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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]
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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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