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  • Perspective
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Organic mixed conductors for bioinspired electronics

Nature Reviews Materials volume 9, pages 134–149 (2024)Cite this article

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Abstract

Owing to its close resemblance to biological systems and materials, soft matter has been successfully implemented in numerous bioelectronic and biosensing applications, as well as in bioinspired computing and neuromorphic electronics. Particularly, organic mixed ionic–electronic conductors possess favourable characteristics for their efficient use in organic electrochemical transistors, electrochemical memory and artificial synapses and neurons. Owing to their mixed ionic–electronic conduction, leading to high amplification, these materials are ideal for translating chemical signals, such as ions or neurotransmitters, into electrical signals, as well as for accurately controlling stable conductance states to efficiently emulate synaptic weights in artificial neural networks. Because these mixed conductors operate with ionic charges — similar to signalling in biological neuronal networks — they also exhibit ideal properties to emulate biological spiking neurons. In this Perspective, we consider the potential of soft matter, especially based on organic mixed conductors, for bioinspired systems and their possible applications. We discuss the potential that these materials have in applications in which low power, conformability and tunability are key, such as smart and adaptive biosensors, low-power in-sensor and edge computing, intelligent agents and robotics, and event-driven systems and biohybrid spiking circuits at the interface with biology. We present a comprehensive perspective of the potential of biomimetic and bioinspired electronics based on soft matter to integrate artificial intelligence into everyday life.

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Fig. 1: Organic biomimetic and bioinspired materials, electronics and systems.
Fig. 2: Organic neuromorphic devices and building blocks based on organic mixed ionic–electronic conductors.
Fig. 3: From devices to integrated networks and architectures.
Fig. 4: Biohybrid devices and systems.

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References

  1. Mehonic, A. & Kenyon, A. J. Brain-inspired computing needs a master plan. Nature 604, 255–260 (2022).

    Article  ADS  CAS  PubMed  Google Scholar 

  2. Indiveri, G. et al. Neuromorphic silicon neuron circuits. Front. Neurosci. 5, 73 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Christensen, D. V. et al. 2022 roadmap on neuromorphic computing and engineering. Neuromorphic Comput. Eng. 2, 022501 (2022).

    Article  Google Scholar 

  4. Burr, G. W. et al. Neuromorphic computing using non-volatile memory. Adv. Phys. X 2, 89–124 (2017).

    Google Scholar 

  5. Marković, D., Mizrahi, A., Querlioz, D. & Grollier, J. Physics for neuromorphic computing. Nat. Rev. Phys. 2, 499–510 (2020).

    Article  Google Scholar 

  6. Zhang, W. et al. Neuro-inspired computing chips. Nat. Electron. 3, 371–382 (2020).

    Article  ADS  Google Scholar 

  7. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    Article  ADS  CAS  PubMed  Google Scholar 

  8. Hinton, G. E. & Salakhutdinov, R. R. Reducing the dimensionality of data with neural networks. Science 313, 504–507 (2006).

    Article  ADS  MathSciNet  CAS  PubMed  Google Scholar 

  9. Bartolozzi, C., Indiveri, G. & Donati, E. Embodied neuromorphic intelligence. Nat. Commun. 13, 1024 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  10. Sandamirskaya, Y., Kaboli, M., Conradt, J. & Celikel, T. Neuromorphic computing hardware and neural architectures for robotics. Sci. Robot. 7, eabl8419 (2022).

    Article  PubMed  Google Scholar 

  11. Liu, F. et al. Neuro-inspired electronic skin for robots. Sci. Robot. 7, eabl7344 (2022).

    Article  PubMed  Google Scholar 

  12. Duan, Q. et al. Spiking neurons with spatiotemporal dynamics and gain modulation for monolithically integrated memristive neural networks. Nat. Commun. 11, 3399 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  13. Bohlen und Halbach, O. V. & Dermietzel, R. Neurotransmitters and Neuromodulators: Handbook of Receptors and Biological Effects 2nd edn (Wiley-VCH, 2006).

  14. van de Burgt, Y., Melianas, A., Keene, S. T., Malliaras, G. & Salleo, A. Organic electronics for neuromorphic computing. Nat. Electron. 1, 386–397 (2018).

    Article  Google Scholar 

  15. van de Burgt, Y. & Gkoupidenis, P. Organic materials and devices for brain-inspired computing: from artificial implementation to biophysical realism. MRS Bull. 45, 631–640 (2020).

    Article  ADS  Google Scholar 

  16. Ling, H. et al. Electrolyte-gated transistors for synaptic electronics, neuromorphic computing, and adaptable biointerfacing. Appl. Phys. Rev. 7, 011307 (2020).

