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Cited by 526Advanced Memory and Neural ComputingAdvanced Materials and MechanicsNeural Networks and Reservoir Computing

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The rise of intelligent matter

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Kaspar, C., Ravoo, B. J., van der Wiel, W. G., Wegner, S. V., Pernice, W. H. P.·2021·Nature
DOI 10.1038/s41586-021-03453-y
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Abstract

Artificial intelligence (AI) is accelerating the development of unconventional computing paradigms inspired by the abilities and energy efficiency of the brain. The human brain excels especially in computationally intensive cognitive tasks, such as pattern recognition and classification. A long-term goal is de-centralized neuromorphic computing, relying on a network of distributed cores to mimic the massive parallelism of the brain, thus rigorously following a nature-inspired approach for information processing. Through the gradual transformation of interconnected computing blocks into continuous computing tissue, the development of advanced forms of matter exhibiting basic features of intelligence can be envisioned, able to learn and process information in a delocalized manner. Such intelligent matter would interact with the environment by receiving and responding to external stimuli, while internally adapting its structure to enable the distribution and storage (as memory) of information. We review progress towards implementations of intelligent matter using molecular systems, soft materials or solid-state materials, with respect to applications in soft robotics, the development of adaptive artificial skins and distributed neuromorphic computing.

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Research works printed but not linked: 105

  1. Sternberg, R. J. Handbook of Intelligence (Cambridge Univ. Press, 2000). · DOI
  2. Sternberg, R. J. Theories of intelligence. In APA Handbook of Giftedness and Talent (eds Pfeiffer, S. I. et al.) 145–161 (American Psychological Association, 2018). · DOI
  3. Legg, S. & Hutter, M. Universal intelligence: a definition of machine intelligence. Minds Mach. 17, 391–444 (2007). · DOI · Paper page
  4. Lane, N. D., Bhattacharya, S., Mathur, A., Forlivesi, C. & Kawsar, F. Squeezing deep learning into mobile and embedded devices. IEEE Pervasive Comput. 16, 82–88 (2017). · DOI
  5. Hecht, J. Lidar for self-driving cars. Opt. Photonics News 29, 26–33 (2018). · DOI
  6. Fernández-Caramés, T. M. & Fraga-Lamas, P. Towards the internet-of-smart-clothing: a review on IoT wearables and garments for creating intelligent connected E-textiles. Electronics 7, 405 (2018). · DOI
  7. Majidi, C. Soft robotics: a perspective—current trends and prospects for the future. Soft Robot. 1, 5–11 (2014). · DOI
  8. Hamdioui, S. et al. Applications of computation-in-memory architectures based on memristive devices. In Proc. 2019 Design, Automation and Test in Europe Conference and Exhibition 486–491, https://doi.org/10.23919/DATE.2019.8715020 (2019). · DOI
  9. Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nat. Nanotechnol. 15, 529–544 (2020). · DOI · Paper page
  10. Wegst, U. G. K., Bai, H., Saiz, E., Tomsia, A. P. & Ritchie, R. O. Bioinspired structural materials. Nat. Mater. 14, 23–36 (2015). · DOI
