Cargando…
Mimicking efferent nerves using a graphdiyne-based artificial synapse with multiple ion diffusion dynamics
A graphdiyne-based artificial synapse (GAS), exhibiting intrinsic short-term plasticity, has been proposed to mimic biological signal transmission behavior. The impulse response of the GAS has been reduced to several millivolts with competitive femtowatt-level consumption, exceeding the biological l...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7886898/ https://www.ncbi.nlm.nih.gov/pubmed/33594066 http://dx.doi.org/10.1038/s41467-021-21319-9 |
_version_ | 1783651896651677696 |
---|---|
author | Wei, Huanhuan Shi, Rongchao Sun, Lin Yu, Haiyang Gong, Jiangdong Liu, Chao Xu, Zhipeng Ni, Yao Xu, Jialiang Xu, Wentao |
author_facet | Wei, Huanhuan Shi, Rongchao Sun, Lin Yu, Haiyang Gong, Jiangdong Liu, Chao Xu, Zhipeng Ni, Yao Xu, Jialiang Xu, Wentao |
author_sort | Wei, Huanhuan |
collection | PubMed |
description | A graphdiyne-based artificial synapse (GAS), exhibiting intrinsic short-term plasticity, has been proposed to mimic biological signal transmission behavior. The impulse response of the GAS has been reduced to several millivolts with competitive femtowatt-level consumption, exceeding the biological level by orders of magnitude. Most importantly, the GAS is capable of parallelly processing signals transmitted from multiple pre-neurons and therefore realizing dynamic logic and spatiotemporal rules. It is also found that the GAS is thermally stable (at 353 K) and environmentally stable (in a relative humidity up to 35%). Our artificial efferent nerve, connecting the GAS with artificial muscles, has been demonstrated to complete the information integration of pre-neurons and the information output of motor neurons, which is advantageous for coalescing multiple sensory feedbacks and reacting to events. Our synaptic element has potential applications in bioinspired peripheral nervous systems of soft electronics, neurorobotics, and biohybrid systems of brain–computer interfaces. |
format | Online Article Text |
id | pubmed-7886898 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-78868982021-03-03 Mimicking efferent nerves using a graphdiyne-based artificial synapse with multiple ion diffusion dynamics Wei, Huanhuan Shi, Rongchao Sun, Lin Yu, Haiyang Gong, Jiangdong Liu, Chao Xu, Zhipeng Ni, Yao Xu, Jialiang Xu, Wentao Nat Commun Article A graphdiyne-based artificial synapse (GAS), exhibiting intrinsic short-term plasticity, has been proposed to mimic biological signal transmission behavior. The impulse response of the GAS has been reduced to several millivolts with competitive femtowatt-level consumption, exceeding the biological level by orders of magnitude. Most importantly, the GAS is capable of parallelly processing signals transmitted from multiple pre-neurons and therefore realizing dynamic logic and spatiotemporal rules. It is also found that the GAS is thermally stable (at 353 K) and environmentally stable (in a relative humidity up to 35%). Our artificial efferent nerve, connecting the GAS with artificial muscles, has been demonstrated to complete the information integration of pre-neurons and the information output of motor neurons, which is advantageous for coalescing multiple sensory feedbacks and reacting to events. Our synaptic element has potential applications in bioinspired peripheral nervous systems of soft electronics, neurorobotics, and biohybrid systems of brain–computer interfaces. Nature Publishing Group UK 2021-02-16 /pmc/articles/PMC7886898/ /pubmed/33594066 http://dx.doi.org/10.1038/s41467-021-21319-9 Text en © The Author(s) 2021 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/. |
spellingShingle | Article Wei, Huanhuan Shi, Rongchao Sun, Lin Yu, Haiyang Gong, Jiangdong Liu, Chao Xu, Zhipeng Ni, Yao Xu, Jialiang Xu, Wentao Mimicking efferent nerves using a graphdiyne-based artificial synapse with multiple ion diffusion dynamics |
title | Mimicking efferent nerves using a graphdiyne-based artificial synapse with multiple ion diffusion dynamics |
title_full | Mimicking efferent nerves using a graphdiyne-based artificial synapse with multiple ion diffusion dynamics |
title_fullStr | Mimicking efferent nerves using a graphdiyne-based artificial synapse with multiple ion diffusion dynamics |
title_full_unstemmed | Mimicking efferent nerves using a graphdiyne-based artificial synapse with multiple ion diffusion dynamics |
title_short | Mimicking efferent nerves using a graphdiyne-based artificial synapse with multiple ion diffusion dynamics |
title_sort | mimicking efferent nerves using a graphdiyne-based artificial synapse with multiple ion diffusion dynamics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7886898/ https://www.ncbi.nlm.nih.gov/pubmed/33594066 http://dx.doi.org/10.1038/s41467-021-21319-9 |
work_keys_str_mv | AT weihuanhuan mimickingefferentnervesusingagraphdiynebasedartificialsynapsewithmultipleiondiffusiondynamics AT shirongchao mimickingefferentnervesusingagraphdiynebasedartificialsynapsewithmultipleiondiffusiondynamics AT sunlin mimickingefferentnervesusingagraphdiynebasedartificialsynapsewithmultipleiondiffusiondynamics AT yuhaiyang mimickingefferentnervesusingagraphdiynebasedartificialsynapsewithmultipleiondiffusiondynamics AT gongjiangdong mimickingefferentnervesusingagraphdiynebasedartificialsynapsewithmultipleiondiffusiondynamics AT liuchao mimickingefferentnervesusingagraphdiynebasedartificialsynapsewithmultipleiondiffusiondynamics AT xuzhipeng mimickingefferentnervesusingagraphdiynebasedartificialsynapsewithmultipleiondiffusiondynamics AT niyao mimickingefferentnervesusingagraphdiynebasedartificialsynapsewithmultipleiondiffusiondynamics AT xujialiang mimickingefferentnervesusingagraphdiynebasedartificialsynapsewithmultipleiondiffusiondynamics AT xuwentao mimickingefferentnervesusingagraphdiynebasedartificialsynapsewithmultipleiondiffusiondynamics |