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Organic electronics for neuromorphic computing


Neuromorphic computing could address the inherent limitations of conventional silicon technology in dedicated machine learning applications. Recent work on silicon-based asynchronous spiking neural networks and large crossbar arrays of two-terminal memristive devices has led to the development of promising neuromorphic systems. However, delivering a compact and efficient parallel computing technology that is capable of embedding artificial neural networks in hardware remains a significant challenge. Organic electronic materials offer an attractive option for such systems and could provide biocompatible and relatively inexpensive neuromorphic devices with low-energy switching and excellent tunability. Here, we review the development of organic neuromorphic devices. We consider different resistance-switching mechanisms, which typically rely on electrochemical doping or charge trapping, and report approaches that enhance state retention and conductance tuning. We also discuss the challenges the field faces in implementing low-power neuromorphic computing, such as device downscaling and improving device speed. Finally, we highlight early demonstrations of device integration into arrays, and consider future directions and potential applications of this technology.

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Fig. 1: Overview of conductance switching mechanisms in organic electronic materials.
Fig. 2: Conductance tuning methods for electrolyte-gated redox-based neuromorphic devices.

reproduced from ref. 84, Springer Nature Ltd.

Fig. 3: Non-volatility in electrolyte-gated redox-based neuromorphic devices.
Fig. 4: Examples of integration and functionality.

reproduced from ref. 105, Springer Nature Ltd (a); ref. 103, Springer Nature Ltd (b); ref. 89, Springer Nature Ltd (c); ref. 79, Elsevier (d); and ref. 109, AAAS (e)


  1. 1.

    Abbott, L. F. & Regehr, W. G. Synaptic computation. Nature 431, 796–803 (2004).

    Google Scholar 

  2. 2.

    Hebb, D. O. The Organization of Behavior: A Neuropsychological Theory. (Wiley, New York, 1949).

    Google Scholar 

  3. 3.

    Mead, C. Neuromorphic electronic systems. Proc. IEEE 78, 1629–1636 (1990). This article contains the first mention of the term neuromorphic systems.

    Google Scholar 

  4. 4.

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

    Google Scholar 

  5. 5.

    Qiao, N. et al. A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses. Front. Neurosci. 9, 141 (2015).

    Google Scholar 

  6. 6.

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

    Google Scholar 

  7. 7.

    Merolla, P. A. et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345, 668–673 (2014).

    Google Scholar 

  8. 8.

    Benjamin, B. V. et al. Neurogrid: A mixed-analog-digital multichip system for large-scale neural simulations. Proc. IEEE 102, 699–716 (2014).

    Google Scholar 

  9. 9.

    Davies, M. et al. Loihi: A neuromorphic manycore processor with on-chip learning. IEEE Micro 38, 82–99 (2018).

    Google Scholar 

  10. 10.

    Chua, L. O. Memristor — The missing circuit element. IEEE T. Circuit Theory 18, 507–519 (1971). This article reports the first theoretical description of the memristor.

    Google Scholar 

  11. 11.

    Strukov, D. B., Snider, G. S., Stewart, D. R. & Williams, R. S. The missing memristor found. Nature 453, 80–83 (2008).

    Google Scholar 

  12. 12.

    Miao, H. et al. Memristor‐based analog computation and neural network classification with a dot product engine. Adv. Mater. 30, 1705914 (2018).

    Google Scholar 

  13. 13.

    Yang, J. J., Strukov, D. B. & Stewart, D. R. Memristive devices for computing. Nat. Nanotech. 8, 13–24 (2013).

    Google Scholar 

  14. 14.

    Simmons, J. G. & Verderber, R. R. New thin-film resistive memory. Radio Electron. Eng. 34, 81–89 (1967). This article reports the first experimental demonstration of a non-volatile analogue memory.

    Google Scholar 

  15. 15.

    Oxley, D. P. Electroforming, switching and memory effects in oxide thin films. Electro. Sci. Tech. 3, 217–224 (1977).

    Google Scholar 

  16. 16.

    Swaroop, B., West, W. C., Martinez, G., Kozicki, M. N. & Akers, L. A. Programmable current mode Hebbian learning neural network using programmable metallization cell. Proc. 1998 IEEE Int. Symp. Circuits Syst. 3, 33–36 (1998).

    Google Scholar 

  17. 17.

