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Short-term plasticity and long-term potentiation mimicked in single inorganic synapses
Abstract
Memory is believed to occur in the human brain as a result of two types of synaptic plasticity: short-term plasticity (STP) and long-term potentiation (LTP; refs 1-4). In neuromorphic engineering, emulation of known neural behaviour has proven to be difficult to implement in software because of the highly complex interconnected nature of thought processes. Here we report the discovery of a Ag(2)S inorganic synapse, which emulates the synaptic functions of both STP and LTP characteristics through the use of input pulse repetition time. The structure known as an atomic switch, operating at critical voltages, stores information as STP with a spontaneous decay of conductance level in response to intermittent input stimuli, whereas frequent stimulation results in a transition to LTP. The Ag(2)S inorganic synapse has interesting characteristics with analogies to an individual biological synapse, and achieves dynamic memorization in a single device without the need of external preprogramming. A psychological model related to the process of memorizing and forgetting is also demonstrated using the inorganic synapses. Our Ag(2)S element indicates a breakthrough in mimicking synaptic behaviour essential for the further creation of artificial neural systems that emulate characteristics of human memory.
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“They can also be used to implement digital logic [9], synapses in neural applications [10,11], and hybrid circuits [6,12].”
From Sub-nanosecond switching of a tantalum oxide memristor (2011) · cites this paper as [11]
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
(1982) · Trends in Neurosciences · doi:10.1016/0166-2236(82)90076-5 · Cited by 9,150
The Psychology of Learning and Motivation: Advances in Research and Theory
(2010) · The Psychology of learning and motivation/The psychology of learning and motivation · doi:10.1016/c2009-0-62209-1 · Cited by 6,035
Nanoionics-based resistive switching memories
Waser, R., & Aono, M. (2007) · Nature Materials · doi:10.1038/nmat2023 · Cited by 4,923
Self-citationBi, G.-Q., & Poo, M.-M. (1998) · The Journal of Neuroscience · doi:10.1523/jneurosci.18-24-10464.1998 · Cited by 4,744
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 switching mechanism for metal/oxide/metal nanodevices
Yang, J. J., Pickett, M. D., Li, X., Ohlberg, D. A. A., Stewart, D. R., & Williams, R. S. (2008) · Nature Nanotechnology · doi:10.1038/nnano.2008.160 · Cited by 2,987
Memory: A Contribution to Experimental Psychology
Ebbinghaus, H. (2013) · Annals of Neurosciences · doi:10.5214/ans.0972.7531.200408 · Cited by 2,827
Synaptic Plasticity and Memory: An Evaluation of the Hypothesis
Martin, S. J., Grimwood, P. D., & Morris, R. G. M. (2000) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.23.1.649 · Cited by 2,794
Cellular neural networks: applications
Chua, L. O., & Yang, L. (1988) · IEEE Transactions on Circuits and Systems · doi:10.1109/31.7601 · Cited by 2,374
Flagged first, then by citations
2 of the 41 references couldn’t be matched to a known paper.
Research works printed but not linked: 5
- Hammerstrom, D. in Nanotechnology: Information Technology II (ed. Waser, R.) 251–285 (Wiley–VCH, 2008).
- Terabe, K., Hasegawa, T., Nakayama, T. & Aono, M. Quantum point contact switch realized by solid electrochemical reaction. Riken Rev. 37, 7–8 (2001).
- Kozicki, M. N. & Mitkova, M. in Nanotechnology: Information Technology I (ed. Waser, R.) 485–515 (Wiley–VCH, 2008).
- Sala, S. D. (ed.) Forgetting (Psychology Press, 2010).
- Kudo, T. & Fueki, K. Solid State Ionics (Kodansha/VCH, 1990).
