An approximate backpropagation learning rule for memristor based neural networks using synaptic plasticity
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Cites
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× · see citing works
“Moreover, the gradual change of the state in such device can be achieved by applying a voltage pulse train — the property which has been demonstrated in the works reporting on synaptic plasticity effects [20], [8].”
BackgroundPattern classification by memristive crossbar circuits using ex situ and in situ training
Alibart, F., Zamanidoost, E., & Strukov, D. B. (2013) · Nature Communications · doi:10.1038/ncomms3072 · Cited by 642 · see citing works
Everything You Wish to Know About Memristors But Are Afraid to Ask
Chua, L. (2015) · Radioengineering · doi:10.13164/re.2015.0319 · Cited by 481 · see citing works
Flagged first, then by citations
Research works printed but not linked: 12
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Cited by
Sorted by citations
Reliability of analog resistive switching memory for neuromorphic computing
Zhao, M., Gao, B., Tang, J., Qian, H., & Wu, H. (2020) · Applied Physics Reviews · doi:10.1063/1.5124915 · Cited by 349 · see citing works
From memristors to compositional machine learning
Nugent, A., & Molter, T. (2017) · Proceedings of the Neuromorphic Computing Symposium · doi:10.1145/3183584.3183615
“In the first approach, groups attempt to implement successful algorithms (primarily DNN trained with stochastic gradient descent) within the constraints of hardware which include limited weight precision, memristor decay, circuit noise, fabrication variations, and non-symmetric and/or stochastic conductance incrementa…”
Background
- Authors
- Negrov, D., Karandashev, I., Shakirov, V., Matveyev, Y., Dunin-Barkowski, W., & Zenkevich, A.
- Affiliations
- Moscow Institute of Physics and Technology (RU)
- Russian Academy of Sciences (RU)
- Corresponding author
- Negrov, D.
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2017-05
- Volume
- 237
- Pages
- 193-199
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2964124294
- Cited by
- 49
- References
- 3
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 2 · 2023: 5 · 2022: 5 · 2021: 5 · 2020: 11 · 2019: 4 · 2018: 9 · 2017: 3
- FWCI
- 2.46
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Neurocomputing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Russian Science Foundation (14-19-01698)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Reservoir Computing
- Keywords
- memristors, synaptic plasticity, deep neural network, in-memory computing, synaptic weight update, multilayer neural networks, neuromorphic hardware
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