  11. Isaacoff, B. P. & Brown, K. A. Progress in top-down control of bottom-up assembly. Nano Lett. 17, 6508–6510 (2017). · DOI
  12. McEvoy, M. A. & Correll, N. Materials that couple sensing, actuation, computation, and communication. Science 347, 1261689 (2015). · DOI
  13. Walther, A. Viewpoint: from responsive to adaptive and interactive materials and materials systems: a roadmap. Adv. Mater. 32, 1905111 (2020). · DOI
  14. Merindol, R. & Walther, A. Materials learning from life: concepts for active, adaptive and autonomous molecular systems. Chem. Soc. Rev. 46, 5588–5619 (2017). · DOI
  15. Urban, M. W. Handbook of Stimuli-Responsive Materials (Wiley, 2011). · DOI
  16. He, X. et al. Synthetic homeostatic materials with chemo-mechano-chemical self-regulation. Nature 487, 214–218 (2012). An intriguing example of an autonomous, homeostatic material system based on chemo-mechanical feedback loops. · DOI
  17. Anderson, C., Theraulaz, G. & Deneubourg, J. L. Self-assemblages in insect societies. Insectes Soc. 49, 99–110 (2002). · DOI
  18. Lopez, U., Gautrais, J., Couzin, I. D. & Theraulaz, G. From behavioural analyses to models of collective motion in fish schools. Interface Focus 2, 693–707 (2012). · DOI
  19. Bajec, I. L. & Heppner, F. H. Organized flight in birds. Anim. Behav. 78, 777–789 (2009). · DOI
  20. Hinchey, M. G., Sterritt, R. & Rouff, C. Swarms and swarm intelligence. Computer 40, 111–113 (2007). · DOI
  21. Yu, J., Wang, B., Du, X., Wang, Q. & Zhang, L. Ultra-extensible ribbon-like magnetic microswarm. Nat. Commun. 9, 3260 (2018). This article demonstrates how paramagnetic nanoparticles self-organize in a microswarm that can pass obstacles and how its locomotion can be controlled by applying oscillating magnetic fields. · DOI
  22. Palacci, J., Sacanna, S., Steinberg, A. P., Pine, D. J. & Chaikin, P. M. Living crystals of light-activated colloidal surfers. Science 339, 936–940 (2013). · DOI
  23. Yan, J., Bloom, M., Bae, S. C., Luijten, E. & Granick, S. Linking synchronization to self-assembly using magnetic Janus colloids. Nature 491, 578–581 (2012). · DOI
  24. Liang, X. et al. Hierarchical microswarms with leader–follower-like structures: electrohydrodynamic self-organization and multimode collective photoresponses. Adv. Funct. Mater. 30, 1908602 (2020). · DOI
  25. Mou, F. et al. Phototactic flocking of photochemical micromotors. iScience 19, 415–424 (2019). This study shows flocking behaviour of synthesized spherical microparticles, which can execute transporting tasks along predefined pathways or bypass obstacles. · DOI
  26. Dai, B. et al. Programmable artificial phototactic microswimmer. Nat. Nanotechnol. 11, 1087–1092 (2016). · DOI
  27. Tagliazucchi, M., Weiss, E. A. & Szleifer, I. Dissipative self-assembly of particles interacting through time-oscillatory potentials. Proc. Natl Acad. Sci. USA 111, 9751–9756 (2014). · DOI
  28. Carnall, J. M. A. et al. Mechanosensitive self-replication driven by self-organization. Science 327, 1502–1506 (2010). · DOI
  29. Sadownik, J. W., Mattia, E., Nowak, P. & Otto, S. Diversification of self-replicating molecules. Nat. Chem. 8, 264–269 (2016). · DOI · Paper page
  30. Monreal Santiago, G., Liu, K., Browne, W. R. & Otto, S. Emergence of light-driven protometabolism upon recruitment of a photocatalytic cofactor by a self-replicator. Nat. Chem. 12, 603–607 (2020). · DOI
