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Towards spike-based machine intelligence with neuromorphic computing

Author

Listed:
  • Kaushik Roy

    (Purdue University)

  • Akhilesh Jaiswal

    (Purdue University)

  • Priyadarshini Panda

    (Purdue University)

Abstract

Guided by brain-like ‘spiking’ computational frameworks, neuromorphic computing—brain-inspired computing for machine intelligence—promises to realize artificial intelligence while reducing the energy requirements of computing platforms. This interdisciplinary field began with the implementation of silicon circuits for biological neural routines, but has evolved to encompass the hardware implementation of algorithms with spike-based encoding and event-driven representations. Here we provide an overview of the developments in neuromorphic computing for both algorithms and hardware and highlight the fundamentals of learning and hardware frameworks. We discuss the main challenges and the future prospects of neuromorphic computing, with emphasis on algorithm–hardware codesign.

Suggested Citation

  • Kaushik Roy & Akhilesh Jaiswal & Priyadarshini Panda, 2019. "Towards spike-based machine intelligence with neuromorphic computing," Nature, Nature, vol. 575(7784), pages 607-617, November.
    • Handle: RePEc:nat:nature:v:575:y:2019:i:7784:d:10.1038_s41586-019-1677-2
    DOI: 10.1038/s41586-019-1677-2
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    Cited by:

    1. Zhou, Qian & Qi, Saibing & Ren, Cong, 2021. "Gene essentiality prediction based on chaos game representation and spiking neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    2. 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.
    3. Rong Zhao & Zheyu Yang & Hao Zheng & Yujie Wu & Faqiang Liu & Zhenzhi Wu & Lukai Li & Feng Chen & Seng Song & Jun Zhu & Wenli Zhang & Haoyu Huang & Mingkun Xu & Kaifeng Sheng & Qianbo Yin & Jing Pei &, 2022. "A framework for the general design and computation of hybrid neural networks," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    4. Filippo Moro & Emmanuel Hardy & Bruno Fain & Thomas Dalgaty & Paul Clémençon & Alessio Prà & Eduardo Esmanhotto & Niccolò Castellani & François Blard & François Gardien & Thomas Mesquida & François Ru, 2022. "Neuromorphic object localization using resistive memories and ultrasonic transducers," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    5. Ruomin Zhu & Sam Lilak & Alon Loeffler & Joseph Lizier & Adam Stieg & James Gimzewski & Zdenka Kuncic, 2023. "Online dynamical learning and sequence memory with neuromorphic nanowire networks," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    6. Dong Gue Roe & Dong Hae Ho & Yoon Young Choi & Young Jin Choi & Seongchan Kim & Sae Byeok Jo & Moon Sung Kang & Jong-Hyun Ahn & Jeong Ho Cho, 2023. "Humanlike spontaneous motion coordination of robotic fingers through spatial multi-input spike signal multiplexing," Nature Communications, Nature, vol. 14(1), pages 1-7, December.
    7. Francesco Barchi & Luca Zanatta & Emanuele Parisi & Alessio Burrello & Davide Brunelli & Andrea Bartolini & Andrea Acquaviva, 2021. "Spiking Neural Network-Based Near-Sensor Computing for Damage Detection in Structural Health Monitoring," Future Internet, MDPI, vol. 13(8), pages 1-22, August.
    8. Choi, Woo Sik & Jang, Jun Tae & Kim, Donguk & Yang, Tae Jun & Kim, Changwook & Kim, Hyungjin & Kim, Dae Hwan, 2022. "Influence of Al2O3 layer on InGaZnO memristor crossbar array for neuromorphic applications," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    9. Pei-Yu Huang & Bi-Yi Jiang & Hong-Ji Chen & Jia-Yi Xu & Kang Wang & Cheng-Yi Zhu & Xin-Yan Hu & Dong Li & Liang Zhen & Fei-Chi Zhou & Jing-Kai Qin & Cheng-Yan Xu, 2023. "Neuro-inspired optical sensor array for high-accuracy static image recognition and dynamic trace extraction," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    10. Takuya Isomura & Kiyoshi Kotani & Yasuhiko Jimbo & Karl J. Friston, 2023. "Experimental validation of the free-energy principle with in vitro neural networks," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    11. Wang, Huan & Li, Yan-Fu, 2023. "Bioinspired membrane learnable spiking neural network for autonomous vehicle sensors fault diagnosis under open environments," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    12. Jiaqi Liu & Jiangdong Gong & Huanhuan Wei & Yameng Li & Haixia Wu & Chengpeng Jiang & Yuelong Li & Wentao Xu, 2022. "A bioinspired flexible neuromuscular system based thermal-annealing-free perovskite with passivation," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    13. Chenhao Wang & Xinyi Xu & Xiaodong Pi & Mark D. Butala & Wen Huang & Lei Yin & Wenbing Peng & Munir Ali & Srikrishna Chanakya Bodepudi & Xvsheng Qiao & Yang Xu & Wei Sun & Deren Yang, 2022. "Neuromorphic device based on silicon nanosheets," Nature Communications, Nature, vol. 13(1), pages 1-10, December.

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