    Alibart, F., Zamanidoost, E. & Strukov, D. B. Pattern classification by memristive crossbar circuits using ex situ and in situ training. Nat. Commun. 4, 2072 (2013).

    Google Scholar 

  18. 18.

    Prezioso, M. et al. Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature 521, 61–64 (2015).

    Google Scholar 

  19. 19.

    Chang, T., Jo, S.-H. & Lu, W. Short-term memory to long-term memory transition in a nanoscale memristor. ACS Nano 5, 7669–7676 (2011).

    Google Scholar 

  20. 20.

    Wang, Z. et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nat. Mater. 16, 101–108 (2017).

    Google Scholar 

  21. 21.

    Kuzum, D., Yu, S. & Wong, H.-S. P. Synaptic electronics: materials, devices and applications. Nanotechnology 24, 382001 (2013).

    Google Scholar 

  22. 22.

    Agarwal, S. et al. Resistive memory device requirements for a neural algorithm accelerator. in 2016 Int. Joint Conf. Neural Networks 929–938 (2016).

  23. 23.

    Jeong, D. S., Kim, K. M., Kim, S., Choi, B. J. & Hwang, C. S. Memristors for energy-efficient new computing paradigms. Adv. Electron. Mater. 2, 1600090 (2016).

    Google Scholar 

  24. 24.

    Someya, T., Bao, Z. & Malliaras, G. G. The rise of plastic bioelectronics. Nature 540, 379–385 (2016).

    Google Scholar 

  25. 25.

    Gregor, L. V. Electrical conductivity of polydivinylbenzene films. Thin Solid Films 2, 235–246 (1968).

    Google Scholar 

  26. 26.

    Carchano, H., Lacoste, R. & Segui, Y. Bistable electrical switching in polymer thin films. Appl. Phys. Lett. 19, 414–415 (1971).

    Google Scholar 

  27. 27.

    Potember, R. S., Poehler, T. O. & Cowan, D. O. Electrical switching and memory phenomena in Cu‐TCNQ thin films. Appl. Phys. Lett. 34, 405–407 (1979).

    Google Scholar 

  28. 28.

    Gao, H. J. et al. Reversible, nanometer-scale conductance transitions in an organic complex. Phys. Rev. Lett. 84, 1780–1783 (2000).

    Google Scholar 

  29. 29.

    Ma, L. P., Liu, J. & Yang, Y. Organic electrical bistable devices and rewritable memory cells. Appl. Phys. Lett. 80, 2997–2999 (2002).

    Google Scholar 

  30. 30.

    Ma, L., Xu, Q. & Yang, Y. Organic nonvolatile memory by controlling the dynamic copper-ion concentration within organic layer. Appl. Phys. Lett. 84, 4908–4910 (2004).

    Google Scholar 

  31. 31.

    Henisch, H. K. & Smith, W. R. Switching in organic polymer films. Appl. Phys. Lett. 24, 589–591 (1974).

    Google Scholar 

  32. 32.

    Tondelier, D., Lmimouni, K., Vuillaume, D., Fery, C. & Haas, G. Metal/organic/metal bistable memory devices. Appl. Phys. Lett. 85, 5763–5765 (2004).

    Google Scholar 

  33. 33.

    Asadi, K., de Leeuw, D. M., de Boer, B. & Blom, P. W. M. Organic non-volatile memories from ferroelectric phase-separated blends. Nat. Mater. 7, 547–550 (2008).

    Google Scholar 

  34. 34.

    Naber, R. C. G., Asadi, K., Blom, P. W. M., de Leeuw, D. M. & de Boer, B. Organic nonvolatile memory devices based on ferroelectricity. Adv. Mater. 22, 933–945 (2010).

    Google Scholar 

  35. 35.

    Kamitsos, E. I., Tzinis, C. H. & Risen, W. M. Raman study of the mechanism of electrical switching in Cu TCNQ films. Solid State Commun. 42, 561–565 (1982).

    Google Scholar 

  36. 36.

    Scott, J. C. & Bozano, L. D. Nonvolatile memory elements based on organic materials. Adv. Mater. 19, 1452–1463 (2007).

    Google Scholar 

  37. 37.

    Ling, Q.-D. et al. Polymer electronic memories: Materials, devices and mechanisms. Prog. Polym. Sci. 33, 917–978 (2008).

    Google Scholar 

  38. 38.