  31. Zhu, B. et al. Skin-inspired haptic memory arrays with an electrically reconfigurable architecture. Adv. Mater. 28, 1559–1566 (2016). · DOI
  32. Son, D. et al. Multifunctional wearable devices for diagnosis and therapy of movement disorders. Nat. Nanotechnol. 9, 397–404 (2014). · DOI · Paper page
  33. Miriyev, A., Stack, K. & Lipson, H. Soft material for soft actuators. Nat. Commun. 8, 596 (2017). · DOI · Paper page
  34. Zhao, Z., Wang, C., Yan, H. & Liu, Y. Soft robotics programmed with double crosslinking DNA hydrogels. Adv. Funct. Mater. 29, 1905911 (2019). This article shows impressively how to translate nanometre-scale DNA self-assembly into macroscopic movements of soft materials, an encouraging achievement for soft robotics. · DOI
  35. Yang, H. et al. 3D printed photoresponsive devices based on shape memory composites. Adv. Mater. 29, 1701627 (2017). · DOI · Paper page
  36. Lai, Y. C. et al. Actively perceiving and responsive soft robots enabled by self-powered, highly extensible, and highly sensitive triboelectric proximity- and pressure-sensing skins. Adv. Mater. 30, 1801114 (2018). This work presents soft robots driven by self-generated electricity via the triboelectric effect, which can sense and embrace close objects. · DOI
  37. Schroeder, T. B. H. et al. An electric-eel-inspired soft power source from stacked hydrogels. Nature 552, 214–218 (2017). · DOI
  38. Liu, Y. et al. Stretchable motion memory devices based on mechanical hybrid materials. Adv. Mater. 29, 1701780 (2017). · DOI
  39. Oh, J. Y. et al. Intrinsically stretchable and healable semiconducting polymer for organic transistors. Nature 539, 411–415 (2016). · DOI
  40. Urban, M. W. et al. Key-and-lock commodity self-healing copolymers. Science 225, 220–225 (2018). A remarkable example for an advanced soft material with self-healing capabilities. · DOI
  41. Chen, Y., Kushner, A. M., Williams, G. A. & Guan, Z. Multiphase design of autonomic self-healing thermoplastic elastomers. Nat. Chem. 4, 467–472 (2012). · DOI
  42. Li, C. H. et al. A highly stretchable autonomous self-healing elastomer. Nat. Chem. 8, 618–624 (2016). · DOI
  43. Beyer, H. M. et al. Synthetic biology makes polymer materials count. Adv. Mater. 30, 1800472 (2018). · DOI
  44. Zhang, H., Zeng, H., Priimagi, A. & Ikkala, O. Programmable responsive hydrogels inspired by classical conditioning algorithm. Nat. Commun. 10, 3267 (2019). · DOI
  45. Zeng, H., Zhang, H., Ikkala, O. & Priimagi, A. Associative learning by classical conditioning in liquid crystal network actuators. Matter 2, 194–206 (2020). Associative learning is realized in a liquid crystal network material via a conditioning process, where an initially neutral light stimulus is asscociated with heating. · DOI
  46. Kumar, B. V. V. S. P., Patil, A. J. & Mann, S. Enzyme-powered motility in buoyant organoclay/DNA protocells. Nat. Chem. 10, 1154–1163 (2018). · DOI
  47. Garrad, M., Soter, G., Conn, A. T., Hauser, H. & Rossiter, J. A soft matter computer for soft robots. Sci. Robot. 4, eaaw6060 (2019). The authors propose a computational system integrated into a soft material, which, inspired by biological systems, tranfers information via a fluid perfusing through the system. · DOI
  48. Miller, J. F. & Downing, K. Evolution in materio: looking beyond the silicon box. In Proc. NASA/DoD Conference on Evolvable Hardware 167–176, https://doi.org/10.1109/EH.2002.1029882 (2002). · DOI
