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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1002/adma.202203684
|2 doi
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|a pubmed24n1141.xml
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|a (NLM)35735048
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Li, Xinyi
|e verfasserin
|4 aut
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| 245 |
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|a A Memristors-Based Dendritic Neuron for High-Efficiency Spatial-Temporal Information Processing
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|c 2023
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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|a Date Revised 14.09.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © 2022 Wiley-VCH GmbH.
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|a Diverse microscopic ionic dynamics help mediate the ability of a biological neural network to handle complex tasks with low energy consumption. Thus, rich internal ionic dynamics in memristors based on transition metal oxide are expected to provide a unique and useful platform for implementing energy-efficient neuromorphic computing. To this end, a titanium oxide (TiOx )-based interface-type dynamic memristor and an niobium oxide (NbOx )-based Mott memristor are integrated as an artificial dendrite and spike-firing soma, respectively, to construct a dendritic neuron unit for realizing high-efficiency spatial-temporal information processing. Further, a dendritic neural network is hardware-implemented for spatial-temporal information processing to highlight the computational advantages achieved by incorporating dendritic functions in the network. Human motion recognition is demonstrated using the Nanyang Technological University-Red Green Blue (NTU-RGB) dataset as a benchmark spatial-temporal task; it shows a nearly 20% improvement in accuracy for the memristors-based hardware incorporating dendrites and a 1000× advantage in power efficiency compared to that of the graphics processing unit (GPU). The dendritic neuron developed in this study can be considered a critical building block for implementing more bio-plausible neural networks that can manage complex spatial-temporal tasks with high efficiency
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|a Journal Article
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|a biological neural networks
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|a dendritic neuron units
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|a ionic dynamics
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| 650 |
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|a neuromorphic computing
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| 700 |
1 |
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|a Zhong, Yanan
|e verfasserin
|4 aut
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|a Chen, Hang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Tang, Jianshi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zheng, Xiaojian
|e verfasserin
|4 aut
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| 700 |
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|a Sun, Wen
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Yang
|e verfasserin
|4 aut
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| 700 |
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|a Wu, Dong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Gao, Bin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Hu, Xiaolin
|e verfasserin
|4 aut
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| 700 |
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|a Qian, He
|e verfasserin
|4 aut
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| 700 |
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|a Wu, Huaqiang
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t Advanced materials (Deerfield Beach, Fla.)
|d 1998
|g 35(2023), 37 vom: 01. Sept., Seite e2203684
|w (DE-627)NLM098206397
|x 1521-4095
|7 nnns
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| 773 |
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|g volume:35
|g year:2023
|g number:37
|g day:01
|g month:09
|g pages:e2203684
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|u http://dx.doi.org/10.1002/adma.202203684
|3 Volltext
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