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Biological receptor-inspired flexible artificial synapse based on ionic dynamics
The memristor has been regarded as a promising candidate for constructing a neuromorphic computing platform that is capable of confronting the bottleneck of the traditional von Neumann architecture. Here, inspired by the working mechanism of the G-protein-linked receptor of biological cells, a novel...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8433456/ https://www.ncbi.nlm.nih.gov/pubmed/34567694 http://dx.doi.org/10.1038/s41378-020-00189-z |
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author | Lu, Qifeng Sun, Fuqin Liu, Lin Li, Lianhui Wang, Yingyi Hao, Mingming Wang, Zihao Wang, Shuqi Zhang, Ting |
author_facet | Lu, Qifeng Sun, Fuqin Liu, Lin Li, Lianhui Wang, Yingyi Hao, Mingming Wang, Zihao Wang, Shuqi Zhang, Ting |
author_sort | Lu, Qifeng |
collection | PubMed |
description | The memristor has been regarded as a promising candidate for constructing a neuromorphic computing platform that is capable of confronting the bottleneck of the traditional von Neumann architecture. Here, inspired by the working mechanism of the G-protein-linked receptor of biological cells, a novel double-layer memristive device with reduced graphene oxide (rGO) nanosheets covered by chitosan (an ionic conductive polymer) as the channel material is constructed. The protons in chitosan and the functional groups in rGO nanosheets imitate the functions of the ligands and receptors of biological cells, respectively. Smooth changes in the response current depending on the historical applied voltages are observed, offering a promising pathway toward biorealistic synaptic emulation. The memristive behavior is mainly a result of the interaction between protons provided by chitosan and the defects and functional groups in the rGO nanosheets. The channel current is due to the hopping of protons through functional groups and is limited by the traps in the rGO nanosheets. The transition from short-term to long-term potentiation is achieved, and learning-forgetting behaviors of the memristor mimicking those of the human brain are demonstrated. Overall, the bioinspired memristor-type artificial synaptic device shows great potential in neuromorphic networks. |
format | Online Article Text |
id | pubmed-8433456 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-84334562021-09-24 Biological receptor-inspired flexible artificial synapse based on ionic dynamics Lu, Qifeng Sun, Fuqin Liu, Lin Li, Lianhui Wang, Yingyi Hao, Mingming Wang, Zihao Wang, Shuqi Zhang, Ting Microsyst Nanoeng Article The memristor has been regarded as a promising candidate for constructing a neuromorphic computing platform that is capable of confronting the bottleneck of the traditional von Neumann architecture. Here, inspired by the working mechanism of the G-protein-linked receptor of biological cells, a novel double-layer memristive device with reduced graphene oxide (rGO) nanosheets covered by chitosan (an ionic conductive polymer) as the channel material is constructed. The protons in chitosan and the functional groups in rGO nanosheets imitate the functions of the ligands and receptors of biological cells, respectively. Smooth changes in the response current depending on the historical applied voltages are observed, offering a promising pathway toward biorealistic synaptic emulation. The memristive behavior is mainly a result of the interaction between protons provided by chitosan and the defects and functional groups in the rGO nanosheets. The channel current is due to the hopping of protons through functional groups and is limited by the traps in the rGO nanosheets. The transition from short-term to long-term potentiation is achieved, and learning-forgetting behaviors of the memristor mimicking those of the human brain are demonstrated. Overall, the bioinspired memristor-type artificial synaptic device shows great potential in neuromorphic networks. Nature Publishing Group UK 2020-09-07 /pmc/articles/PMC8433456/ /pubmed/34567694 http://dx.doi.org/10.1038/s41378-020-00189-z Text en © The Author(s) 2020 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Lu, Qifeng Sun, Fuqin Liu, Lin Li, Lianhui Wang, Yingyi Hao, Mingming Wang, Zihao Wang, Shuqi Zhang, Ting Biological receptor-inspired flexible artificial synapse based on ionic dynamics |
title | Biological receptor-inspired flexible artificial synapse based on ionic dynamics |
title_full | Biological receptor-inspired flexible artificial synapse based on ionic dynamics |
title_fullStr | Biological receptor-inspired flexible artificial synapse based on ionic dynamics |
title_full_unstemmed | Biological receptor-inspired flexible artificial synapse based on ionic dynamics |
title_short | Biological receptor-inspired flexible artificial synapse based on ionic dynamics |
title_sort | biological receptor-inspired flexible artificial synapse based on ionic dynamics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8433456/ https://www.ncbi.nlm.nih.gov/pubmed/34567694 http://dx.doi.org/10.1038/s41378-020-00189-z |
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