1932

Abstract

Neuromorphic computing is becoming increasingly prominent as artificial intelligence (AI) facilitates progressively seamless interaction between humans and machines. The conventional von Neumann architecture and complementary metal-oxide-semiconductor transistor scaling are unable to meet the highly demanding computational density and energy efficiency requirements of AI. Neuromorphic computing aims to address these challenges by using brain-like computing architectures and novel synaptic memories that coallocate information storage and computation, thereby enabling low latency at high energy efficiency and high memory density. Though various emerging memory devices have been extensively studied to emulate the functionality of biological synapses, there is currently no material/device system that encompasses both the needed metrics for high-performance neuromorphic computing and the required biocompatibility for potential body-computer integration. In this review, we aim to equip the reader with general design principles and materials requirements for realizing high-performance organic neuromorphic devices. We use instructive examples from recent literature to discuss each requirement, illustrating the challenges as well as future research opportunities. Though organic devices still face many challenges to become major players in neuromorphic computing, mostly due to their lack of compliance with back-end-of-line processes required for integration with digital logic, we propose that their biocompatibility and mechanical conformability give them an advantage for creating adaptive biointerfaces, brain-machine interfaces, and biology-inspired prosthetics.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-matsci-080619-111402
2021-07-26
2024-05-22
Loading full text...

Full text loading...

/deliver/fulltext/matsci/51/1/annurev-matsci-080619-111402.html?itemId=/content/journals/10.1146/annurev-matsci-080619-111402&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    Mead C. 1990. Neuromorphic electronic systems. Proc. IEEE 78:1629–36
    [Google Scholar]
  2. 2. 
    Bliss TVP, Collingridge GL. 1993. A synaptic model of memory: long-term potentiation in the hippocampus. Nature 361:31–39
    [Google Scholar]
  3. 3. 
    Strukov DB, Snider GS, Stewart DR, Williams RS. 2008. The missing memristor found. Nature 453:80–83
    [Google Scholar]
  4. 4. 
    Yang JJ, Strukov DB, Stewart DR. 2013. Memristive devices for computing. Nat. Nanotechnol. 8:13–24
    [Google Scholar]
  5. 5. 
    Jeong DS, Kim KM, Kim S, Choi BJ, Hwang CS. 2016. Memristors for energy-efficient new computing paradigms. Adv. Electron. Mater. 2:1600090
    [Google Scholar]
  6. 6. 
    Sun J, Fu Y, Wan Q 2018. Organic synaptic devices for neuromorphic systems. J. Phys. D Appl. Phys. 51:314004
    [Google Scholar]
  7. 7. 
    Zidan MA, Strachan JP, Lu WD. 2018. The future of electronics based on memristive systems. Nat. Electron. 1:22–29
    [Google Scholar]
  8. 8. 
    Cho B, Song S, Ji Y, Kim T-W, Lee T 2011. Organic resistive memory devices: performance enhancement, integration, and advanced architectures. Adv. Funct. Mater. 21:2806–29
    [Google Scholar]
  9. 9. 
    Hickmott TW. 1962. Low-frequency negative resistance in thin anodic oxide films. J. Appl. Phys. 33:2669–82
    [Google Scholar]
  10. 10. 
    Hickmott TW. 1964. Potential distribution and negative resistance in thin oxide films. J. Appl. Phys. 35:2679–89
    [Google Scholar]
  11. 11. 
    Choi BJ, Torrezan AC, Strachan JP, Kotula PG, Lohn AJ et al. 2016. High-speed and low-energy nitride memristors. Adv. Funct. Mater. 26:5290–96
    [Google Scholar]
  12. 12. 
    Govoreanu B, Kar GS, Chen Y, Paraschiv V, Kubicek S et al. 2011. 10×10nm2 Hf/HfOx crossbar resistive RAM with excellent performance, reliability and low-energy operation. 2011 International Electron Devices Meeting31.6.1–4 Piscataway, NJ: IEEE
    [Google Scholar]
  13. 13. 
    Benjamin BV, Gao P, McQuinn E, Choudhary S, Chandrasekaran AR et al. 2014. Neurogrid: a mixed-analog-digital multichip system for large-scale neural simulations. Proc. IEEE 102:699–716
    [Google Scholar]
  14. 14. 
    Davies M, Srinivasa N, Lin T, Chinya G, Cao Y et al. 2018. Loihi: a neuromorphic manycore processor with on-chip learning. IEEE Micro 38:82–99
    [Google Scholar]
  15. 15. 
