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Mimicking efferent nerves using a graphdiyne-based artificial synapse with multiple ion diffusion dynamics.


ABSTRACT: 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.

SUBMITTER: Wei H 

PROVIDER: S-EPMC7886898 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Mimicking efferent nerves using a graphdiyne-based artificial synapse with multiple ion diffusion dynamics.

Wei Huanhuan H   Shi Rongchao R   Sun Lin L   Yu Haiyang H   Gong Jiangdong J   Liu Chao C   Xu Zhipeng Z   Ni Yao Y   Xu Jialiang J   Xu Wentao W  

Nature communications 20210216 1


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.  ...[more]

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