    Heremans, P. et al. Polymer and organic nonvolatile memory devices. Chem. Mater. 23, 341–358 (2011).

    Google Scholar 

  39. 39.

    Cho, B., Song, S., Ji, Y., Kim, T.-W. & Lee, T. Organic resistive memory devices: Performance enhancement, integration, and advanced architectures. Adv. Funct. Mater. 21, 2806–2829 (2011).

    Google Scholar 

  40. 40.

    Yu, S. et al. Stochastic learning in oxide binary synaptic device for neuromorphic computing. Front. Neurosci. 7, 186 (2013).

    Google Scholar 

  41. 41.

    Shibata, T. & Ohmi, T. Neural microelectronics. in Int. Electron. Dev. Meet. Tech. Digest 337–342 (1997).

  42. 42.

    Jo, S. H. et al. Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett. 10, 1297–1301 (2010).

    Google Scholar 

  43. 43.

    Möller, S., Perlov, C., Jackson, W., Taussig, C. & Forrest, S. R. A polymer/semiconductor write-once read-many-times memory. Nature 426, 166–169 (2003).

    Google Scholar 

  44. 44.

    Kaneto, K., Asano, T. & Takashima, W. Memory device using a conducting polymer and solid polymer electrolyte. Jpn J. Appl. Phys. 30, L215–L217 (1991). This article reports the first demonstration of hybrid electronic/ionic switching in a conducting-polymer-based non-volatile memory device.

    Google Scholar 

  45. 45.

    Nilsson, D. et al. Bi‐stable and dynamic current modulation in electrochemical organic transistors. Adv. Mater. 14, 51–54 (2002).

    Google Scholar 

  46. 46.

    Erokhin, V., Berzina, T. & Fontana, M. P. Hybrid electronic device based on polyaniline-polyethyleneoxide junction. J. Appl. Phys. 97, 064501 (2005).

    Google Scholar 

  47. 47.

    Gkoupidenis, P., Schaefer, N., Garlan, B. & Malliaras, G. G. Neuromorphic functions in PEDOT:PSS organic electrochemical transistors. Adv. Mater. 27, 7176–7180 (2015). This article demonstrates synaptic functionality in an electrochemically gated conducting polymer device.

    Google Scholar 

  48. 48.

    Kumar, R., Pillai, R. G., Pekas, N., Wu, Y. & McCreery, R. L. Spatially resolved Raman spectroelectrochemistry of solid-state polythiophene/viologen memory devices. J. Am. Chem. Soc. 134, 14869–14876 (2012).

    Google Scholar 

  49. 49.

    Qian, C. et al. Artificial synapses based on in-plane gate organic electrochemical transistors. ACS Appl. Mater. Inter. 8, 26169–26175 (2016).

    Google Scholar 

  50. 50.

    Kong, L. et al. Long-term synaptic plasticity simulated in ionic liquid/polymer hybrid electrolyte gated organic transistors. Org. Electron. 47, 126–132 (2017).

    Google Scholar 

  51. 51.

    Xu, W., Min, S.-Y., Hwang, H. & Lee, T.-W. Organic core-sheath nanowire artificial synapses with femtojoule energy consumption. Sci. Adv. 2, e1501326 (2016). This article shows an artificial synapse that switches at femtojoule energy consumption.

    Google Scholar 

  52. 52.

    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).

    Google Scholar 

  53. 53.

    Das, B. C., Szeto, B., James, D. D., Wu, Y. & McCreery, R. L. Ion transport and switching speed in redox-gated 3-terminal organic memory devices. J. Electrochem. Soc. 161, H831–H838 (2014).

    Google Scholar 

  54. 54.

    Liu, G. et al. Organic biomimicking memristor for information storage and processing applications. Adv. Electron. Mater. 2, 1500298 (2016).

    Google Scholar 

  55. 55.

    Novembre, C., Guérin, D., Lmimouni, K., Gamrat, C. & Vuillaume, D. Gold nanoparticle-pentacene memory transistors. Appl. Phys. Lett. 92, 103314 (2008).

    Google Scholar 

  56. 56.

    Ouyang, J., Chu, C.-W., Szmanda, C. R., Ma, L. & Yang, Y. Programmable polymer thin film and non-volatile memory device. Nat. Mater. 3, 918–922 (2004). This article demonstrates the first solution-processed bistable organic memory device based on charge storage.

    Google Scholar 

  57. 57.