    Merolla PA, Arthur JV, Alvarez-Icaza R, Cassidy AS, Sawada J et al. 2014. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345:668–73
    [Google Scholar]
  16. 16. 
    Li S, Zeng F, Chen C, Liu H, Tang G et al. 2013. Synaptic plasticity and learning behaviours mimicked through Ag interface movement in an Ag/conducting polymer/Ta memristive system. J. Mater. Chem. C 1:5292–98
    [Google Scholar]
  17. 17. 
    Wu S, Tsuruoka T, Terabe K, Hasegawa T, Hill JP et al. 2011. A polymer-electrolyte-based atomic switch. Adv. Funct. Mater. 21:93–99
    [Google Scholar]
  18. 18. 
    Park Y, Lee J-S. 2017. Artificial synapses with short- and long-term memory for spiking neural networks based on renewable materials. ACS Nano 11:8962–69
    [Google Scholar]
  19. 19. 
    Wang L, Wang Z, Lin J, Yang J, Xie L et al. 2016. Long-term homeostatic properties complementary to Hebbian rules in CuPc-based multifunctional memristor. Sci. Rep. 6:35273
    [Google Scholar]
  20. 20. 
    Wang H, Meng F, Cai Y, Zheng L, Li Y et al. 2013. Sericin for resistance switching device with multilevel nonvolatile memory. Adv. Mater. 25:5498–503
    [Google Scholar]
  21. 21. 
    Kim DH, Kim WK, Woo SJ, Wu C, Kim TW. 2017. Highly-reproducible nonvolatile memristive devices based on polyvinylpyrrolidone: graphene quantum-dot nanocomposites. Org. Electron. 51:156–61
    [Google Scholar]
  22. 22. 
    van de Burgt Y, Lubberman E, Fuller EJ, Keene ST, Faria GC et al. 2017. A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing. Nat. Mater. 16:414–18
    [Google Scholar]
  23. 23. 
    Gkoupidenis P, Schaefer N, Garlan B, Malliaras GG. 2015. Neuromorphic functions in PEDOT:PSS organic electrochemical transistors. Adv. Mater. 27:7176–80
    [Google Scholar]
  24. 24. 
    Bandyopadhyay A, Sahu S, Higuchi M. 2011. Tuning of nonvolatile bipolar memristive switching in Co(III) polymer with an extended azo aromatic ligand. J. Am. Chem. Soc. 133:1168–71
    [Google Scholar]
  25. 25. 
    Dong WS, Zeng F, Lu SH, Liu A, Li XJ, Pan F. 2015. Frequency-dependent learning achieved using semiconducting polymer/electrolyte composite cells. Nanoscale 7:16880–89
    [Google Scholar]
  26. 26. 
    Yang X, Wang C, Shang J, Zhang C, Tan H et al. 2016. An organic terpyridyl-iron polymer based memristor for synaptic plasticity and learning behavior simulation. RSC Adv 6:25179–84
    [Google Scholar]
  27. 27. 
    Xu W, Cho H, Kim Y-H, Kim Y-T, Wolf C et al. 2016. Organometal halide perovskite artificial synapses. Adv. Mater. 28:5916–22
    [Google Scholar]
  28. 28. 
    Tress W. 2017. Metal halide perovskites as mixed electronic–ionic conductors: challenges and opportunities—from hysteresis to memristivity. J. Phys. Chem. Lett. 8:3106–14
    [Google Scholar]
  29. 29. 
    Fuller EJ, Li Y, Bennet C, Keene ST, Melianas A et al. 2019. Redox transistors for neuromorphic computing. IBM J. Res. Dev. 63:91–9:9
    [Google Scholar]
  30. 30. 
    van de Burgt Y, Melianas A, Keene ST, Malliaras G, Salleo A. 2018. Organic electronics for neuromorphic computing. Nat. Electron. 1:386–97
    [Google Scholar]
  31. 31. 
    Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM et al. 2017. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542:115–18
    [Google Scholar]
  32. 32. 
    Stephansen JB, Olesen AN, Olsen M, Ambati A, Leary EB et al. 2018. Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy. Nat. Commun. 9:5229
    [Google Scholar]
  33. 33. 
    Schwemmer MA, Skomrock ND, Sederberg PB, Ting JE, Sharma G et al. 2018. Meeting brain–computer interface user performance expectations using a deep neural network decoding framework. Nat. Med. 24:1669–76
    [Google Scholar]
  34. 34. 
