The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Short-Term Plasticity and Long-Term Potentiation in Artificial Biosynapses with Diffusive Dynamics
Abstract
The development of electronic devices possessing the functionality of biological synapses is a crucial step toward replicating the capabilities of the human brain. Of the various materials that have been used to realize artificial synapses, renewable natural materials have the advantages of being abundant, inexpensive, biodegradable, and ecologically benign. In this study, we report a biocompatible artificial synapse based on a matrix of the biopolymer ι-carrageenan (ι-car), which exploits Ag dynamics. This artificial synapse emulates the short-term plasticity (STP), paired-pulse facilitation (PPF), and transition from STP to long-term potentiation (LTP) of a biological synapse. The above-mentioned characteristics are realized by exploiting the similarities between the Ag dynamics in the ι-car matrix and the Ca 2+ dynamics in a biological synapse. By demonstrating a method that uses biomaterials and Ag dynamics to emulate synaptic functions, this study confirms that ι-car has the potential for constructing neuromorphic systems that use biocompatible artificial synapses.
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
A synaptic model of memory: long-term potentiation in the hippocampus
Bliss, T. V. P., & Collingridge, G. L. (1993) · Nature · doi:10.1038/361031a0 · Cited by 11,566
Human Memory: A Proposed System and its Control Processes
Atkinson, R. C., & Shiffrin, R. M. (1968) · Psychology of Learning and Motivation · doi:10.1016/s0079-7421(08)60422-3 · Cited by 7,216
Redox‐Based Resistive Switching Memories – Nanoionic Mechanisms, Prospects, and Challenges
Waser, R., Dittmann, R., Staikov, G., & Szot, K. (2009) · Advanced Materials · doi:10.1002/adma.200900375 · Cited by 5,080
Short-Term Synaptic Plasticity
Zucker, R. S., & Regehr, W. G. (2002) · Annual Review of Physiology · doi:10.1146/annurev.physiol.64.092501.114547 · Cited by 5,068
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 for computing
Yang, J. J., Strukov, D. B., & Stewart, D. R. (2013) · Nature Nanotechnology · doi:10.1038/nnano.2012.240 · Cited by 3,885
Neuromorphic electronic systems
Mead, C. (1990) · Proceedings of the IEEE · doi:10.1109/5.58356 · Cited by 2,368
Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing
Wang, Z., Joshi, S., Savel’ev, S. E., Jiang, H., Midya, R., Lin, P., Hu, M., Ge, N., Strachan, J. P., Li, Z., et al. (2017) · Nature Materials · doi:10.1038/nmat4756 · Cited by 2,358
Genetic enhancement of learning and memory in mice
Tang, Y.-P., Shimizu, E., Dube, G. R., Rampon, C., Kerchner, G. A., Zhuo, M., Liu, G., & Tsien, J. Z. (1999) · Nature · doi:10.1038/43432 · Cited by 1,863
Short-term plasticity and long-term potentiation mimicked in single inorganic synapses
Ohno, T., Hasegawa, T., Tsuruoka, T., Terabe, K., Gimzewski, J. K., & Aono, M. (2011) · Nature Materials · doi:10.1038/nmat3054 · Cited by 1,793
Cited by
Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics
Park, H., Lee, Y., Kim, N., Seo, D., Go, G., & Lee, T. (2020) · Advanced Materials · doi:10.1002/adma.201903558 · Cited by 533
Recent Progress in Three‐Terminal Artificial Synapses: From Device to System
Han, H., Yu, H., Wei, H., Gong, J., & Xu, W. (2019) · Small · doi:10.1002/smll.201900695 · Cited by 348
Surface diffusion-limited lifetime of silver and copper nanofilaments in resistive switching devices
Wang, W., Wang, M., Ambrosi, E., Bricalli, A., Laudato, M., Sun, Z., Chen, X., & Ielmini, D. (2019) · Nature Communications · doi:10.1038/s41467-018-07979-0 · Cited by 328
Metadata
- Authors
- Kim, M.-K., & Lee, J.-S.
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2018-01-26
- Volume
- 12
- Issue
- 2
- Pages
- 1680-1687
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W2784557985
- Cited by
- 294
- References
- 60
- FWCI
- 12.32
- 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
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.