    Bozano, L. D., Kean, B. W., Deline, V. R., Salem, J. R. & Scott, J. C. Mechanism for bistability in organic memory elements. Appl. Phys. Lett. 84, 607–609 (2004).

    Google Scholar 

  58. 58.

    Son, D. I., You, C. H., Kim, W. T., Jung, J. H. & Kim, T. W. Electrical bistabilities and memory mechanisms of organic bistable devices based on colloidal ZnO quantum dot-polymethylmethacrylate polymer nanocomposites. Appl. Phys. Lett. 94, 132103 (2009).

    Google Scholar 

  59. 59.

    Zhou, Y., Han, S., Sonar, P. & Roy, V. A. L. Nonvolatile multilevel data storage memory device from controlled ambipolar charge trapping mechanism. Sci. Rep. 3, 2319 (2013).

    Google Scholar 

  60. 60.

    Kim, C.-H., Sung, S. & Yoon, M.-H. Synaptic organic transistors with a vacuum-deposited charge-trapping nanosheet. Sci. Rep. 6, srep33355 (2016).

    Google Scholar 

  61. 61.

    Alibart, F. et al. An organic nanoparticle transistor behaving as a biological spiking synapse. Adv. Funct. Mater. 20, 330–337 (2010).

    Google Scholar 

  62. 62.

    Alibart, F. et al. A memristive nanoparticle/organic hybrid synapstor for neuroinspired computing. Adv. Funct. Mater. 22, 609–616 (2012).

    Google Scholar 

  63. 63.

    Valov, I. & Kozicki, M. Non-volatile memories: Organic memristors come of age. Nat. Mater. 16, 1170–1172 (2017).

    Google Scholar 

  64. 64.

    Burr, G. W. et al. Phase change memory technology. J. Vac. Sci. Technol. B 28, 223–262 (2010).

    Google Scholar 

  65. 65.

    Agarwal, S. et al. Designing an analog crossbar based neuromorphic accelerator. in 2017 5th Berkeley Symp. on Energy Efficient Electronic Systems Steep Transistors Workshop (E3S) 1–3 (2017).

  66. 66.

    Zhang, T. et al. Negative differential resistance, memory, and reconfigurable logic functions based on monolayer devices derived from gold nanoparticles functionalized with electropolymerizable TEDOT units. J. Phys. Chem. C. 121, 10131–10139 (2017).

    Google Scholar 

  67. 67.

    Lai, Q. et al. Ionic/electronic hybrid materials integrated in a synaptic transistor with signal processing and learning functions. Adv. Mater. 22, 2448–2453 (2010).

    Google Scholar 

  68. 68.

    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).

    Google Scholar 

  69. 69.

    Demin, V. A. et al. Hardware elementary perceptron based on polyaniline memristive devices. Org. Electron. 25, 16–20 (2015).

    Google Scholar 

  70. 70.

    Smerieri, A., Berzina, T., Erokhin, V. & Fontana, M. P. Polymeric electrochemical element for adaptive networks: Pulse mode. J. Appl. Phys. 104, 114513 (2008).

    Google Scholar 

  71. 71.

    Xuan, Y., Sandberg, M., Berggren, M. & Crispin, X. An all-polymer-air PEDOT battery. Org. Electron. 13, 632–637 (2012).

    Google Scholar 

  72. 72.

    Zeng, F., Li, S., Yang, J., Pan, F. & Guo, D. Learning processes modulated by the interface effects in a Ti/conducting polymer/Ti resistive switching cell. RSC Adv. 4, 14822–14828 (2014).

    Google Scholar 

  73. 73.

    Leydecker, T. et al. Flexible non-volatile optical memory thin-film transistor device with over 256 distinct levels based on an organic bicomponent blend. Nat. Nanotech. 11, 769–775 (2016).

    Google Scholar 

  74. 74.

    Tan, H. et al. Light-gated memristor with integrated logic and memory functions. ACS Nano 11, 11298–11305 (2017).

    Google Scholar 

  75. 75.

    Burr, G. W. et al. Access devices for 3D crosspoint memory. J. Vac. Sci. Technol. B 32, 040802 (2014).

    Google Scholar 

  76. 76.

    Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagating errors. Nature 323, 533–536 (1986).

    MATH  Google Scholar 

  77. 77.