    Martin SJ, Grimwood PD, Morris RGM. 2000. Synaptic plasticity and memory: an evaluation of the hypothesis. Annu. Rev. Neurosci. 23:649–711
    [Google Scholar]
  35. 35. 
    Whitlock JR, Heynen AJ, Shuler MG, Bear MF. 2006. Learning induces long-term potentiation in the hippocampus. Science 313:1093–97
    [Google Scholar]
  36. 36. 
    Cosemans S, Verhoef B, Doevenspeck J, Papistas IA, Catthoor F et al. 2019. Towards 10000TOPS/W DNN inference with analog in-memory computing—a circuit blueprint, device options and requirements. 2019 IEEE International Electron Devices Meeting (IEDM)22.2.1–4 Piscataway, NJ: IEEE
    [Google Scholar]
  37. 37. 
    Hasegawa T, Ohno T, Terabe K, Tsuruoka T, Nakayama T et al. 2010. Learning abilities achieved by a single solid-state atomic switch. Adv. Mater. 22:1831–34
    [Google Scholar]
  38. 38. 
    Pickett MD, Medeiros-Ribeiro G, Williams RS. 2013. A scalable neuristor built with Mott memristors. Nat. Mater. 12:114–17
    [Google Scholar]
  39. 39. 
    Salinga M, Kersting B, Ronneberger I, Jonnalagadda VP, Vu XT et al. 2018. Monatomic phase change memory. Nat. Mater. 17:681–85
    [Google Scholar]
  40. 40. 
    Goswami S, Matula AJ, Rath SP, Hedström S, Saha S et al. 2017. Robust resistive memory devices using solution-processable metal-coordinated azo aromatics. Nat. Mater. 16:1216–24
    [Google Scholar]
  41. 41. 
    Xu W, Min S-Y, Hwang H, Lee T-W. 2016. Organic core-sheath nanowire artificial synapses with femtojoule energy consumption. Sci. Adv. 2:e1501326
    [Google Scholar]
  42. 42. 
    Tang J, Bishop D, Kim S, Copel M, Gokmen T et al. 2018. ECRAM as scalable synaptic cell for high-speed, low-power neuromorphic computing. 2018 IEEE International Electron Devices Meeting (IEDM)13.1.1–4 Piscataway, NJ: IEEE
    [Google Scholar]
  43. 43. 
    Melianas A, Quill TJ, LeCroy G, Tuchman Y, Loo HV et al. 2020. Temperature-resilient solid-state organic artificial synapses for neuromorphic computing. Sci. Adv. 6:eabb2958
    [Google Scholar]
  44. 44. 
    Fuller EJ, Keene ST, Melianas A, Wang Z, Agarwal S et al. 2019. Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing. Science 364:570–74
    [Google Scholar]
  45. 45. 
    Jang S, Jang S, Lee E-H, Kang M, Wang G, Kim T-W. 2019. Ultrathin conformable organic artificial synapse for wearable intelligent device applications. ACS Appl. Mater. Interfaces 11:1071–80
    [Google Scholar]
  46. 46. 
    Mainzer K 2013. Local activity principle: The cause of complexity and symmetry breaking. Chaos, CNN, Memristors and Beyond A Adamatzky, G Chen 146–59 Singapore: World Sci.
    [Google Scholar]
  47. 47. 
    Agarwal S, Plimpton SJ, Hughart DR, Hsia AH, Richter I et al. 2016. Resistive memory device requirements for a neural algorithm accelerator. 2016 International Joint Conference on Neural Networks929–38 Piscataway, NJ: IEEE
    [Google Scholar]
  48. 48. 
    Yang B, Lu Y, Jiang D, Li Z, Zeng Y et al. 2020. Bioinspired multifunctional organic transistors based on natural chlorophyll/organic semiconductors. Adv. Mater. 32:2001227
    [Google Scholar]
  49. 49. 
    Dai S, Wu X, Liu D, Chu Y, Wang K et al. 2018. Light-stimulated synaptic devices utilizing interfacial effect of organic field-effect transistors. ACS Appl. Mater. Interfaces 10:21472–80
    [Google Scholar]
  50. 50. 
    Wang T-Y, He Z-Y, Chen L, Zhu H, Sun Q-Q et al. 2018. An organic flexible artificial bio-synapses with long-term plasticity for neuromorphic computing. Micromachines 9:239
    [Google Scholar]
  51. 51. 
