The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Neural Synaptic Weighting With a Pulse-Based Memristor Circuit
Abstract
A pulse-based programmable memristor circuit for implementing synaptic weights for artificial neural networks is proposed. In the memristor weighting circuit, both positive and negative multiplications are performed via a charge-dependent Ohm's law (). The circuit is composed of five memristors with bridge-like connections and operates like an artificial synapse with pulse-based processing and adjustability. The sign switching pulses, weight setting pulses and synaptic processing pulses are applied through a shared input terminal. Simulations are done with both linear memristor and window-based nonlinear memristor models.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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 1×
“Chua as the fourth basic element of electrical circuits [12],”
BackgroundSelf-citationNanoscale 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×
“recently reported the development of a new type of memristor which shows improved analog characteristics using Ag and Si in a sandwiched layer [25].”
BackgroundMemristive devices and systems
Chua, L. O., & Kang, S. M. (1976) · Proceedings of the IEEE · doi:10.1109/proc.1976.10092 · Cited by 2,604
Self-citation‘Memristive’ switches enable ‘stateful’ logic operations via material implication
Borghetti, J., Snider, G. S., Kuekes, P. J., Yang, J. J., Stewart, D. R., & Williams, R. S. (2010) · Nature · doi:10.1038/nature08940 · Cited by 1,929
Memristor−CMOS Hybrid Integrated Circuits for Reconfigurable Logic
Xia, Q., Robinett, W., Cumbie, M. W., Banerjee, N., Cardinali, T. J., Yang, J. J., Wu, W., Li, X., Tong, W. M., Strukov, D. B., et al. (2009) · Nano Letters · doi:10.1021/nl901874j · Cited by 671
Practical Approach to Programmable Analog Circuits With Memristors
Pershin, Y. V., & Di Ventra, M. (2010) · IEEE Transactions on Circuits and Systems I: Regular Papers · doi:10.1109/tcsi.2009.2038539 · Cited by 639
Cited by
Memristor-based neural networks
Thomas, A. (2013) · Journal of Physics D: Applied Physics · doi:10.1088/0022-3727/46/9/093001 · Cited by 471
Memristor Emulator for Memristor Circuit Applications
Kim, H., Sah, M. P., Yang, C., Cho, S., & Chua, L. O. (2012) · IEEE Transactions on Circuits and Systems I: Regular Papers · doi:10.1109/tcsi.2012.2188957 · Cited by 453
Self-citationMemristor Crossbar-Based Neuromorphic Computing System: A Case Study
Hu, M., Li, H., Chen, Y., Wu, Q., Rose, G. S., & Linderman, R. W. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2296777 · Cited by 414
Memristor Bridge Synapse-Based Neural Network and Its Learning
Adhikari, S. P., Yang, C., Kim, H., & Chua, L. O. (2012) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2012.2204770 · Cited by 386
Self-citationNeuromemristive Circuits for Edge Computing: A Review
Krestinskaya, O., James, A. P., & Chua, L. O. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2899262 · Cited by 319
Self-citation
Metadata
- Authors
- Kim, H., Sah, M. P., Yang, C., Roska, T., & Chua, L. O.
- Venue
- IEEE Transactions on Circuits and Systems I: Regular Papers
- Published
- 2012-01
- Volume
- 59
- Issue
- 1
- Pages
- 148-158
- ISSN
- 1549-8328 (ISSN-L)
- 1558-0806
- OpenAlex
- W1990306796
- Cited by
- 348
- References
- 7
- FWCI
- 20.50
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Neural Synaptic Weighting With a Pulse-Based Memristor Circuit. https://app.cochat.ai/papers/10.1109/tcsi.2011.2161360
- The paper
- Kim, H., Sah, M. P., Yang, C., Roska, T., & Chua, L. O. (2012). Neural Synaptic Weighting With a Pulse-Based Memristor Circuit. IEEE Transactions on Circuits and Systems I: Regular Papers. https://doi.org/10.1109/tcsi.2011.2161360
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
