Cited by 1,793Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance DevicesPhase-change materials and chalcogenides

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Short-term plasticity and long-term potentiation mimicked in single inorganic synapses

Ohno, Takeo, Hasegawa, Tsuyoshi, Tsuruoka, Tohru, Terabe, Kazuya, Gimzewski, James K., Aono, Masakazu·2011·Nature Materials

DOI 10.1038/nmat3054

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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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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
Principles of neural science
(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-citation
Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type
Bi, 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

Show all 41 references

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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Wang, Y., Yin, L., Huang, W., Li, Y., Huang, S., Zhu, Y., Yang, D., & Pi, X. (2020) · Advanced Intelligent Systems · doi:10.1002/aisy.202000099 · Cited by 415
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Ahmed, T., Kuriakose, S., Mayes, E. L. H., Ramanathan, R., Bansal, V., Bhaskaran, M. C., Sriram, S., & Walia, S. (2019) · Small · doi:10.1002/smll.201900966 · Cited by 335
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Shen, Z., Zhao, C., Qi, Y., Xu, W., Liu, Y., Mitrović, I. Z., Li, Y., & Zhao, C. (2020) · Nanomaterials · doi:10.3390/nano10081437 · Cited by 333
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Jiang, J., Guo, J., Wan, X., Yang, Y., Xie, H., Niu, D., Yang, J., He, J., Gao, Y., & Wan, Q. (2017) · Small · doi:10.1002/smll.201700933 · Cited by 328
Surface diffusion-limited lifetime of silver and copper nanofilaments in resistive switching devices
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Shim, H., Sim, K., Ershad, F., Yang, P., Thukral, A., Rao, Z., Kim, H., Liu, Y., Wang, X., Gu, G., et al. (2019) · Science Advances · doi:10.1126/sciadv.aax4961 · Cited by 316
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Hong, X., Loy, D. J., Dananjaya, P. A., Tan, F., Ng, C., & Lew, W. S. (2018) · Journal of Materials Science · doi:10.1007/s10853-018-2134-6 · Cited by 307
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Ariga, K., Ji, Q., Nakanishi, W., Hill, J. P., & Aono, M. (2015) · Materials Horizons · doi:10.1039/c5mh00012b · Cited by 305
Self-citation
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Nanoarchitectonics: what's coming next after nanotechnology?
Ariga, K. (2021) · Nanoscale Horizons · doi:10.1039/d0nh00680g · Cited by 305
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Electrolyte-gated transistors for synaptic electronics, neuromorphic computing, and adaptable biointerfacing
Ling, H., Koutsouras, D. A., Kazemzadeh, S., van de Burgt, Y., Yan, F., & Gkoupidenis, P. (2020) · Applied Physics Reviews · doi:10.1063/1.5122249 · Cited by 301

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
ORCID: Ohno, Takeo
Hasegawa, Tsuyoshi
Tsuruoka, Tohru
Terabe, Kazuya
Gimzewski, James K.
Aono, Masakazu
Venue: Nature Materials
Publisher: Springer Science and Business Media LLC
Published: 2011-06-26
Volume: 10
Issue: 8
Pages: 591-595
ISSN: 1476-1122 (ISSN-L)
1476-4660
DOI: 10.1038/nmat3054
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
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
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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Short-term plasticity and long-term potentiation mimicked in single inorganic synapses

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