    Lapkin, D. A., Emelyanov, A. V., Demin, V. A., Berzina, T. S. & Erokhin, V. V. Spike-timing-dependent plasticity of polyaniline-based memristive element. Microelectron. Eng. 185–186, 43–47 (2018).

    Google Scholar 

  78. 78.

    Li, S. Z. et al. Synaptic plasticity and learning behaviours mimicked through Ag interface movement in an Ag/conducting polymer/Ta memristive system. J. Mater. Chem. C. 1, 5292–5298 (2013).

    Google Scholar 

  79. 79.

    Desbief, S. et al. Electrolyte-gated organic synapse transistor interfaced with neurons. Org. Electron. 38, 21–28 (2016).

    Google Scholar 

  80. 80.

    Ohno, T. et al. Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. Nat. Mater. 10, 591–595 (2011).

    Google Scholar 

  81. 81.

    Sekitani, T. et al. Organic nonvolatile memory transistors for flexible sensor arrays. Science 326, 1516–1519 (2009).

    Google Scholar 

  82. 82.

    Nawrocki, R. A. et al. An inverted, organic WORM device based on PEDOT:PSS with very low turn-on voltage. Org. Electron. 15, 1791–1798 (2014).

    Google Scholar 

  83. 83.

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

    Google Scholar 

  84. 84.

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

    Google Scholar 

  85. 85.

    Winther-Jensen, B., Kolodziejczyk, B. & Winther-Jensen, O. New one-pot poly(3,4-ethylenedioxythiophene): poly(tetrahydrofuran) memory material for facile fabrication of memory organic electrochemical transistors. APL Mater. 3, 014903 (2014).

    Google Scholar 

  86. 86.

    Fabiano, S. et al. Ferroelectric polarization induces electronic nonlinearity in ion-doped conducting polymers. Sci. Adv. 3, e1700345 (2017).

    Google Scholar 

  87. 87.

    Lapkin, D. A. et al. Polyaniline-based memristive microdevice with high switching rate and endurance. Appl. Phys. Lett. 112, 043302 (2018).

    Google Scholar 

  88. 88.

    Erokhin, V., Berzina, T., Camorani, P. & Fontana, M. P. On the stability of polymeric electrochemical elements for adaptive networks. Colloid Surf. A 321, 218–221 (2008).

    Google Scholar 

  89. 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). This article demonstrates a three-dimensional integrated artificial synapse network.

    Google Scholar 

  90. 90.

    Xiao, Z. & Huang, J. Energy-efficient hybrid perovskite memristors and synaptic devices. Adv. Electron. Mater. 2, 1600100 (2016).

    Google Scholar 

  91. 91.

    Kang, S. H., Crisp, T., Kymissis, I. & Bulović, V. Memory effect from charge trapping in layered organic structures. Appl. Phys. Lett. 85, 4666–4668 (2004).

    Google Scholar 

  92. 92.

    Lin, H. T., Pei, Z. & Chan, Y. J. Carrier transport mechanism in a nanoparticle-incorporated organic bistable memory device. IEEE Electron. Dev. Lett. 28, 569–571 (2007).

    Google Scholar 

  93. 93.

    Desbief, S. et al. Low voltage and time constant organic synapse-transistor. Org. Electron. 21, 47–53 (2015).

    Google Scholar 

  94. 94.

    Erokhin, V. et al. Stochastic hybrid 3D matrix: Learning and adaptation of electrical properties. J. Mater. Chem. 22, 22881–22887 (2012).

    MathSciNet  Google Scholar 

  95. 95.

    Nawrocki, R. A., Voyles, R. M. & Shaheen, S. E. Neurons in polymer: Hardware neural units based on polymer memristive devices and polymer transistors. IEEE T. Electron. Dev. 61, 3513–3519 (2014).

    Google Scholar 

  96. 96.

    Rosenblatt, F. The perceptron: A probabilistic model for information storage and organization in the brain. Psychol. Rev. 65, 386–408 (1958).

    Google Scholar 

  97. 97.

    Lin, Y.-P. et al. Physical realization of a supervised learning system built with organic memristive synapses. Sci. Rep. 6, 31932 (2016).

    Google Scholar 

  98. 98.

    Erokhin, V. et al. Material memristive device circuits with synaptic plasticity: Learning and memory. BioNanoSci 1, 24–30 (2011).

    Google Scholar 

  99. 99.