    Article  ADS  CAS  Google Scholar 

  17. Tuchman, Y. et al. Organic neuromorphic devices: past, present, and future challenges. MRS Bull. 45, 619–630 (2020).

    Article  ADS  Google Scholar 

  18. Lubrano, C. et al. Towards biomimetic electronics that emulate cells. MRS Commun. 10, 398–412 (2020).

    Article  CAS  Google Scholar 

  19. van de Burgt, Y. et al. A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing. Nat. Mater. 16, 414–418 (2017).

    Article  ADS  PubMed  Google Scholar 

  20. Harikesh, P. C. et al. Organic electrochemical neurons and synapses with ion mediated spiking. Nat. Commun. 13, 901 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Sarkar, T. et al. An organic artificial spiking neuron for in situ neuromorphic sensing and biointerfacing. Nat. Electron. 5, 774–783 (2022).

    Article  Google Scholar 

  22. Harikesh, P. C. et al. Ion-tunable antiambipolarity in mixed ion–electron conducting polymers enables biorealistic organic electrochemical neurons. Nat. Mater. 22, 242–248 (2023).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  23. Fuller, E. J. et al. Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing. Science 364, 570–574 (2019).

    Article  ADS  CAS  PubMed  Google Scholar 

  24. Koutsouras, D. A., Malliaras, G. G. & Gkoupidenis, P. Emulating homeoplasticity phenomena with organic electrochemical devices. MRS Commun. 8, 493–497 (2018).

    Article  CAS  Google Scholar 

  25. Gkoupidenis, P., Koutsouras, D. A. & Malliaras, G. G. Neuromorphic device architectures with global connectivity through electrolyte gating. Nat. Commun. 8, 15448 (2017).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  26. Koutsouras, D. A., Prodromakis, T., Malliaras, G. G., Blom, P. W. M. & Gkoupidenis, P. Functional connectivity of organic neuromorphic devices by global voltage oscillations. Adv. Intell. Syst. 1, 1900013 (2019).

    Article  Google Scholar 

  27. Zhang, Y. et al. Adaptive biosensing and neuromorphic classification based on an ambipolar organic mixed ionic–electronic conductor. Adv. Mater. 34, 2200393 (2022).

    Article  CAS  Google Scholar 

  28. van Doremaele, E. R. W., Ji, X., Rivnay, J. & van de Burgt, Y. A retrainable neuromorphic biosensor for on-chip learning and classification. Nat. Electron. 6, 765–770 (2023).

    Article  Google Scholar 

  29. Keene, S. T. et al. A biohybrid synapse with neurotransmitter-mediated plasticity. Nat. Mater. 19, 969–973 (2020).

    Article  ADS  CAS  PubMed  Google Scholar 

  30. Matrone, G. M. et al. Electrical and optical modulation of a PEDOT:PSS-based electrochemical transistor for multiple neurotransmitter-mediated artificial synapses. Adv. Mater. Tech. 8, 2201911 (2023).

    Article  CAS  Google Scholar 

  31. Cucchi, M. et al. Reservoir computing with biocompatible organic electrochemical networks for brain-inspired biosignal classification. Sci. Adv. 7, eabh0693 (2021).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  32. Strakosas, X. et al. Metabolite-induced in vivo fabrication of substrate-free organic bioelectronics. Science 379, 795–802 (2023).

    Article  ADS  CAS  PubMed  Google Scholar 

  33. Krauhausen, I. et al. Organic neuromorphic electronics for sensorimotor integration and learning in robotics. Sci. Adv. 7, eabl5068 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  34. Rivnay, J. et al. Organic electrochemical transistors. Nat. Rev. Mater. 3, 17086 (2018).

    Article  ADS  CAS  Google Scholar 

  35. Gkoupidenis, P., Schaefer, N., Strakosas, X., Fairfield, J. A. & Malliaras, G. G. Synaptic plasticity functions in an organic electrochemical transistor. Appl. Phys. Lett. 107, 263302 (2015).

    Article  ADS  Google Scholar 

  36. Yamamoto, S. & Malliaras, G. G. Controlling the neuromorphic behavior of organic electrochemical transistors by blending mixed and ion conductors. ACS Appl. Electron. Mater. 2, 2224–2228 (2020).

    Article  CAS  Google Scholar 

  37. Ji, X. et al. Mimicking associative learning using an ion-trapping non-volatile synaptic organic electrochemical transistor. Nat. Commun. 12, 2480 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  38. Giovannitti, A. et al. Sodium and potassium ion selective conjugated polymers for optical ion detection in solution and solid state. Adv. Funct. Mater. 26, 514–523 (2016).