  49. Feynman, R. P. The Character of Physical Law (MIT Press, 1967).
  50. Yoshihito, A. Information processing using intelligent materials - information-processing architectures for material processors. J. Intell. Mater. Syst. Struct. 5, 418–423 (1994). · DOI
  51. Merolla, P. A. et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345, 668–673 (2014). · DOI
  52. Zhang, W., Mazzarello, R., Wuttig, M. & Ma, E. Designing crystallization in phase-change materials for universal memory and neuro-inspired computing. Nat. Rev. Mater. 4, 150–168 (2019). · DOI
  53. Sebastian, A. et al. Tutorial: brain-inspired computing using phase-change memory devices. J. Appl. Phys. 124, 111101 (2018). · DOI
  54. Feldmann, J., Youngblood, N., Wright, C. D., Bhaskaran, H. & Pernice, W. H. P. All-optical spiking neurosynaptic networks with self-learning capabilities. Nature 569, 208–214 (2019). · DOI
  55. Ríos, C. et al. Integrated all-photonic non-volatile multi-level memory. Nat. Photon. 9, 725–732 (2015). · DOI
  56. Cheng, Z., Ríos, C., Pernice, W. H. P., David Wright, C. & Bhaskaran, H. On-chip photonic synapse. Sci. Adv. 3, e1700160 (2017). This article shows an artificial synapse consisting of a photonic waveguide and a phase-change material, which paves the way for on-chip neuromorphic computing. · DOI
  57. Gupta, A., Sakthivel, T. & Seal, S. Recent development in 2D materials beyond graphene. Prog. Mater. Sci. 73, 44–126 (2015). · DOI
  58. Novoselov, K. S., Mishchenko, A., Carvalho, A. & Castro Neto, A. H. 2D materials and van der Waals heterostructures. Science 353, aac9439 (2016). · DOI
  59. Geim, A. K. & Grigorieva, I. V. Van der Waals heterostructures. Nature 499, 419–425 (2013). · DOI
  60. Mak, K. F., Lee, C., Hone, J., Shan, J. & Heinz, T. F. Atomically thin MoS2: a new direct-gap semiconductor. Phys. Rev. Lett. 105, 136805 (2010). · DOI · Paper page
  61. Splendiani, A. et al. Emerging photoluminescence in monolayer MoS2. Nano Lett. 10, 1271–1275 (2010). · DOI
  62. Wan, J. et al. Tuning two-dimensional nanomaterials by intercalation: materials, properties and applications. Chem. Soc. Rev. 45, 6742–6765 (2016). · DOI
  63. Zeng, M. et al. Bandgap tuning of two-dimensional materials by sphere diameter engineering. Nat. Mater. 19, 528–533 (2020). · DOI
  64. Choi, C. et al. Human eye-inspired soft optoelectronic device using high-density MoS2-graphene curved image sensor array. Nat. Commun. 8, 1664 (2017). · DOI
  65. Shi, Y. et al. Electronic synapses made of layered two-dimensional materials. Nat. Electron. 1, 458–465 (2018). · DOI
  66. He, C. et al. Artificial synapse based on van der Waals heterostructures with tunable synaptic functions for neuromorphic computing. ACS Appl. Mater. Interfaces 12, 11945–11954 (2020). · DOI
  67. Park, H., Mastro, M. A., Tadjer, M. J. & Kim, J. Programmable multilevel memtransistors based on van der Waals heterostructures. Adv. Electron. Mater. 5, 1900333 (2019). · DOI
  68. Liu, C. et al. A semi-floating gate memory based on van der Waals heterostructures for quasi-non-volatile applications. Nat. Nanotechnol. 13, 404–410 (2018). · DOI
  69. Mennel, L. et al. Ultrafast machine vision with 2D material neural network image sensors. Nature 579, 62–66 (2020). The presented image sensor based on a 2D material constitutes at the same time an artificial neural network. · DOI
  70. Bose, S. K. et al. Evolution of a designless nanoparticle network into reconfigurable Boolean logic. Nat. Nanotechnol. 10, 1048–1052 (2015). Computational functionality is experimentally realized in a disordered nanomaterial network consisting of arbitrarily interconnected, functionalized nanoparticles. · DOI
  71. Chen, T. et al. Classification with a disordered dopant-atom network in silicon. Nature 577, 341–345 (2020). · DOI · Paper page
  72. Ruiz Euler, H.-C. et al. A deep-learning approach to realising functionality in nanoelectronic devices. Nat. Nanotechnol. 15, 992–998 (2020). · DOI · Paper page
  73. Ruiz Euler, H.-C. et al. Dopant network processing units: towards efficient neural-network emulators with high-capacity nanoelectronic nodes. Preprint at http://arxiv.org/abs/2007.12371 (2020). · arXiv
  74. Estakhri, N. M., Edwards, B. & Engheta, N. Inverse-designed metastructures that solve equations. Science 363, 1333–1338 (2019). · DOI
  75. Hirano, Y., Segawa, Y., Kuroda-Sowa, T., Kawai, T. & Matsumoto, T. Conductance with stochastic resonance in Mn12 redox network without tuning. Appl. Phys. Lett. 104, 233104 (2014). · DOI
  76. Maass, W., Natschläger, T. & Markram, H. Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput. 14, 2531–2560 (2002). · DOI · Paper page
  77. Steil, J. J. Backpropagation-decorrelation: online recurrent learning with O(N) complexity. In IEEE Int. Conf. on Neural Networks 2, 843–848 (IEEE, 2004). · DOI
  78. Schürmann, F., Meier, K. & Schemmel, J. Edge of chaos computation in mixed-mode VLSI—a hard liquid. In Advances in Neural Information Processing Systems 17, 1201–1208 (2004).