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Ginnaram, S., Qiu, J.-T. T., & Maikap, S. (2020) · IEEE Electron Device Letters · doi:10.1109/led.2020.2980625 · Cited by 40
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Simanjuntak, F. M., Ohno, T., & Samukawa, S. (2019) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.9b00617 · Cited by 39
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Islam, R., Shi, Y., Silva, G. V. D. O., Sachdev, M., & Miao, G. (2024) · ACS Nano · doi:10.1021/acsnano.4c05137 · Cited by 39
Chen, Y., Xia, J., Qu, Y., Zhang, H., Mei, T., Zhu, X., Xu, G., Li, D., Wang, L., Liu, Q., et al. (2025) · Advanced Materials · doi:10.1002/adma.202419013 · Cited by 40
Lim, H., Kim, I., Kim, J.-S., Hwang, C. S., & Jeong, D. S. (2013) · Nanotechnology · doi:10.1088/0957-4484/24/38/384005 · Cited by 40
Memristor and selector devices fabricated from HfO2−xNx
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Iqbal, S., Duy, L. T., Kang, H., Singh, R., Kumar, M., Park, J., & Seo, H. (2021) · Advanced Functional Materials · doi:10.1002/adfm.202102567 · Cited by 39
Terabe, K., Tsuchiya, T., & Tsuruoka, T. (2021) · Advanced Electronic Materials · doi:10.1002/aelm.202100645 · Cited by 39
Self-citationLi, X., Li, S., Tang, B. H., Liao, J., & Chen, Q. (2022) · Advanced Electronic Materials · doi:10.1002/aelm.202200343 · Cited by 39
Luo, F., Wu, Y., Tong, J., Tian, F., & Zhang, X. (2023) · Nano Research · doi:10.1007/s12274-023-5411-x · Cited by 39
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Valov, I., & Waser, R. M. (2012) · Advanced Materials · doi:10.1002/adma.201202592 · Cited by 38
Shi, T., Yin, X.-B., Yang, R., & Guo, X. (2016) · Physical Chemistry Chemical Physics · doi:10.1039/c5cp07675g · Cited by 37
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Sun, Z., Zhao, Y., He, M., Gu, L., Ma, C., Jin, K., Zhao, D., Luo, N., Zhang, Q., Wang, N., et al. (2016) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.6b01400 · Cited by 37
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Sun, L., Yu, H., Wang, D., Jiang, J., Kim, D., Kim, H. W., Zheng, S., Zhao, M., Ge, Q., & Yang, H. (2018) · 2D Materials · doi:10.1088/2053-1583/aaf3d0 · Cited by 38
Tan, H., Majumdar, S., Qin, Q., Lahtinen, J., & van Dijken, S. (2019) · Advanced Intelligent Systems · doi:10.1002/aisy.201900036 · Cited by 37
Jiang, D., Li, J., Fu, W., Chen, Q., Yang, Y., Zhou, Y.-H., & Zhang, J. (2020) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.0c00474 · Cited by 37
Yu, R., Yan, Y., Li, E., Wu, X., Zhang, X., Chen, J., Hu, Y., Chen, H., & Guo, T. (2021) · Materials Horizons · doi:10.1039/d1mh01061a · Cited by 37
Kumar, M., Lim, J., & Seo, H. (2021) · Nano Energy · doi:10.1016/j.nanoen.2021.106471 · Cited by 37
Ismail, M., Mahata, C., Kang, M., & Kim, S. (2022) · Nanoscale Research Letters · doi:10.1186/s11671-022-03699-z · Cited by 38
Wei, H., Xu, Z., Ni, Y., Yang, L., Sun, L., Gong, J., Zhang, S., Qu, S., & Xu, W. (2023) · Nano Letters · doi:10.1021/acs.nanolett.3c02836 · Cited by 36
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Subramaniam, A., Cantley, K. D., Bersuker, G. I., Gilmer, D. C., & Vogel, E. M. (2013) · IEEE Transactions on Nanotechnology · doi:10.1109/tnano.2013.2256366 · Cited by 36
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Hui, F., Liu, P., Hodge, S. A., Carey, T., Wen, C., Torrisi, F., Galhena, D. T. L., Tomarchio, F., Lin, Y., Moreno, E., et al. (2021) · Small · doi:10.1002/smll.202101100 · Cited by 36
Cao, Y., Zhao, T., Zhao, C., Liu, Y., Song, P., Gao, H., & Zhao, C. (2022) · Journal of Materials Chemistry C · doi:10.1039/d1tc04827a · Cited by 36
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Self-citationMimic synaptic behavior with a single floating gate transistor: A MemFlash synapse
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Guo, L., Wen, J., Cheng, G., Yuan, N., & Ding, J. (2016) · Journal of Materials Chemistry C · doi:10.1039/c6tc02228f · Cited by 35
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Ge, N., Yoon, J. H., Hu, M., Grafals, E. M., Dávila, N., Strachan, J. P., Li, Z., Holder, H., Xia, Q., Williams, R. S., et al. (2017) · Scientific Reports · doi:10.1038/srep40135 · Cited by 34
