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Spiking neurons with spatiotemporal dynamics and gain modulation for monolithically integrated memristive neural networks

Nature communications, 2020-07, Vol.11 (1), p.3399-3399, Article 3399 [Peer Reviewed Journal]

The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2020 ;ISSN: 2041-1723 ;EISSN: 2041-1723 ;DOI: 10.1038/s41467-020-17215-3 ;PMID: 32636385

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  • Title:
    Spiking neurons with spatiotemporal dynamics and gain modulation for monolithically integrated memristive neural networks
  • Author: Duan, Qingxi ; Jing, Zhaokun ; Zou, Xiaolong ; Wang, Yanghao ; Yang, Ke ; Zhang, Teng ; Wu, Si ; Huang, Ru ; Yang, Yuchao
  • Subjects: Biomimetics ; Construction ; Data processing ; Distance learning ; Energy efficiency ; Firing pattern ; Information processing ; Internet ; Machine learning ; Memristors ; Modulation ; Neural networks ; Neurons ; Pattern recognition ; Spiking ; Synapses
  • Is Part Of: Nature communications, 2020-07, Vol.11 (1), p.3399-3399, Article 3399
  • Description: As a key building block of biological cortex, neurons are powerful information processing units and can achieve highly complex nonlinear computations even in individual cells. Hardware implementation of artificial neurons with similar capability is of great significance for the construction of intelligent, neuromorphic systems. Here, we demonstrate an artificial neuron based on NbO volatile memristor that not only realizes traditional all-or-nothing, threshold-driven spiking and spatiotemporal integration, but also enables dynamic logic including XOR function that is not linearly separable and multiplicative gain modulation among different dendritic inputs, therefore surpassing neuronal functions described by a simple point neuron model. A monolithically integrated 4 × 4 fully memristive neural network consisting of volatile NbO memristor based neurons and nonvolatile TaO memristor based synapses in a single crossbar array is experimentally demonstrated, showing capability in pattern recognition through online learning using a simplified δ-rule and coincidence detection, which paves the way for bio-inspired intelligent systems.
  • Publisher: England: Nature Publishing Group
  • Language: English
  • Identifier: ISSN: 2041-1723
    EISSN: 2041-1723
    DOI: 10.1038/s41467-020-17215-3
    PMID: 32636385
  • Source: PubMed Central
    ProQuest Central
    DOAJ Directory of Open Access Journals
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