  79. Schrauwen, B., D’Haene, M., Verstraeten, D. & Van Campenhout, J. Compact hardware liquid state machines on FPGA for real-time speech recognition. Neural Netw. 21, 511–523 (2008). · DOI
  80. Fernando, C. & Sojakka, S. Pattern recognition in a bucket. In Proc. ECAL 588–597 (2003). · DOI
  81. Jones, B., Stekel, D., Rowe, J. & Fernando, C. Is there a liquid state machine in the bacterium Escherichia coli? In Proc. 2007 IEEE Symp. Artif. Life (CI-ALife 2007) 187–191, https://doi.org/10.1109/ALIFE.2007.367795 (2007). · DOI
  82. Dai, X. in Advances in Neural Networks Vol. 3174 (eds Yin, F. L. et al.) 519–524 (Springer, 2004). · DOI
  83. Goudarzi, A., Lakin, M. R. & Stefanovic, D. DNA reservoir computing: a novel molecular computing approach. In DNA Computing and Molecular Programming (eds Soloveichik D. & Yurke, B.) Vol. 8141, 76–89 (Springer, 2013). · DOI
  84. Nikolić, D., Haeusler, S., Singer, W. & Maass, W. Temporal dynamics of information content carried by neurons in the primary visual cortex. In Advances in Neural Information Processing Systems 1041–1048, https://doi.org/10.7551/mitpress/7503.003.0135 (2007). · DOI
  85. Duport, F., Smerieri, A., Akrout, A., Haelterman, M. & Massar, S. Fully analogue photonic reservoir computer. Sci. Rep. 6, 22381 (2016). · DOI
  86. Larger, L. et al. Photonic information processing beyond Turing: an optoelectronic implementation of reservoir computing. Opt. Express 20, 3241 (2012). · DOI
  87. Vandoorne, K. et al. Experimental demonstration of reservoir computing on a silicon photonics chip. Nat. Commun. 5, 3541 (2014). · DOI
  88. Larger, L. et al. High-speed photonic reservoir computing using a time-delay-based architecture: million words per second classification. Phys. Rev. X 7, 011015 (2017).
  89. Appeltant, L. et al. Information processing using a single dynamical node as complex system. Nat. Commun. 2, 468 (2011). · DOI
  90. Kulkarni, M. S. Memristor-based reservoir computing. In 2012 IEEE/ACM Int. Symp. on Nanoscale 226–232, https://doi.org/10.1145/2765491.2765531 (IEEE/ACM, 2012). · DOI
  91. Bürger, J. & Teuscher, C. Variation-tolerant computing with memristive reservoirs. In 2013 IEEE/ACM Int. Symp. on Nanoscale Architectures (NANOARCH) 1–6, https://doi.org/10.1109/NanoArch.2013.6623028 (IEEE/ACM, 2013). · DOI
  92. Merkel, C., Saleh, Q., Donahue, C. & Kudithipudi, D. Memristive reservoir computing architecture for epileptic seizure detection. Proc. Comput. Sci. 41, 249–254 (2014). · DOI
  93. Hassan, A. M., Li, H. H. & Chen, Y. Hardware implementation of echo state networks using memristor double crossbar arrays. In 2017 Int. Joint Conf. on Neural Networks (IJCNN) 2171–2177, https://doi.org/10.1109/IJCNN.2017.7966118 (IEEE, 2017). · DOI
  94. Soures, N., Hays, L. & Kudithipudi, D. Robustness of a memristor based liquid state machine. In 2017 Int. Joint Conf. on Neural Networks (IJCNN) 2414–2420, https://doi.org/10.1109/IJCNN.2017.7966149 (IEEE, 2017). · DOI
  95. Du, C. et al. Reservoir computing using dynamic memristors for temporal information processing. Nat. Commun. 8, 2204 (2017). · DOI
  96. Moon, J. et al. Temporal data classification and forecasting using a memristor-based reservoir computing system. Nat. Electron. 2, 480–487 (2019). · DOI · Paper page