    Article  CAS  Google Scholar 

  39. Wustoni, S. et al. Membrane-free detection of metal cations with an organic electrochemical transistor. Adv. Funct. Mater. 29, 1904403 (2019).

    Article  CAS  Google Scholar 

  40. Salinas, G. et al. Sodium-ion selectivity study of a crown-ether-functionalized PEDOT analog. ChemElectroChem 7, 2826–2830 (2020).

    Article  CAS  Google Scholar 

  41. Giovannitti, A. et al. Controlling the mode of operation of organic transistors through side-chain engineering. Proc. Natl Acad. Sci. USA 113, 12017–12022 (2016).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  42. Giovannitti, A. et al. N-type organic electrochemical transistors with stability in water. Nat. Commun. 7, 13066 (2016).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  43. Schattling, P., Jochum, F. D. & Theato, P. Multi-stimuli responsive polymers — the all-in-one talents. Polym. Chem. 5, 25–36 (2014).

    Article  CAS  Google Scholar 

  44. Chen, K. et al. Organic optoelectronic synapse based on photon-modulated electrochemical doping. Nat. Photonics 17, 629–637 (2023).

    Article  ADS  CAS  Google Scholar 

  45. Xu, J. et al. Highly stretchable polymer semiconductor films through the nanoconfinement effect. Science 355, 59–64 (2017).

    Article  ADS  CAS  PubMed  Google Scholar 

  46. Wang, S. et al. Skin electronics from scalable fabrication of an intrinsically stretchable transistor array. Nature 555, 83–88 (2018).

    Article  ADS  CAS  PubMed  Google Scholar 

  47. Li, N. et al. Bioadhesive polymer semiconductors and transistors for intimate biointerfaces. Science 381, 686–693 (2023).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  48. Wang, S. & Urban, M. W. Self-healing polymers. Nat. Rev. Mater. 5, 562–583 (2020).

    Article  ADS  CAS  Google Scholar 

  49. Cooper, C. B. et al. Autonomous alignment and healing in multilayer soft electronics using immiscible dynamic polymers. Science 380, 935–941 (2023).

    Article  ADS  CAS  PubMed  Google Scholar 

  50. Christov-Moore, L. et al. Preventing antisocial robots: a pathway to artificial empathy. Sci. Robot. 8, eabq3658 (2023).

    Article  PubMed  Google Scholar 

  51. Gerasimov, J. Y. et al. An evolvable organic electrochemical transistor for neuromorphic applications. Adv. Sci. 6, 1801339 (2019).

    Article  Google Scholar 

  52. Cucchi, M., Parker, D., Stavrinidou, E., Gkoupidenis, P. & Kleemann, H. In liquido computation with electrochemical transistors and mixed conductors for intelligent bioelectronics. Adv. Mater. 35, 2209516 (2023).

    Article  CAS  Google Scholar 

  53. Cucchi, M. et al. Directed growth of dendritic polymer networks for organic electrochemical transistors and artificial synapses. Adv. Electron. Mater. 7, 2100586 (2021).

    Article  CAS  Google Scholar 

  54. Chen, S., Zhang, T., Tappertzhofen, S., Yang, Y. & Valov, I. Electrochemical-memristor-based artificial neurons and synapses — fundamentals, applications, and challenges. Adv. Mater. 35, 2301924 (2023).

    Article  CAS  Google Scholar 

  55. Sekitani, T. & Someya, T. Stretchable, large-area organic electronics. Adv. Mater. 22, 2228–2246 (2010).

    Article  CAS  PubMed  Google Scholar 

  56. Moser, M. et al. Ethylene glycol-based side chain length engineering in polythiophenes and its impact on organic electrochemical transistor performance. Chem. Mater. 32, 6618–6628 (2020).

    Article  ADS  CAS  Google Scholar 

  57. Moser, M. et al. Polaron delocalization in donor–acceptor polymers and its impact on organic electrochemical transistor performance. Angew. Chem. Int. Ed. 60, 7777–7785 (2021).

    Article  ADS  CAS  Google Scholar 

  58. Moser, M. et al. Side chain redistribution as a strategy to boost organic electrochemical transistor performance and stability. Adv. Mater. 32, 2002748 (2020).

    Article  ADS  CAS  Google Scholar 

  59. Inal, S., Malliaras, G. G. & Rivnay, J. Benchmarking organic mixed conductors for transistors. Nat. Commun. 8, 1767 (2017).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  60. Keene, S. T. et al. Enhancement-mode PEDOT:PSS organic electrochemical transistors using molecular de-doping. Adv. Mater. 32, 2000270 (2020).

