Bioinspired activation of silent synapses in layered materials for extensible neuromorphic computing Article Swipe
Yan Kang
,
Yabo Chen
,
Yinlong Tan
,
Hao Hao
,
Cheng Li
,
Xiangnan Xie
,
Weihong Hua
,
Tian Jiang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.jmat.2023.02.007
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.jmat.2023.02.007
Activation of silent synapses is of great significance for the extension of neural plasticity related to learning and memory. Inspired by the activation of silent synapses via receptor insertion in neural synapses, we propose an efficient method for activating artificial synapses through the intercalation of Sn in layered α-MoO3. Sn intercalation is capable of switching on the response of layered α-MoO3 to the stimuli of visible and near infrared light by decreasing the bandgap. This mimics the receptor insertion process in silent neural synapses. The Sn-intercalated MoO3 (Sn-MoO3) exhibits persistent photoconductivity due to the donor impurity induced by Sn intercalation. This enables the two-terminal Sn-MoO3 device promising optoelectronic synapse with an ultrahigh paired pulse facilitation (PPF) up to 199.5%. On-demand activation and tunable synaptic plasticity endow the device great potentials for extensible neuromorphic computing. Superior performance of the extensible artificial neural network (ANN) based on the Sn-MoO3 synapses are demonstrated in pattern recognition. Impressively, the recognition accuracy increases from 89.7% to 94.8% by activating more nodes into the ANN. This is consistent with the recognition process of physical neural network during brain development. The intercalation engineering of MoO3 may provide inspirations for the design of high-performance neuromorphic computing architectures.
Related Topics
Concepts
Neuromorphic engineering
Materials science
Synapse
Artificial neural network
Intercalation (chemistry)
Neural facilitation
Computer science
Optoelectronics
Neuroscience
Nanotechnology
Artificial intelligence
Excitatory postsynaptic potential
Physics
Biology
Quantum mechanics
Inhibitory postsynaptic potential
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.jmat.2023.02.007
- OA Status
- gold
- Cited By
- 8
- References
- 53
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4360836760
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4360836760Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.jmat.2023.02.007Digital Object Identifier
- Title
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Bioinspired activation of silent synapses in layered materials for extensible neuromorphic computingWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-03-24Full publication date if available
- Authors
-
Yan Kang, Yabo Chen, Yinlong Tan, Hao Hao, Cheng Li, Xiangnan Xie, Weihong Hua, Tian JiangList of authors in order
- Landing page
-
https://doi.org/10.1016/j.jmat.2023.02.007Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.jmat.2023.02.007Direct OA link when available
- Concepts
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Neuromorphic engineering, Materials science, Synapse, Artificial neural network, Intercalation (chemistry), Neural facilitation, Computer science, Optoelectronics, Neuroscience, Nanotechnology, Artificial intelligence, Excitatory postsynaptic potential, Physics, Biology, Quantum mechanics, Inhibitory postsynaptic potentialTop concepts (fields/topics) attached by OpenAlex
- Cited by
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8Total citation count in OpenAlex
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2025: 5, 2024: 3Per-year citation counts (last 5 years)
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53Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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