    Bichler, O. et al. Pavlov’s dog associative learning demonstrated on synaptic-like organic transistors. Neural Comput. 25, 549–566 (2012).

    Google Scholar 

  100. 100.

    Gkoupidenis, P., Rezaei-Mazinani, S., Proctor, C. M., Ismailova, E. & Malliaras, G. G. Orientation selectivity with organic photodetectors and an organic electrochemical transistor. AIP Adv. 6, 111307 (2016).

    Google Scholar 

  101. 101.

    Gkoupidenis, P., Koutsouras, D. A., Lonjaret, T., Fairfield, J. A. & Malliaras, G. G. Orientation selectivity in a multi-gated organic electrochemical transistor. Sci. Rep. 6, 27007 (2016).

    Google Scholar 

  102. 102.

    Qian, C., Kong, L., Yang, J., Gao, Y. & Sun, J. Multi-gate organic neuron transistors for spatiotemporal information processing. Appl. Phys. Lett. 110, 083302 (2017).

    Google Scholar 

  103. 103.

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

    Google Scholar 

  104. 104.

    Tybrandt, K., Forchheimer, R. & Berggren, M. Logic gates based on ion transistors. Nat. Commun. 3, 1869 (2012).

    Google Scholar 

  105. 105.

    Khodagholy, D. et al. NeuroGrid: Recording action potentials from the surface of the brain. Nat. Neurosci. 18, 310–315 (2015).

    Google Scholar 

  106. 106.

    Rivnay, J., Wang, H., Fenno, L., Deisseroth, K. & Malliaras, G. G. Next-generation probes, particles, and proteins for neural interfacing. Sci. Adv. 3, e1601649 (2017).

    Google Scholar 

  107. 107.

    Simon, D. T. et al. An organic electronic biomimetic neuron enables auto-regulated neuromodulation. Biosens. Bioelectron. 71, 359–364 (2015).

    Google Scholar 

  108. 108.

    Lv, Z., Zhou, Y., Han, S.-T. & Roy, V. A. L. From biomaterial-based data storage to bio-inspired artificial synapse. Mater. Today (February 2018).

  109. 109.

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

    Google Scholar 

  110. 110.

    Simon, D. T., Gabrielsson, E. O., Tybrandt, K. & Berggren, M. Organic bioelectronics: Bridging the signaling gap between biology and technology. Chem. Rev. 116, 13009–13041 (2016).

    Google Scholar 

  111. 111.

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

    Google Scholar 

  112. 112.

    Fuller, E. J. et al. Li-ion synaptic transistor for low power analog computing. Adv. Mater. 29, 1604310 (2016).

    Google Scholar 

  113. 113.

    Chortos, A., Liu, J. & Bao, Z. Pursuing prosthetic electronic skin. Nat. Mater. 15, 937–950 (2016).

    Google Scholar 

  114. 114.

    Berzina, T. et al. Optimization of an organic memristor as an adaptive memory element. J. Appl. Phys. 105, 124515 (2009).

    Google Scholar 

  115. 115.

    Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. in Advances in Neural Information Processing Systems 25 (eds Pereira, F., Burges, C. J. C., Bottou, L. & Weinberger, K. Q.) 1097–1105 (Curran Associates, Red Hook, NY, 2012).

  116. 116.

    Hinton, G. E., Osindero, S. & Teh, Y.-W. A fast learning algorithm for deep belief nets. Neural Comput. 18, 1527–1554 (2006).

    MathSciNet  MATH  Google Scholar 

  117. 117.

    Rumelhart, D. E. & McClelland, J. L., PDP Research Group. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1: Foundations (MIT Press, Cambridge, MA, 1987).

    Google Scholar 

  118. 118.

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

    Google Scholar 

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The authors would like to thank M. Marinella and S. Agarwal from Sandia National Labs for help in preparing this document. A.M. gratefully acknowledges support from the Knut and Alice Wallenberg Foundation (KAW 2016.0494) for postdoctoral research at Stanford University. S.T.K. was funded by the Stanford Graduate Fellowship.

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Y.v.d.B. collected all the articles and data. Y.v.d.B., A.M. and S.K. wrote the manuscript. All authors contributed to the discussion and writing.

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Correspondence to Yoeri van de Burgt or Armantas Melianas.

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van de Burgt, Y., Melianas, A., Keene, S.T. et al. Organic electronics for neuromorphic computing. Nat Electron 1, 386–397 (2018).

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