    Article  CAS  Google Scholar 

  61. Sun, H., Gerasimov, J., Berggren, M. & Fabiano, S. n-Type organic electrochemical transistors: materials and challenges. J. Mater. Chem. C 6, 11778–11784 (2018).

    Article  CAS  Google Scholar 

  62. Sun, H. et al. Complementary logic circuits based on high-performance n-type organic electrochemical transistors. Adv. Mater. 30, 1704916 (2018).

    Article  ADS  Google Scholar 

  63. Yang, C.-Y. et al. A high-conductivity n-type polymeric ink for printed electronics. Nat. Commun. 12, 2354 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  64. Paterson, A. F. et al. Water stable molecular n-doping produces organic electrochemical transistors with high transconductance and record stability. Nat. Commun. 11, 3004 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  65. Liao, H. et al. Efficient n-type small-molecule mixed ion-electron conductors and application in hydrogen peroxide sensors. ACS Appl. Mater. Interfaces 14, 16477–16486 (2022).

    Article  CAS  PubMed  Google Scholar 

  66. Bischak, C. G., Flagg, L. Q., Yan, K., Li, C.-Z. & Ginger, D. S. Fullerene active layers for n-type organic electrochemical transistors. ACS Appl. Mater. Interfaces 11, 28138–28144 (2019).

    Article  CAS  PubMed  Google Scholar 

  67. Zhang, Y. et al. High-performance organic electrochemical transistors and neuromorphic devices comprising naphthalenediimide-dialkoxybithiazole copolymers bearing glycol ether pendant groups. Adv. Funct. Mater. 32, 2201593 (2022).

    Article  CAS  Google Scholar 

  68. Samuel, J. J. et al. Single-component CMOS-like logic using diketopyrrolopyrrole-based ambipolar organic electrochemical transistors. Adv. Funct. Mater. 31, 2102903 (2021).

    Article  CAS  Google Scholar 

  69. Huang, W. et al. Vertical organic electrochemical transistors for complementary circuits. Nature 613, 496–502 (2023).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  70. Stein, E. et al. Ambipolar blend-based organic electrochemical transistors and inverters. Nat. Commun. 13, 5548 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  71. Rashid, R. B. et al. Ambipolar inverters based on cofacial vertical organic electrochemical transistor pairs for biosignal amplification. Sci. Adv. 7, eabh1055 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  72. Xu, K. et al. On the origin of Seebeck coefficient inversion in highly doped conducting polymers. Adv. Funct. Mater. 32, 2112276 (2022).

    Article  CAS  Google Scholar 

  73. Weissbach, A. et al. Photopatternable solid electrolyte for integrable organic electrochemical transistors: operation and hysteresis. J. Mater. Chem. C 10, 2656–2662 (2022).

    Article  CAS  Google Scholar 

  74. Cucchi, M. et al. Thermodynamics of organic electrochemical transistors. Nat. Commun. 13, 4514 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  75. Rebetez, G., Bardagot, O., Affolter, J., Réhault, J. & Banerji, N. What drives the kinetics and doping level in the electrochemical reactions of PEDOT:PSS? Adv. Funct. Mater. 32, 2105821 (2022).

    Article  CAS  Google Scholar 

  76. Paulsen, B. D. et al. Time-resolved structural kinetics of an organic mixed ionic–electronic conductor. Adv. Mater. 32, 2003404 (2020).

    Article  CAS  Google Scholar 

  77. Wu, R., Matta, M., Paulsen, B. D. & Rivnay, J. Operando characterization of organic mixed ionic/electronic conducting materials. Chem. Rev. 122, 4493–4551 (2022).

    Article  CAS  PubMed  Google Scholar 

  78. Xia, Q. & Yang, J. J. Memristive crossbar arrays for brain-inspired computing. Nat. Mater. 18, 309–323 (2019).

    Article  ADS  CAS  PubMed  Google Scholar 

  79. Ielmini, D. & Wong, H. S. P. In-memory computing with resistive switching devices. Nat. Electron. 1, 333–343 (2018).

    Article  Google Scholar 

  80. Goswami, S. et al. Robust resistive memory devices using solution-processable metal-coordinated azo aromatics. Nat. Mater. 16, 1216–1224 (2017).

    Article  ADS  CAS  PubMed  Google Scholar 

  81. Goswami, S. et al. Decision trees within a molecular memristor. Nature 597, 51–56 (2021).

    Article  ADS  CAS  PubMed  Google Scholar 

  82. Wang, H. et al. Configurable resistive switching between memory and threshold characteristics for protein-based devices. Adv. Funct. Mater. 25, 3825–3831 (2015).