  97. Sillin, H. O. et al. A theoretical and experimental study of neuromorphic atomic switch networks for reservoir computing. Nanotechnology 24, 384004 (2013). · DOI
  98. Demis, E. C. et al. Atomic switch networks—nanoarchitectonic design of a complex system for natural computing. Nanotechnology 26, 204003 (2015). · DOI
  99. Demis, E. C. et al. Nanoarchitectonic atomic switch networks for unconventional computing. Jpn. J. Appl. Phys. 55, 1102B2 (2016). · DOI
  100. Dale, M., Stepney, S., Miller, J. F. & Trefzer, M. Reservoir computing in materio: an evaluation of configuration through evolution. In 2016 IEEE Symp. Ser. Comput. Intell. SSCI 2016 https://doi.org/10.1109/SSCI.2016.7850170 (IEEE, 2016). · DOI
  101. Dale, M., Miller, J. F. & Stepney, S. Reservoir computing as a model for in-materio computing. In Advances in Unconventional Computing (ed. Adamatzky, A.) 533–571 (Springer, 2017). · DOI
  102. Tanaka, H. et al. A molecular neuromorphic network device consisting of single-walled carbon nanotubes complexed with polyoxometalate. Nat. Commun. 9, 2693 (2018). · DOI
  103. Appeltant, L., Van Der Sande, G., Danckaert, J. & Fischer, I. Constructing optimized binary masks for reservoir computing with delay systems. Sci. Rep. 4, 3629 (2015). · DOI
  104. Dale, M., Miller, J. F., Stepney, S. & Trefzer, M. A. Evolving Carbon nanotube reservoir computers. In Unconventional Computation and Natural Computation (eds Amos, M. & Condon, A.) 49–61 (Springer, 2016). This study demonstrates how physical media can be exploited as a reservoir for machine-learning capabilities. · DOI
  105. Tanaka, G. et al. Recent advances in physical reservoir computing: a review. Neural Netw. 115, 100–123 (2019). · DOI · Paper page

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Metadata

Authors
Kaspar, C., Ravoo, B. J., van der Wiel, W. G., Wegner, S. V., & Pernice, W. H. P.
Affiliations
University of Münster (DE)
Corresponding author
Pernice, W. H. P.
ORCID
Venue
Nature
Publisher
Springer Science and Business Media LLC
Published
2021-06-16
Volume
594
Issue
7863
Pages
345-355
ISSN
  • 0028-0836 (ISSN-L)
  • 1476-4687
DOI
10.1038/s41586-021-03453-y
OpenAlex
W3169707058
Cited by
526
References
0
Citations by year
2026: 70 · 2025: 136 · 2024: 112 · 2023: 106 · 2022: 75 · 2021: 27
FWCI
5.06
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature (publishedVersion)
  • PubMed (publishedVersion)
  • University of Twente Research Information (publishedVersion, other-oa)
  • RePEc: Research Papers in Economics (submittedVersion)
  • University of Twente Research Information (submittedVersion, other-oa)
Funders
  • European Commission (101017237, HORIZON2020)
  • Deutsche Forschungsgemeinschaft (SFB 1459, 433682494)
  • Volkswagen Foundation (A126874)
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
  • Physical Sciences › Engineering › Mechanical Engineering › Advanced Materials and Mechanics
  • Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Reservoir Computing
Keywords
neuromorphic computing, decentralized computing, molecular systems, soft materials, solid-state materials, soft robotics, distributed memory, pattern recognition
MeSH terms
Artificial Intelligence, Colloids, Environment, Enzymes, Equipment Design, Homeostasis, Humans, Physical Stimulation, Robotics, Skin, Artificial, Biomimetics, Biomimetic Materials

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