IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-28487-2.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-28487-2
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-022-28487-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Valerio Mante & David Sussillo & Krishna V. Shenoy & William T. Newsome, 2013. "Context-dependent computation by recurrent dynamics in prefrontal cortex," Nature, Nature, vol. 503(7474), pages 78-84, November.
    2. Irem Boybat & Manuel Le Gallo & S. R. Nandakumar & Timoleon Moraitis & Thomas Parnell & Tomas Tuma & Bipin Rajendran & Yusuf Leblebici & Abu Sebastian & Evangelos Eleftheriou, 2018. "Neuromorphic computing with multi-memristive synapses," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
    3. Rohit Abraham John & Naveen Tiwari & Muhammad Iszaki Bin Patdillah & Mohit Rameshchandra Kulkarni & Nidhi Tiwari & Joydeep Basu & Sumon Kumar Bose & Ankit & Chan Jun Yu & Amoolya Nirmal & Sujaya Kumar, 2020. "Self healable neuromorphic memtransistor elements for decentralized sensory signal processing in robotics," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
    4. Gina G. Turrigiano & Kenneth R. Leslie & Niraj S. Desai & Lana C. Rutherford & Sacha B. Nelson, 1998. "Activity-dependent scaling of quantal amplitude in neocortical neurons," Nature, Nature, vol. 391(6670), pages 892-896, February.
    5. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
    6. Manuel Schaffner & Jakob A. Faber & Lucas Pianegonda & Patrick A. Rühs & Fergal Coulter & André R. Studart, 2018. "3D printing of robotic soft actuators with programmable bioinspired architectures," Nature Communications, Nature, vol. 9(1), pages 1-9, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Yue Yang & Fangduo Zhu & Xumeng Zhang & Pei Chen & Yongzhou Wang & Jiaxue Zhu & Yanting Ding & Lingli Cheng & Chao Li & Hao Jiang & Zhongrui Wang & Peng Lin & Tuo Shi & Ming Wang & Qi Liu & Ningsheng , 2024. "Firing feature-driven neural circuits with scalable memristive neurons for robotic obstacle avoidance," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    3. Imke Krauhausen & Sophie Griggs & Iain McCulloch & Jaap M. J. Toonder & Paschalis Gkoupidenis & Yoeri Burgt, 2024. "Bio-inspired multimodal learning with organic neuromorphic electronics for behavioral conditioning in robotics," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    4. Man Yao & Ole Richter & Guangshe Zhao & Ning Qiao & Yannan Xing & Dingheng Wang & Tianxiang Hu & Wei Fang & Tugba Demirci & Michele Marchi & Lei Deng & Tianyi Yan & Carsten Nielsen & Sadique Sheik & C, 2024. "Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    5. Shengbo Wang & Shuo Gao & Chenyu Tang & Edoardo Occhipinti & Cong Li & Shurui Wang & Jiaqi Wang & Hubin Zhao & Guohua Hu & Arokia Nathan & Ravinder Dahiya & Luigi Giuseppe Occhipinti, 2024. "Memristor-based adaptive neuromorphic perception in unstructured environments," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    6. Elisa Donati & Giacomo Valle, 2024. "Neuromorphic hardware for somatosensory neuroprostheses," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    7. Gabriel Béna & Dan F. M. Goodman, 2025. "Dynamics of specialization in neural modules under resource constraints," Nature Communications, Nature, vol. 16(1), pages 1-11, December.
    8. Qian Li & Ting Tan & Benlong Wang & Zhimiao Yan, 2024. "Avian-inspired embodied perception in biohybrid flapping-wing robotics," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    9. Beckert, Bernd & Kroll, Henning, 2024. "Definition of the research and innovation field of "Artificial Intelligence" and approaches to determining quality," Discussion Papers "Innovation Systems and Policy Analysis" 88, Fraunhofer Institute for Systems and Innovation Research (ISI).
    10. Romain Beaubois & Jérémy Cheslet & Tomoya Duenki & Giuseppe De Venuto & Marta Carè & Farad Khoyratee & Michela Chiappalone & Pascal Branchereau & Yoshiho Ikeuchi & Timothée Levi, 2024. "BiœmuS: A new tool for neurological disorders studies through real-time emulation and hybridization using biomimetic Spiking Neural Network," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    11. 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.
    12. Shijie Lin & Guangze Zheng & Ziwei Wang & Ruihua Han & Wanli Xing & Zeqing Zhang & Yifan Peng & Jia Pan, 2024. "Embodied neuromorphic synergy for lighting-robust machine vision to see in extreme bright," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    13. Matteo Cartiglia & Filippo Costa & Shyam Narayanan & Cat-Vu H. Bui & Hasan Ulusan & Nicoletta Risi & Germain Haessig & Andreas Hierlemann & Fernando Cardes & Giacomo Indiveri, 2024. "A 4096 channel event-based multielectrode array with asynchronous outputs compatible with neuromorphic processors," Nature Communications, Nature, vol. 15(1), pages 1-11, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiaoyue Li & John M. Mulvey, 2023. "Optimal Portfolio Execution in a Regime-switching Market with Non-linear Impact Costs: Combining Dynamic Program and Neural Network," Papers 2306.08809, arXiv.org.