    Article  CAS  Google Scholar 

  83. Wang, H. et al. Resistive switching memory devices based on proteins. Adv. Mater. 27, 7670–7676 (2015).

    Article  CAS  PubMed  Google Scholar 

  84. Fu, T. et al. Bioinspired bio-voltage memristors. Nat. Commun. 11, 1861 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  85. Najem, J. S. et al. Dynamical nonlinear memory capacitance in biomimetic membranes. Nat. Commun. 10, 3239 (2019).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  86. Nau, S. et al. Inkjet-printed resistive switching memory based on organic dielectric materials: from single elements to array technology. Adv. Electron. Mater. 1, 1400003 (2015).

    Article  Google Scholar 

  87. Gärisch, F. et al. Organic synaptic diodes based on polymeric mixed ionic-electronic conductors. Adv. Electron. Mater. 8, 2100866 (2022).

    Article  Google Scholar 

  88. Minnekhanov, A. A. et al. Parylene based memristive devices with multilevel resistive switching for neuromorphic applications. Sci. Rep. 9, 10800 (2019).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  89. Wu, C., Kim, T. W., Choi, H. Y., Strukov, D. B. & Yang, J. J. Flexible three-dimensional artificial synapse networks with correlated learning and trainable memory capability. Nat. Commun. 8, 752 (2017).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  90. Fuller, E. J. et al. Li-ion synaptic transistor for low power analog computing. Adv. Mater. 29, 1604310 (2017).

    Article  Google Scholar 

  91. Li, Y. et al. In situ parallel training of analog neural network using electrochemical random-access memory. Front. Neurosci. 15, 636127 (2021).

    Article  MathSciNet  PubMed  PubMed Central  Google Scholar 

  92. Mangoma, T. N., Yamamoto, S., Malliaras, G. G. & Daly, R. Hybrid 3D/inkjet-printed organic neuromorphic transistors. Adv. Mater. Technol. 7, 2000798 (2022).

    Article  CAS  Google Scholar 

  93. Dai, S. et al. Intrinsically stretchable neuromorphic devices for on-body processing of health data with artificial intelligence. Matter 5, 3375–3390 (2022).

    Article  CAS  Google Scholar 

  94. Melianas, A. et al. Temperature-resilient solid-state organic artificial synapses for neuromorphic computing. Sci. Adv. 6, eabb2958 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  95. Keene, S. T. et al. Optimized pulsed write schemes improve linearity and write speed for low-power organic neuromorphic devices. J. Phys. D Appl. Phys. 51, 224002 (2018).

    Article  ADS  Google Scholar 

  96. Yao, X. et al. Protonic solid-state electrochemical synapse for physical neural networks. Nat. Commun. 11, 3134 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  97. Giovannitti, A. et al. Energetic control of redox-active polymers toward safe organic bioelectronic materials. Adv. Mater. 32, 1908047 (2020).

    Article  CAS  Google Scholar 

  98. Keene, S. T. et al. Efficient electronic tunneling governs transport in conducting polymer-insulator blends. J. Am. Chem. Soc. 144, 10368–10376 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Kazemzadeh, S., Dodsworth, L., Pereira, I. F. & van de Burgt, Y. Polydopamine-based all solid-state flexible organic neuromorphic devices for access device-free artificial neural networks. Adv. Electron. Mater. 9, 2200427 (2023).

    Article  CAS  Google Scholar 

  100. Wang, S. et al. An organic electrochemical transistor for multi-modal sensing, memory and processing. Nat. Electron. 6, 281–291 (2023).

    Article  CAS  Google Scholar 

  101. Keene, S. T., Melianas, A., van de Burgt, Y. & Salleo, A. Mechanisms for enhanced state retention and stability in redox-gated organic neuromorphic devices. Adv. Electron. Mater. 5, 1800686 (2019).

    Article  Google Scholar 

  102. Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Adv. Mater. 32, 2003984 (2020).

    Article  CAS  Google Scholar 

  103. Izhikevich, E. M. Which model to use for cortical spiking neurons? IEEE Trans. Neural Netw. 15, 1063–1070 (2004).

    Article  PubMed  Google Scholar 

  104. Yi, W. et al. Biological plausibility and stochasticity in scalable VO2 active memristor neurons. Nat. Commun. 9, 4661 (2018).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  105. Beck, M. E. et al. Spiking neurons from tunable Gaussian heterojunction transistors. Nat. Commun. 11, 1565 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  106. Mirshojaeian Hosseini, M. J. et al. Organic electronics Axon-Hillock neuromorphic circuit: towards biologically compatible, and physically flexible, integrate-and-fire spiking neural networks. J. Phys. D Appl. Phys. 54, 104004 (2021).