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Huang, C., Zhang, Y., & Nomura, K. (2022) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.1c24327 · Cited by 38
Wang, L., Wang, Z., Lin, J., Yang, J., Xie, L., Yi, M., Li, W., Ling, H., Ou, C., & Huang, W. (2016) · Scientific Reports · doi:10.1038/srep35273 · Cited by 34
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Chaudhary, M., Yang, T., Chen, C., Lai, P., Hsu, Y., Peng, Y., Kumar, A., Lee, C., & Chueh, Y. (2023) · Advanced Functional Materials · doi:10.1002/adfm.202303697 · Cited by 33
Yuan, L., Zhao, T., Dai, J., Xue, L., Zhang, X., Peng, C., Wen, P., Liu, H., Hu, H., Chen, L., et al. (2025) · Advanced Functional Materials · doi:10.1002/adfm.202418052 · Cited by 34
Rafique, H., Abbas, G., Mendes, M. J., Barquinha, P. M. C., Martins, R., Fortunato, E. M. C., Águas, H., & Jana, S. (2025) · Nano-Micro Letters · doi:10.1007/s40820-025-01823-z · Cited by 33
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Self-citationBuilding Light Stimulated Synaptic Memory Devices for Visual Memory Simulation
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Bousoulas, P., Panagopoulou, M., Boukos, N. K., & Tsoukalas, D. (2021) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/abea3b · Cited by 33
Multiplexed neurotransmission emulated for emotion control
Ni, Y., Ma, M., Wei, H., Gong, J., Han, H., Liu, L., Xu, Z., & Xu, W. (2021) · Nano Energy · doi:10.1016/j.nanoen.2021.106038 · Cited by 34
Shamsi, J., Avedillo, M. J., Linares-Barranco, B., & Serrano‐Gotarredona, T. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.674567 · Cited by 34
Robust and Low-Power-Consumption Black Phosphorus–Graphene Artificial Synaptic Devices
Yuan, S., Qiu, B., Amina, K., Li, L., Zhai, P., Su, Y., Xue, T., Jiang, T., Ding, L., & Wei, G. (2022) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.2c03667 · Cited by 33
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Lee, M.-S., Lee, J.-W., Kim, C.-H., Park, B., & Lee, J. (2015) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2014.2378758 · Cited by 33
Tsuruoka, T., Hasegawa, T., Terabe, K., & Aono, M. (2017) · Journal of Electroceramics · doi:10.1007/s10832-016-0063-9 · Cited by 33
Self-citationWang, Z., Wang, W., Liu, P., Liu, G., Li, J., Zhao, J., Zhou, Z., Wang, J., Pei, Y., Zhao, Z., et al. (2022) · Research · doi:10.34133/2022/9754876 · Cited by 32
Wan, Z., Zhang, Q., Hu, F., Dong, Y., Li, R., Hu, L., Xie, Y., Yue, Z., Chen, X., & Gu, M. (2022) · Advanced Optical Materials · doi:10.1002/adom.202201852 · Cited by 32
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Metadata
- Authors
- Ohno, T., Hasegawa, T., Tsuruoka, T., Terabe, K., Gimzewski, J. K., & Aono, M.
- Affiliations
- National Institute for Materials Science (JP)
- California NanoSystems Institute (US)
- Corresponding author
- Ohno, Takeo
- Hasegawa, Tsuyoshi
- Venue
- Nature Materials
- Published
- 2011-06-26
- Volume
- 10
- Issue
- 8
- Pages
- 591-595
- ISSN
- 1476-1122 (ISSN-L)
- 1476-4660
- OpenAlex
- W2071947829
- Semantic Scholar
- 61633cf7b93c6ef2eab0c74d661654a27ea52aae
- Cited by
- 1,793
- References
- 41
- Citations by year
- 2026: 68 · 2025: 133 · 2024: 126 · 2023: 162 · 2022: 166 · 2021: 173 · 2020: 182 · 2019: 163 · 2018: 182 · 2017: 106 · 2016: 119 · 2015: 61 · 2014: 48 · 2013: 61 · 2012: 33
- FWCI
- 33.77
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nature Materials (publishedVersion)
- PubMed (publishedVersion)
- Bristol Research (University of Bristol) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Ferroelectric and Negative Capacitance Devices
- Physical Sciences › Materials Science › Materials Chemistry › Phase-change materials and chalcogenides
- Keywords
- short-term plasticity, long-term potentiation, Ag2S, atomic switch, neuromorphic engineering, synaptic plasticity emulation, pulse repetition time, artificial neural networks
- MeSH terms
- Electric Stimulation, Neuronal Plasticity, Synapses, Long-Term Potentiation, Silver Compounds, Excitatory Postsynaptic Potentials
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