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Embodied neuromorphic intelligence

Author

Listed:
  • Chiara Bartolozzi

    (Istituto Italiano di Tecnologia)

  • Giacomo Indiveri

    (Institute of Neuroinformatics, University of Zurich and ETH Zurich)

  • Elisa Donati

    (Institute of Neuroinformatics, University of Zurich and ETH Zurich)

Abstract

The design of robots that interact autonomously with the environment and exhibit complex behaviours is an open challenge that can benefit from understanding what makes living beings fit to act in the world. Neuromorphic engineering studies neural computational principles to develop technologies that can provide a computing substrate for building compact and low-power processing systems. We discuss why endowing robots with neuromorphic technologies – from perception to motor control – represents a promising approach for the creation of robots which can seamlessly integrate in society. We present initial attempts in this direction, highlight open challenges, and propose actions required to overcome current limitations.

Suggested Citation

  • Chiara Bartolozzi & Giacomo Indiveri & Elisa Donati, 2022. "Embodied neuromorphic intelligence," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28487-2
    DOI: 10.1038/s41467-022-28487-2
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    References listed on IDEAS

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    Cited by:

    1. Ningning Bai & Yiheng Xue & Shuiqing Chen & Lin Shi & Junli Shi & Yuan Zhang & Xingyu Hou & Yu Cheng & Kaixi Huang & Weidong Wang & Jin Zhang & Yuan Liu & Chuan Fei Guo, 2023. "A robotic sensory system with high spatiotemporal resolution for texture recognition," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    2. Elisa Donati & Giacomo Valle, 2024. "Neuromorphic hardware for somatosensory neuroprostheses," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    3. Simone D’Agostino & Filippo Moro & Tristan Torchet & Yiğit Demirağ & Laurent Grenouillet & Niccolò Castellani & Giacomo Indiveri & Elisa Vianello & Melika Payvand, 2024. "DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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