    Article  ADS  Google Scholar 

  107. Yang, C.-Y. et al. Low-power/high-gain flexible complementary circuits based on printed organic electrochemical transistors. Adv. Electron. Mater. 8, 2100907 (2022).

    Article  CAS  Google Scholar 

  108. Calvet, L. E., Renard, O. & Hepburn, C. Spiking sensory neurons for analyzing electrophysiological data. ECS J. Solid State Sci. Technol. 9, 115004 (2020).

    Article  ADS  CAS  Google Scholar 

  109. Kim, S.-M. et al. Influence of PEDOT:PSS crystallinity and composition on electrochemical transistor performance and long-term stability. Nat. Commun. 9, 3858 (2018).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  110. Nawaz, A., Liu, Q., Leong, W. L., Fairfull-Smith, K. E. & Sonar, P. Organic electrochemical transistors for in vivo bioelectronics. Adv. Mater. 33, 2101874 (2021).

    Article  CAS  Google Scholar 

  111. Koklu, A. et al. Organic bioelectronic devices for metabolite sensing. Chem. Rev. 122, 4581–4635 (2022).

    Article  CAS  PubMed  Google Scholar 

  112. Marks, A., Griggs, S., Gasparini, N. & Moser, M. Organic electrochemical transistors: an emerging technology for biosensing. Adv. Mater. Interfaces 9, 2102039 (2022).

    Article  CAS  Google Scholar 

  113. Yao, P. et al. Fully hardware-implemented memristor convolutional neural network. Nature 577, 641–646 (2020).

    Article  ADS  CAS  PubMed  Google Scholar 

  114. Li, C. et al. Long short-term memory networks in memristor crossbar arrays. Nat. Mach. Intell. 1, 49–57 (2019).

    Article  Google Scholar 

  115. Felder, D., Muche, K., Linkhorst, J. & Wessling, M. Reminding forgetful organic neuromorphic device networks. Neuromorphic Comput. Eng. 2, 044014 (2022).

    Article  Google Scholar 

  116. Wan, W. et al. A compute-in-memory chip based on resistive random-access memory. Nature 608, 504–512 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  117. Koutsouras, D. A. et al. An iontronic multiplexer based on spatiotemporal dynamics of multiterminal organic electrochemical transistors. Adv. Funct. Mater. 31, 2011013 (2021).

    Article  CAS  Google Scholar 

  118. Koizumi, Y. et al. Electropolymerization on wireless electrodes towards conducting polymer microfibre networks. Nat. Commun. 7, 10404 (2016).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  119. Tommasini, G. et al. Seamless integration of bioelectronic interface in an animal model via in vivo polymerization of conjugated oligomers. Bioact. Mater. 10, 107–116 (2022).

    CAS  PubMed  Google Scholar 

  120. Gerasimov, J. Y. et al. A biologically interfaced evolvable organic pattern classifier. Adv. Sci. 10, 2207023 (2023).

    Article  CAS  Google Scholar 

  121. Pecqueur, S. et al. Neuromorphic time-dependent pattern classification with organic electrochemical transistor arrays. Adv. Electron. Mater. 4, 1800166 (2018).

    Article  Google Scholar 

  122. Petrauskas, L. et al. Nonlinear behavior of dendritic polymer networks for reservoir computing. Adv. Electron. Mater. 8, 2100330 (2022).

    Article  CAS  Google Scholar 

  123. Janzakova, K. et al. Analog programming of conducting-polymer dendritic interconnections and control of their morphology. Nat. Commun. 12, 6898 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  124. Boahen, K. Dendrocentric learning for synthetic intelligence. Nature 612, 43–50 (2022).

    Article  ADS  CAS  PubMed  Google Scholar 

  125. John, R. A. et al. Self healable neuromorphic memtransistor elements for decentralized sensory signal processing in robotics. Nat. Commun. 11, 4030 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  126. Strand, E. J. et al. Multimodal operation of printed electrochemical transistors for sensing in controlled environment agriculture. Sens. Actuators B Chem. 387, 133763 (2023).

    Article  CAS  Google Scholar 

  127. Strand, E. J. et al. Printed organic electrochemical transistors for detecting nutrients in whole plant sap. Adv. Electron. Mater. 8, 2100853 (2022).

    Article  CAS  Google Scholar 

  128. Adamatzky, A. & Kendon, V. in From Astrophysics to Unconventional Computation: Essays Presented to Susan Stepney on the Occasion of Her 60th Birthday (eds Adamatzky, A. & Kendon, V.) 311–335 (Springer, 2020).

  129. Lubrano, C., Bruno, U., Ausilio, C. & Santoro, F. Supported lipid bilayers coupled to organic neuromorphic devices modulate short-term plasticity in biomimetic synapses. Adv. Mater. 34, 2110194 (2022).

