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241016s2024 xx |||||o 00| ||eng c |
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|a 10.1002/adma.202409040
|2 doi
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|a eng
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| 100 |
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|a Li, Hanxi
|e verfasserin
|4 aut
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|a Boolean Computation in Single-Transistor Neuron
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|c 2024
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|a Text
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|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Completed 05.12.2024
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|a Date Revised 05.12.2024
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a © 2024 Wiley‐VCH GmbH.
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|a Brain neurons exhibit far more sophisticated and powerful information-processing capabilities than the simple integrators commonly modeled in neuromorphic computing. A biological neuron can in fact efficiently perform Boolean algebra, including linear nonseparable operations. Traditional logic circuits require more than a dozen transistors combined as NOT, AND, and OR gates to implement XOR. Lacking biological competency, artificial neural networks require multilayered solutions to exercise XOR operation. Here, it is shown that a single-transistor neuron, harnessing the intrinsic ambipolarity of graphene and ionic filamentary dynamics, can enable in situ reconfigurable multiple Boolean operations from linear separable to linear nonseparable in an ultra-compact design. By leveraging the spatiotemporal integration of inputs, bio-realistic spiking-dependent Boolean computation is fully realized, rivaling the efficiency of a human brain. Furthermore, a soft-XOR-based neural network via algorithm-hardware co-design, showcasing substantial performance improvement, is demonstrated. These results demonstrate how the artificial neuron, in the ultra-compact form of a single transistor, may function as a powerful platform for Boolean operations. These findings are anticipated to be a starting point for implementing more sophisticated computations at the individual transistor neuron level, leading to super-scalable neural networks for resource-efficient brain-inspired information processing
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|a Journal Article
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|a artificial intelligence
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|a boolean algebra
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|a neural network
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|a neuromorphic computing
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|a neuron model
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|a reconfigurable logic
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|a Graphite
|2 NLM
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|a 7782-42-5
|2 NLM
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| 700 |
1 |
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|a Hu, Jiayang
|e verfasserin
|4 aut
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| 700 |
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|a Zhang, Yishu
|e verfasserin
|4 aut
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|a Chen, Anzhe
|e verfasserin
|4 aut
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1 |
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|a Lin, Li
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chen, Ge
|e verfasserin
|4 aut
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| 700 |
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|a Chen, Yance
|e verfasserin
|4 aut
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|a Chai, Jian
|e verfasserin
|4 aut
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| 700 |
1 |
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|a He, Qian
|e verfasserin
|4 aut
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|a Wang, Hailiang
|e verfasserin
|4 aut
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|a Huang, Shiman
|e verfasserin
|4 aut
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1 |
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|a Zhou, Jiachao
|e verfasserin
|4 aut
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| 700 |
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|a Xu, Yang
|e verfasserin
|4 aut
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| 700 |
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|a Yu, Bin
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Advanced materials (Deerfield Beach, Fla.)
|d 1998
|g 36(2024), 49 vom: 16. Dez., Seite e2409040
|w (DE-627)NLM098206397
|x 1521-4095
|7 nnns
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|g volume:36
|g year:2024
|g number:49
|g day:16
|g month:12
|g pages:e2409040
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|u http://dx.doi.org/10.1002/adma.202409040
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