    Nguyen VC, Lee PS. 2016. Coexistence of write once read many memory and memristor in blend of poly(3,4-ethylenedioxythiophene):polystyrene sulfonate and polyvinyl alcohol. Sci. Rep. 6:38816
    [Google Scholar]
  52. 52. 
    Prudnikov NV, Lapkin DA, Emelyanov AV, Minnekhanov AA, Malakhova YN et al. 2020. Associative STDP-like learning of neuromorphic circuits based on polyaniline memristive microdevices. J. Phys. D Appl. Phys. 53:414001
    [Google Scholar]
  53. 53. 
    Leydecker T, Herder M, Pavlica E, Bratina G, Hecht S et al. 2016. Flexible non-volatile optical memory thin-film transistor device with over 256 distinct levels based on an organic bicomponent blend. Nat. Nanotechnol. 11:769–75
    [Google Scholar]
  54. 54. 
    Keene ST, van der Pol TPA, Zakhidov D, Weijtens CHL, Janssen RAJ et al. 2020. Enhancement-mode PEDOT:PSS organic electrochemical transistors using molecular de-doping. Adv. Mater. 32:2000270
    [Google Scholar]
  55. 55. 
    Keene ST, Melianas A, van de Burgt Y, Salleo A. 2019. Mechanisms for enhanced state retention and stability in redox-gated organic neuromorphic devices. Adv. Electron. Mater. 5:1800686
    [Google Scholar]
  56. 56. 
    Giovannitti A, Rashid RB, Thiburce Q, Paulsen BD, Cendra C et al. 2020. Energetic control of redox-active polymers toward safe organic bioelectronic materials. Adv. Mater. 32:1908047
    [Google Scholar]
  57. 57. 
    Doris SE, Pierre A, Street RA 2018. Dynamic and tunable threshold voltage in organic electrochemical transistors. Adv. Mater. 30:1706757
    [Google Scholar]
  58. 58. 
    Kergoat L, Herlogsson L, Piro B, Pham MC, Horowitz G et al. 2012. Tuning the threshold voltage in electrolyte-gated organic field-effect transistors. PNAS 109:8394–99
    [Google Scholar]
  59. 59. 
    Wu H, Chan G, Choi JW, Ryu I, Yao Y et al. 2012. Stable cycling of double-walled silicon nanotube battery anodes through solid–electrolyte interphase control. Nat. Nanotechnol. 7:310–15
    [Google Scholar]
  60. 60. 
    Pickett MD, Williams RS. 2012. Sub-100 fJ and sub-nanosecond thermally driven threshold switching in niobium oxide crosspoint nanodevices. Nanotechnology 23:215202
    [Google Scholar]
  61. 61. 
    Lanza M, Wong H-SP, Pop E, Ielmini D, Strukov D et al. 2019. Recommended methods to study resistive switching devices. Adv. Electron. Mater. 5:1800143
    [Google Scholar]
  62. 62. 
    Battistoni S, Erokhin V, Iannotta S. 2019. Frequency driven organic memristive devices for neuromorphic short term and long term plasticity. Org. Electron. 65:434–38
    [Google Scholar]
  63. 63. 
    Keene ST, Melianas A, Fuller EJ, van de Burgt Y, Talin AA, Salleo A. 2018. Optimized pulsed write schemes improve linearity and write speed for low-power organic neuromorphic devices. J. Phys. D Appl. Phys. 51:224002
    [Google Scholar]
  64. 64. 
    Fuller EJ, Gabaly FE, Léonard F, Agarwal S, Plimpton SJ et al. 2017. Li-ion synaptic transistor for low power analog computing. Adv. Mater. 29:1604310
    [Google Scholar]
  65. 65. 
    Kim S, Todorov T, Onen M, Gokmen T, Bishop D et al. 2019. Metal-oxide based, CMOS-compatible ECRAM for deep learning accelerator. 2019 IEEE International Electron Devices Meeting (IEDM)35.7.1–4 Piscataway, NJ: IEEE
    [Google Scholar]
  66. 66. 
    Li Y, Fuller EJ, Asapu S, Agarwal S, Kurita T et al. 2019. Low-voltage, CMOS-free synaptic memory based on LiXTiO2 redox transistors. ACS Appl. Mater. Interfaces 11:38982–92
    [Google Scholar]
  67. 67. 