    Article  CAS  Google Scholar 

  130. Wang, T. et al. A chemically mediated artificial neuron. Nat. Electron. 5, 586–595 (2022).

    Article  CAS  Google Scholar 

  131. Lee, Y. et al. A low-power stretchable neuromorphic nerve with proprioceptive feedback. Nat. Biomed. Eng. 7, 511–519 (2023).

    Article  PubMed  Google Scholar 

  132. Wang, W. et al. Neuromorphic sensorimotor loop embodied by monolithically integrated, low-voltage, soft e-skin. Science 380, 735–742 (2023).

    Article  ADS  CAS  PubMed  Google Scholar 

  133. Kim, Y. et al. A bioinspired flexible organic artificial afferent nerve. Science 360, 998–1003 (2018).

    Article  ADS  CAS  PubMed  Google Scholar 

  134. Bruno, U. et al. From neuromorphic to neurohybrid: transition from the emulation to the integration of neuronal networks. Neuromorphic Comput. Eng. 3, 023002 (2023).

    Article  Google Scholar 

  135. Dijk, G., Kaszas, A., Pas, J. & O’Connor, R. P. Fabrication and in vivo 2-photon microscopy validation of transparent PEDOT:PSS microelectrode arrays. Microsyst. Nanoeng. 8, 90 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  136. Takemoto, A. et al. Fully transparent, ultrathin flexible organic electrochemical transistors with additive integration for bioelectronic applications. Adv. Sci. 10, 2204746 (2023).

    Article  CAS  Google Scholar 

  137. Tang, X., Shen, H., Zhao, S., Li, N. & Liu, J. Flexible brain–computer interfaces. Nat. Electron. 6, 109–118 (2023).

    Article  Google Scholar 

  138. Xu, Y. et al. Recent advances in flexible organic synaptic transistors. Adv. Electron. Mater. 7, 2100336 (2021).

    Article  CAS  Google Scholar 

  139. Mariano, A. et al. Advances in cell-conductive polymer biointerfaces and role of the plasma membrane. Chem. Rev. 122, 4552–4580 (2022).

    Article  CAS  PubMed  Google Scholar 

  140. De Salvo, A. et al. Organic electronics circuitry for in situ real-time processing of electrophysiological signals. Adv. Mater. Interfaces https://doi.org/10.1002/admi.202300583 (2023).

  141. Tzouvadaki, I., Gkoupidenis, P., Vassanelli, S., Wang, S. & Prodromakis, T. Interfacing biology and electronics with memristive materials. Adv. Mater. 35, 2210035 (2023).

    Article  CAS  Google Scholar 

  142. Farahany, N. A. The Battle for Your Brain: Defending the Right to Think Freely in the Age of Neurotechnology 1st edn (St. Martin’s, 2023).

  143. Chen, S., Tan, M. W. M., Gong, X. & Lee, P. S. Low‐voltage soft actuators for interactive human–machine interfaces. Adv. Intell. Syst. 4, 2100075 (2022).

    Article  Google Scholar 

  144. Paulsen, B. D., Tybrandt, K., Stavrinidou, E. & Rivnay, J. Organic mixed ionic–electronic conductors. Nat. Mater. 19, 13–26 (2020).

    Article  ADS  CAS  PubMed  Google Scholar 

  145. Keene, S. T. et al. Hole-limited electrochemical doping in conjugated polymers. Nat. Mater. 22, 1121–1127 (2023).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  146. Moro, S. et al. The effect of glycol side chains on the assembly and microstructure of conjugated polymers. ACS Nano 16, 21303–21314 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. Samson, E., Marchand, J. & Snyder, K. A. Calculation of ionic diffusion coefficients on the basis of migration test results. Mater. Struct. 36, 156–165 (2003).

    Article  CAS  Google Scholar 

  148. Modarresi, M., Franco-Gonzalez, J. F. & Zozoulenko, I. Morphology and ion diffusion in PEDOT:Tos. A coarse grained molecular dynamics simulation. Phys. Chem. Chem. Phys. 20, 17188–17198 (2018).

    Article  CAS  PubMed  Google Scholar 

  149. Collins, S. D. et al. Observing ion motion in conjugated polyelectrolytes with Kelvin probe force microscopy. Adv. Electron. Mater. 3, 1700005 (2017).

    Article  ADS  Google Scholar 

Download references

Acknowledgements

P.G. acknowledges financial support from the Carl‐Zeiss Foundation (Emergent AI Center, JGU Mainz). H.K. acknowledges financial support from the Bundesministerium für Bildung und Forschung (BMBF) within the project ”BAYOEN” (grant agreement no. 01IS21089) and the European Commission through the project BAYFLEX (grant agreement no. 101099555). F.S. acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (BRAIN-ACT, grant agreement no. 949478). Y.v.d.B. acknowledges funding from the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement no. 802615).

