Memristive Devices for Neuromorphic Applications: Comparative Analysis
Author:
Funder
Российский Фонд Фундаментальных Исследований
Publisher
Springer Science and Business Media LLC
Subject
Biomedical Engineering,Bioengineering
Link
https://link.springer.com/content/pdf/10.1007/s12668-020-00795-1.pdf
Reference221 articles.
1. Chua, L. (1971). Memristor—the missing circuit element. IEEE Transactions on Circuit Theory, 18, 507–519.
2. Indiveri, G., Linares-Barranco, B., Legenstein, R., Deligeorgis, & Prodromakis, T. (2013). Integration of nanoscale memristor synapses in neuromorphic computing architectures. Nanotechnology, 24, 384010.
3. Jeong, D. S., Kim, I., Ziegler, M., & Kohlstedt, H. (2013). Towards artificial neurons and synapses: a materials point of view. RSC Advances, 3, 3169–3183.
4. Ielmini, D. (2018). Brain-inspired computing with resistive switching memory (RRAM): devices, synapses and neural networks. Microelectronic Engineering, 190, 44–53.
5. Li, Y. B., Wang, Z. R., Midya, R., Xia, Q. F., & Yang, J. J. (2018). Review of memristor devices in neuromorphic computing: materials sciences and device challenges. Journal of Physics D: Applied Physics, 51, 503002.
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