model of memory: long-term potentiation in the hippocampus
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Memristor—The Missing Circuit Element
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“In 1971, Leon Chua indicated the possible existence of a fourth basic electrical component (Chua, 1971).”
BackgroundHebb, D. O. (2005) · doi:10.4324/9781410612403 · Cited by 9,825
Nanoionics-based resistive switching memories
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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
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×
“This component, the memristor, would complement those already known namely resistance, capacitor, and inductor, and offer new opportunities for system design (Chua and Kang, 1976).”
BackgroundShort-term plasticity and long-term potentiation mimicked in single inorganic synapses
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Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing
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Cited by
Neuromorphic computing with multi-memristive synapses
Boybat, I., Le Gallo, M., Nandakumar, S. R., Moraitis, T., Parnell, T., Tuma, T., Rajendran, B., Leblebici, Y., Sebastian, A., & Eleftheriou, E. (2018) · Nature Communications · doi:10.1038/s41467-018-04933-y · Cited by 899
Tang, J., Yuan, F., Shen, X., Wang, Z., Rao, M., He, Y., Sun, Y., Li, X., Zhang, W., Li, Y., et al. (2019) · Advanced Materials · doi:10.1002/adma.201902761 · Cited by 834
Gokmen, T., & Vlasov, Y. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00333 · Cited by 491
Recent advances in convolutional neural network acceleration
Zhang, Q., Zhang, M., Chen, T., Sun, Z., Ma, Y., & Yu, B. (2019) · Neurocomputing · doi:10.1016/j.neucom.2018.09.038 · Cited by 434
Spin-Transfer Torque Magnetic Memory as a Stochastic Memristive Synapse for Neuromorphic Systems
Vincent, A. F., Larroque, J., Locatelli, N., Ben Romdhane, N., Bichler, O., Gamrat, C., Zhao, W. S., Klein, J.-O., Galdin-Retailleau, S., & Querlioz, D. (2015) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2015.2414423 · Cited by 360
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Metadata
- Authors
- Saïghi, S., Mayr, C. G., Serrano-Gotarredona, T., Schmidt, H., Lecerf, G., Tomas, J., Grollier, J., Boyn, S., Vincent, A. F., Querlioz, D., La Barbera, S., Alibart, F., Vuillaume, D., Bichler, O., Gamrat, C., & Linares-Barranco, B.
- Venue
- Frontiers in Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2015-03-02
- Volume
- 9
- Pages
- 51-51
- ISSN
- 1662-453X (ISSN-L)
- OpenAlex
- W2083407323
- Cited by
- 238
- References
- 15
- FWCI
- 13.47
- Type
- journal article
- Language
- en
- Open access
- gold
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- Saïghi, S., Mayr, C. G., Serrano-Gotarredona, T., Schmidt, H., Lecerf, G., Tomas, J., Grollier, J., Boyn, S., Vincent, A. F., Querlioz, D., La Barbera, S., Alibart, F., Vuillaume, D., Bichler, O., Gamrat, C., & Linares-Barranco, B. (2015). Plasticity in memristive devices for spiking neural networks. Frontiers in Neuroscience. https://doi.org/10.3389/fnins.2015.00051
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