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Authors and Affiliations

  1. Department of Molecular Electronics, Max Planck Institute for Polymer Research, Mainz, Germany

    P. Gkoupidenis

  2. The Institute of Flexible Electronics (IFE, Future Technologies), Xiamen University, Xiamen, China

    Y. Zhang

  3. Dresden Integrated Center for Applied Physics and Photonic Materials (IAPP), TU Dresden, Dresden, Germany

    H. Kleemann

  4. State Key Laboratory of Organic Electronics and Information Displays, Institute of Advanced Materials, Nanjing University of Posts & Telecommunications, Nanjing, China

    H. Ling

  5. Institute of Biological Information Processing – Bioelectronics (IBI-3), Forschungszentrum Juelich, Juelich, Germany

    F. Santoro

  6. Laboratory of Organic Electronics, Department of Science and Technology, Linköping University, Norrköping, Sweden

    S. Fabiano

  7. Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA

    A. Salleo

  8. Microsystems, Department of Mechanical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, Netherlands

    Y. van de Burgt

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  1. P. Gkoupidenis
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  6. S. Fabiano
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The authors contributed equally to the preparation of this article.

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Glossary

Accumulation mode transistor

Type of field-effect transistor that is normally off and requires a positive gate–source voltage to control or enhance its conductivity.

Axon-Hillock neuron

Simplified representation used in computational neuroscience to simulate the behaviour of a neuron, focusing on the integration of incoming signals and the initiation of action potentials at the axon hillock region.

Backpropagation algorithm

Supervised learning technique used in artificial neural networks, which involves iteratively adjusting the internal parameters of a network by propagating error information backward through the network to minimize the difference between predicted and actual outputs.

Depletion mode transistor

A type of field-effect transistor that is normally conducting in the absence of an applied voltage and requires a negative gate–source voltage to control or reduce its conductivity.

Edge computing

Distributed computing paradigm that involves processing data closer to the source of data generation such as in IoT devices or sensors, rather than relying solely on centralized cloud servers, to reduce latency and improve real-time data processing capabilities.

Hodgkin–Huxley model

Comprehensive mathematical model used in neuroscience to describe the complex electrical behaviour of biological neurons, taking into account the dynamics of ion channels and membrane potential, and providing insights into the generation of action potentials.

Integrate-and-fire model

Simplified computational model in neuroscience that accumulates incoming electrical signals (inputs) and generates an action potential (output) when the accumulated signal surpasses a certain threshold, mimicking the basic firing behaviour of biological neurons.

Long-term synaptic plasticity

Persistent changes in the strength and efficacy of synaptic connections between neurons, typically lasting from minutes to indefinitely, and is often associated with processes such as long-term potentiation and depression.

Membrane potential

Voltage difference across the cell membrane of a neuron or other cell, resulting from differences in ion concentrations inside and outside the cell, which has a crucial role in the electrical excitability and communication of a cell.

Morris–Lecar neuron

Mathematical model that describes the behaviour of a simplified biological neuron, particularly in the context of the membrane potential dynamics and ion channel conductance, allowing for the study of neuronal excitability and spiking patterns.

Neural features

Different firing modes of neurons including regular, phasing, stochastic firing and excitability.

Perceptrons

Simplified models of a biological neuron, used in machine learning as a basic unit for binary classification, in which it takes a set of input values, applies weights to them and produces an output based on whether the weighted sum exceeds a certain threshold.

Rail-to-rail switching

The ability of an electronic device or component, such as an operational amplifier, to operate and output signals with minimal distortion or clipping while covering the full range of its power supply voltage.

Reinforcement learning

Machine learning paradigm in which an agent learns to make sequential decisions by interacting with an environment, aiming to maximize a cumulative reward signal through a trial-and-error process.

Sensorimotor integration

A process by which sensory information is received, processed and used to plan and execute motor actions, enabling organisms to perceive and respond to their environment effectively.

Short-term synaptic plasticity

Transient changes in the strength of synaptic connections between neurons that occur over a relatively brief period, typically milliseconds to seconds, and can involve either facilitation or depression of synaptic transmission.

Synaptogenesis

A process by which new synapses are formed between neurons in the developing nervous system, allowing for the establishment of neural circuits and networks.

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Gkoupidenis, P., Zhang, Y., Kleemann, H. et al. Organic mixed conductors for bioinspired electronics. Nat Rev Mater 9, 134–149 (2024). https://doi.org/10.1038/s41578-023-00622-5

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