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Synaptic plasticity in self-powered artificial striate cortex for binocular orientation selectivity

Author

Listed:
  • Yanyun Ren

    (Shenzhen University
    Shenzhen University)

  • Xiaobo Bu

    (Shenzhen University)

  • Ming Wang

    (Shenzhen University)

  • Yue Gong

    (Shenzhen University)

  • Junjie Wang

    (Shenzhen University)

  • Yuyang Yang

    (Shenzhen University)

  • Guijun Li

    (Shenzhen University)

  • Meng Zhang

    (Shenzhen University)

  • Ye Zhou

    (Shenzhen University)

  • Su-Ting Han

    (Shenzhen University)

Abstract

Get in-depth understanding of each part of visual pathway yields insights to conquer the challenges that classic computer vision is facing. Here, we first report the bioinspired striate cortex with binocular and orientation selective receptive field based on the crossbar array of self-powered memristors which is solution-processed monolithic all-perovskite system with each cross-point containing one CsFAPbI3 solar cell directly stacking on the CsPbBr2I memristor. The plasticity of self-powered memristor can be modulated by optical stimuli following triplet-STDP rules. Furthermore, plasticity of 3 × 3 flexible crossbar array of self-powered memristors has been successfully modulated based on generalized BCM learning rule for optical-encoded pattern recognition. Finally, we implemented artificial striate cortex with binocularity and orientation selectivity based on two simulated 9 × 9 self-powered memristors networks. The emulation of striate cortex with binocular and orientation selectivity will facilitate the brisk edge and corner detection for machine vision in the future applications.

Suggested Citation

  • Yanyun Ren & Xiaobo Bu & Ming Wang & Yue Gong & Junjie Wang & Yuyang Yang & Guijun Li & Meng Zhang & Ye Zhou & Su-Ting Han, 2022. "Synaptic plasticity in self-powered artificial striate cortex for binocular orientation selectivity," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33393-8
    DOI: 10.1038/s41467-022-33393-8
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    References listed on IDEAS

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    1. Jonathan W. Pillow & Jonathon Shlens & Liam Paninski & Alexander Sher & Alan M. Litke & E. J. Chichilnisky & Eero P. Simoncelli, 2008. "Spatio-temporal correlations and visual signalling in a complete neuronal population," Nature, Nature, vol. 454(7207), pages 995-999, August.
    2. Cynthia D. Rittenhouse & Harel Z. Shouval & Michael A. Paradiso & Mark F. Bear, 1999. "Monocular deprivation induces homosynaptic long-term depression in visual cortex," Nature, Nature, vol. 397(6717), pages 347-350, January.
    3. M. Prezioso & M. R. Mahmoodi & F. Merrikh Bayat & H. Nili & H. Kim & A. Vincent & D. B. Strukov, 2018. "Spike-timing-dependent plasticity learning of coincidence detection with passively integrated memristive circuits," Nature Communications, Nature, vol. 9(1), pages 1-8, December.
    4. Robert C. Froemke & Yang Dan, 2002. "Spike-timing-dependent synaptic modification induced by natural spike trains," Nature, Nature, vol. 416(6879), pages 433-438, March.
    5. Pierre Yger & Kenneth D Harris, 2013. "The Convallis Rule for Unsupervised Learning in Cortical Networks," PLOS Computational Biology, Public Library of Science, vol. 9(10), pages 1-16, October.
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