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A sensory memory processing system with multi-wavelength synaptic-polychromatic light emission for multi-modal information recognition

Author

Listed:
  • Liuting Shan

    (Fuzhou University
    Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China)

  • Qizhen Chen

    (Fuzhou University
    Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China
    Xiamen University of Technology)

  • Rengjian Yu

    (Fuzhou University
    Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China)

  • Changsong Gao

    (Fuzhou University
    Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China)

  • Lujian Liu

    (Fuzhou University
    Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China)

  • Tailiang Guo

    (Fuzhou University
    Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China)

  • Huipeng Chen

    (Fuzhou University
    Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China)

Abstract

Realizing multi-modal information recognition tasks which can process external information efficiently and comprehensively is an urgent requirement in the field of artificial intelligence. However, it remains a challenge to achieve simple structure and high-performance multi-modal recognition demonstrations owing to the complex execution module and separation of memory processing based on the traditional complementary metal oxide semiconductor (CMOS) architecture. Here, we propose an efficient sensory memory processing system (SMPS), which can process sensory information and generate synapse-like and multi-wavelength light-emitting output, realizing diversified utilization of light in information processing and multi-modal information recognition. The SMPS exhibits strong robustness in information encoding/transmission and the capability of visible information display through the multi-level color responses, which can implement the multi-level pain warning process of organisms intuitively. Furthermore, different from the conventional multi-modal information processing system that requires independent and complex circuit modules, the proposed SMPS with unique optical multi-information parallel output can realize efficient multi-modal information recognition of dynamic step frequency and spatial positioning simultaneously with the accuracy of 99.5% and 98.2%, respectively. Therefore, the SMPS proposed in this work with simple component, flexible operation, strong robustness, and highly efficiency is promising for future sensory-neuromorphic photonic systems and interactive artificial intelligence.

Suggested Citation

  • Liuting Shan & Qizhen Chen & Rengjian Yu & Changsong Gao & Lujian Liu & Tailiang Guo & Huipeng Chen, 2023. "A sensory memory processing system with multi-wavelength synaptic-polychromatic light emission for multi-modal information recognition," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-38396-7
    DOI: 10.1038/s41467-023-38396-7
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    as
    1. Enlong Li & Changsong Gao & Rengjian Yu & Xiumei Wang & Lihua He & Yuanyuan Hu & Huajie Chen & Huipeng Chen & Tailiang Guo, 2022. "MXene based saturation organic vertical photoelectric transistors with low subthreshold swing," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    2. J. Feldmann & N. Youngblood & M. Karpov & H. Gehring & X. Li & M. Stappers & M. Gallo & X. Fu & A. Lukashchuk & A. S. Raja & J. Liu & C. D. Wright & A. Sebastian & T. J. Kippenberg & W. H. P. Pernice , 2021. "Publisher Correction: Parallel convolutional processing using an integrated photonic tensor core," Nature, Nature, vol. 591(7849), pages 13-13, March.
    3. Yaqian Liu & Di Liu & Changsong Gao & Xianghong Zhang & Rengjian Yu & Xiumei Wang & Enlong Li & Yuanyuan Hu & Tailiang Guo & Huipeng Chen, 2022. "Self-powered high-sensitivity all-in-one vertical tribo-transistor device for multi-sensing-memory-computing," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    4. Sourav Dutta & Georgios Detorakis & Abhishek Khanna & Benjamin Grisafe & Emre Neftci & Suman Datta, 2022. "Neural sampling machine with stochastic synapse allows brain-like learning and inference," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    5. Zhongda Sun & Minglu Zhu & Xuechuan Shan & Chengkuo Lee, 2022. "Augmented tactile-perception and haptic-feedback rings as human-machine interfaces aiming for immersive interactions," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    6. Hongwei Tan & Yifan Zhou & Quanzheng Tao & Johanna Rosen & Sebastiaan van Dijken, 2021. "Bioinspired multisensory neural network with crossmodal integration and recognition," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    7. Xingyuan Xu & Mengxi Tan & Bill Corcoran & Jiayang Wu & Andreas Boes & Thach G. Nguyen & Sai T. Chu & Brent E. Little & Damien G. Hicks & Roberto Morandotti & Arnan Mitchell & David J. Moss, 2021. "11 TOPS photonic convolutional accelerator for optical neural networks," Nature, Nature, vol. 589(7840), pages 44-51, January.
    8. Jinhui Zhang & Haimin Yao & Jiaying Mo & Songyue Chen & Yu Xie & Shenglin Ma & Rui Chen & Tao Luo & Weisong Ling & Lifeng Qin & Zuankai Wang & Wei Zhou, 2022. "Finger-inspired rigid-soft hybrid tactile sensor with superior sensitivity at high frequency," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    9. J. Feldmann & N. Youngblood & M. Karpov & H. Gehring & X. Li & M. Stappers & M. Gallo & X. Fu & A. Lukashchuk & A. S. Raja & J. Liu & C. D. Wright & A. Sebastian & T. J. Kippenberg & W. H. P. Pernice , 2021. "Parallel convolutional processing using an integrated photonic tensor core," Nature, Nature, vol. 589(7840), pages 52-58, January.
    10. Rengjian Yu & Lihua He & Changsong Gao & Xianghong Zhang & Enlong Li & Tailiang Guo & Wenwu Li & Huipeng Chen, 2022. "Programmable ferroelectric bionic vision hardware with selective attention for high-precision image classification," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    11. J. Feldmann & N. Youngblood & C. D. Wright & H. Bhaskaran & W. H. P. Pernice, 2019. "All-optical spiking neurosynaptic networks with self-learning capabilities," Nature, Nature, vol. 569(7755), pages 208-214, May.
    12. Jie Wang & Changsheng Wu & Yejing Dai & Zhihao Zhao & Aurelia Wang & Tiejun Zhang & Zhong Lin Wang, 2017. "Achieving ultrahigh triboelectric charge density for efficient energy harvesting," Nature Communications, Nature, vol. 8(1), pages 1-8, December.
    13. Binghao Wang & Anish Thukral & Zhaoqian Xie & Limei Liu & Xinan Zhang & Wei Huang & Xinge Yu & Cunjiang Yu & Tobin J. Marks & Antonio Facchetti, 2020. "Flexible and stretchable metal oxide nanofiber networks for multimodal and monolithically integrated wearable electronics," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
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    1. Zhenjia Chen & Zhenyuan Lin & Ji Yang & Cong Chen & Di Liu & Liuting Shan & Yuanyuan Hu & Tailiang Guo & Huipeng Chen, 2024. "Cross-layer transmission realized by light-emitting memristor for constructing ultra-deep neural network with transfer learning ability," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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