The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Recent Advances in Transistor‐Based Artificial Synapses
Abstract
Simulating biological synapses with electronic devices is a re‐emerging field of research. It is widely recognized as the first step in hardware building brain‐like computers and artificial intelligent systems. Thus far, different types of electronic devices have been proposed to mimic synaptic functions. Among them, transistor‐based artificial synapses have the advantages of good stability, relatively controllable testing parameters, clear operation mechanism, and can be constructed from a variety of materials. In addition, they can perform concurrent learning, in which synaptic weight update can be performed without interrupting the signal transmission process. Synergistic control of one device can also be implemented in a transistor‐based artificial synapse, which opens up the possibility of developing robust neuron networks with significantly fewer neural elements. These unique features of transistor‐based artificial synapses make them more suitable for emulating synaptic functions than other types of devices. However, the development of transistor‐based artificial synapses is still in its very early stages. Herein, this article presents a review of recent advances in transistor‐based artificial synapses in order to give a guideline for future implementation of synaptic functions with transistors. The main challenges and research directions of transistor‐based artificial synapses are also presented.
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Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
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
Training and operation of an integrated neuromorphic network based on metal-oxide memristors
Prezioso, M., Merrikh-Bayat, F., Hoskins, B. D., Adam, G. C., Likharev, K. K., & Strukov, D. B. (2015) · Nature · doi:10.1038/nature14441 · Cited by 3,001
Spike Timing–Dependent Plasticity: A Hebbian Learning Rule
Caporale, N., & Dan, Y. (2008) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.31.060407.125639 · Cited by 1,997
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
Abbott, L. F., & Regehr, W. G. (2004) · Nature · doi:10.1038/nature03010 · Cited by 1,684
A bioinspired flexible organic artificial afferent nerve
Kim, Y., Chortos, A., Xu, W., Liu, Y., Oh, J. Y., Son, D., Kang, J., Foudeh, A. M., Zhu, C., Lee, Y., et al. (2018) · Science · doi:10.1126/science.aao0098 · Cited by 1,466
Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing
Kuzum, D., Jeyasingh, R. G. D., Lee, B., & Wong, H.-S. P. (2012) · Nano Letters · doi:10.1021/nl201040y · Cited by 1,267
Neuro-Inspired Computing With Emerging Nonvolatile Memorys
Yu, S. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2018.2790840 · Cited by 1,170
Fully memristive neural networks for pattern classification with unsupervised learning
Wang, Z., Joshi, S., Savel’ev, S., Song, W., Midya, R., Li, Y., Rao, M., Yan, P., Asapu, S., Zhuo, Y., et al. (2018) · Nature Electronics · doi:10.1038/s41928-018-0023-2 · Cited by 1,106
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Hou, Y.-X., Li, Y., Zhang, Z.-C., Li, J.-Q., Qi, D.-H., Chen, X.-D., Wang, J.-J., Yao, B.-W., Yu, M.-X., Lu, T.-B., et al. (2021) · ACS Nano · doi:10.1021/acsnano.0c08921 · Cited by 438
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Torricelli, F., Adrahtas, D. Z., Bao, Z., Berggren, M., Biscarini, F., Bonfiglio, A., Bortolotti, C. A., Frisbie, C. D., Macchia, E., Malliaras, G. G., et al. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00065-8 · Cited by 422
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Yu, J., Yang, X., Gao, G., Xiong, Y., Wang, Y., Han, J., Chen, Y., Zhang, H., Sun, Q., & Wang, Z. L. (2021) · Science Advances · doi:10.1126/sciadv.abd9117 · Cited by 389
Self-citationSun, T., Feng, B., Huo, J., Xiao, Y., Wang, W., Peng, J., Li, Z., Du, C., Wang, W., Zou, G., et al. (2024) · Nano-Micro Letters · doi:10.1007/s40820-023-01235-x · Cited by 384
2D Material Based Synaptic Devices for Neuromorphic Computing
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Metadata
- Authors
- Dai, S., Zhao, Y., Wang, Y., Zhang, J., Fang, L., Jin, S., Shao, Y., & Huang, J.
- Venue
- Advanced Functional Materials
- Publisher
- Wiley
- Published
- 2019-08-09
- Volume
- 29
- Issue
- 42
- ISSN
- 1616-301X (ISSN-L)
- 1616-3028
- OpenAlex
- W2968992554
- Cited by
- 671
- References
- 12
- FWCI
- 23.53
- Type
- journal article
- Language
- en
- Open access
- closed
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