    2. Nathan Companez & Aldeida Aleti, 2016. "Can Monte-Carlo Tree Search learn to sacrifice?," Journal of Heuristics, Springer, vol. 22(6), pages 783-813, December.
    3. Zhewei Zhang & Youngjin Yoo & Kalle Lyytinen & Aron Lindberg, 2021. "The Unknowability of Autonomous Tools and the Liminal Experience of Their Use," Information Systems Research, INFORMS, vol. 32(4), pages 1192-1213, December.
    4. Yuhong Wang & Lei Chen & Hong Zhou & Xu Zhou & Zongsheng Zheng & Qi Zeng & Li Jiang & Liang Lu, 2021. "Flexible Transmission Network Expansion Planning Based on DQN Algorithm," Energies, MDPI, vol. 14(7), pages 1-21, April.
    5. Gokhale, Gargya & Claessens, Bert & Develder, Chris, 2022. "Physics informed neural networks for control oriented thermal modeling of buildings," Applied Energy, Elsevier, vol. 314(C).
    6. Li Xia, 2020. "Risk‐Sensitive Markov Decision Processes with Combined Metrics of Mean and Variance," Production and Operations Management, Production and Operations Management Society, vol. 29(12), pages 2808-2827, December.
    7. Yan Li & Mitchell Swerdloff & Tianyu She & Asiyah Rahman & Naveen Sharma & Reema Shah & Michael Castellano & Daniel Mogel & Jason Wu & Asim Ahmed & James San Miguel & Jared Cohn & Nikesh Shah & Raddy , 2023. "Robust odor identification in novel olfactory environments in mice," Nature Communications, Nature, vol. 14(1), pages 1-29, December.
    8. Neha Soni & Enakshi Khular Sharma & Narotam Singh & Amita Kapoor, 2019. "Impact of Artificial Intelligence on Businesses: from Research, Innovation, Market Deployment to Future Shifts in Business Models," Papers 1905.02092, arXiv.org.
    9. Yin, Linfei & He, Xiaoyu, 2023. "Artificial emotional deep Q learning for real-time smart voltage control of cyber-physical social power systems," Energy, Elsevier, vol. 273(C).
    10. Taejong Joo & Hyunyoung Jun & Dongmin Shin, 2022. "Task Allocation in Human–Machine Manufacturing Systems Using Deep Reinforcement Learning," Sustainability, MDPI, vol. 14(4), pages 1-18, February.
    11. Burka, Dávid & Puppe, Clemens & Szepesváry, László & Tasnádi, Attila, 2022. "Voting: A machine learning approach," European Journal of Operational Research, Elsevier, vol. 299(3), pages 1003-1017.
    12. Oleh Lukianykhin & Tetiana Bogodorova, 2021. "Voltage Control-Based Ancillary Service Using Deep Reinforcement Learning," Energies, MDPI, vol. 14(8), pages 1-22, April.
    13. De Bruyn, Arnaud & Viswanathan, Vijay & Beh, Yean Shan & Brock, Jürgen Kai-Uwe & von Wangenheim, Florian, 2020. "Artificial Intelligence and Marketing: Pitfalls and Opportunities," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 91-105.
    14. Matteo Saponati & Martin Vinck, 2023. "Sequence anticipation and spike-timing-dependent plasticity emerge from a predictive learning rule," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    15. Zhimian Chen & Yizeng Wang & Hao Hu & Zhipeng Zhang & Chengwei Zhang & Shukun Zhou, 2024. "Investigating Autonomous Vehicle Driving Strategies in Highway Ramp Merging Zones," Mathematics, MDPI, vol. 12(23), pages 1-22, December.
    16. Keerthana Sivamayil & Elakkiya Rajasekar & Belqasem Aljafari & Srete Nikolovski & Subramaniyaswamy Vairavasundaram & Indragandhi Vairavasundaram, 2023. "A Systematic Study on Reinforcement Learning Based Applications," Energies, MDPI, vol. 16(3), pages 1-23, February.
    17. Jan Weber & Anne-Kristin Solbakk & Alejandro O. Blenkmann & Anais Llorens & Ingrid Funderud & Sabine Leske & Pål Gunnar Larsson & Jugoslav Ivanovic & Robert T. Knight & Tor Endestad & Randolph F. Helf, 2024. "Ramping dynamics and theta oscillations reflect dissociable signatures during rule-guided human behavior," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    18. Pierre O. Boucher & Tian Wang & Laura Carceroni & Gary Kane & Krishna V. Shenoy & Chandramouli Chandrasekaran, 2023. "Initial conditions combine with sensory evidence to induce decision-related dynamics in premotor cortex," Nature Communications, Nature, vol. 14(1), pages 1-28, December.
    19. Niranjan Chakravarthy & Shivkumar Sabesan & Kostas Tsakalis & Leon Iasemidis, 2009. "Controlling epileptic seizures in a neural mass model," Journal of Combinatorial Optimization, Springer, vol. 17(1), pages 98-116, January.
    20. Rishi Rajalingham & Aída Piccato & Mehrdad Jazayeri, 2022. "Recurrent neural networks with explicit representation of dynamic latent variables can mimic behavioral patterns in a physical inference task," Nature Communications, Nature, vol. 13(1), pages 1-15, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28487-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.