    Giovannitti A, Sbircea D-T, Inal S, Nielsen CB, Bandiello E et al. 2016. Controlling the mode of operation of organic transistors through side-chain engineering. PNAS 113:12017–22
    [Google Scholar]
  68. 68. 
    Giovannitti A, Maria IP, Hanifi D, Donahue MJ, Bryant D et al. 2018. The role of the side chain on the performance of n-type conjugated polymers in aqueous electrolytes. Chem. Mater. 30:2945–53
    [Google Scholar]
  69. 69. 
    Bischak CG, Flagg LQ, Ginger DS. 2020. Ion exchange gels allow organic electrochemical transistor operation with hydrophobic polymers in aqueous solution. Adv. Mater. 32:2002610
    [Google Scholar]
  70. 70. 
    Spyropoulos GD, Gelinas JN, Khodagholy D. 2019. Internal ion-gated organic electrochemical transistor: a building block for integrated bioelectronics. Sci. Adv. 5:eaau7378
    [Google Scholar]
  71. 71. 
    Wu X, Hong JJ, Shin W, Ma L, Liu T et al. 2019. Diffusion-free Grotthuss topochemistry for high-rate and long-life proton batteries. Nat. Energy 4:123–30
    [Google Scholar]
  72. 72. 
    Giordani M, Berto M, Di Lauro M, Bortolotti CA, Zoli M, Biscarini F. 2017. Specific dopamine sensing based on short-term plasticity behavior of a whole organic artificial synapse. ACS Sens 2:1756–60
    [Google Scholar]
  73. 73. 
    Banerjee W. 2020. Challenges and applications of emerging nonvolatile memory devices. Electronics 9:1029
    [Google Scholar]
  74. 74. 
    Qian C, Sun J, Kong L-A, Gou G, Yang J et al. 2016. Artificial synapses based on in-plane gate organic electrochemical transistors. ACS Appl. Mater. Interfaces 8:26169–75
    [Google Scholar]
  75. 75. 
    Gkoupidenis P, Schaefer N, Strakosas X, Fairfield JA, Malliaras GG. 2015. Synaptic plasticity functions in an organic electrochemical transistor. Appl. Phys. Lett. 107:263302
    [Google Scholar]
  76. 76. 
    Berzina T, Smerieri A, Bernabò M, Pucci A, Ruggeri G et al. 2009. Optimization of an organic memristor as an adaptive memory element. J. Appl. Phys. 105:124515
    [Google Scholar]
  77. 77. 
    Zhou J, Anjum DH, Chen L, Xu X, Ventura IA et al. 2014. The temperature-dependent microstructure of PEDOT/PSS films: insights from morphological, mechanical and electrical analyses. J. Mater. Chem. C 2:9903–10
    [Google Scholar]
  78. 78. 
    Kim S-M, Kim C-H, Kim Y, Kim N, Lee W-J et al. 2018. Influence of PEDOT:PSS crystallinity and composition on electrochemical transistor performance and long-term stability. Nat. Commun. 9:3858
    [Google Scholar]
  79. 79. 
    Gumyusenge A, Luo X, Ke Z, Tran DT, Mei J. 2019. Polyimide-based high-temperature plastic electronics. ACS Mater. Lett. 1:154–57
    [Google Scholar]
  80. 80. 
    Gumyusenge A, Tran DT, Luo X, Pitch GM, Zhao Y et al. 2018. Semiconducting polymer blends that exhibit stable charge transport at high temperatures. Science 362:1131–34
    [Google Scholar]
  81. 81. 
    Arnold C Jr. 1979. Stability of high-temperature polymers. J. Polym. Sci. Macromol. Rev. 14:265–378
    [Google Scholar]
  82. 82. 
    Meine N, Benedito F, Rinaldi R. 2010. Thermal stability of ionic liquids assessed by potentiometric titration. Green Chem 12:1711–14
    [Google Scholar]
  83. 83. 
    Villanueva M, Coronas A, García J, Salgado J. 2013. Thermal stability of ionic liquids for their application as new absorbents. Ind. Eng. Chem. Res. 52:15718–27
    [Google Scholar]
  84. 84. 
    Song E, Li J, Won SM, Bai W, Rogers JA. 2020. Materials for flexible bioelectronic systems as chronic neural interfaces. Nat. Mater. 19:590–603
    [Google Scholar]
  85. 85. 
    Rogers J, Bao Z, Lee T-W. 2019. Wearable bioelectronics: opportunities for chemistry. Acc. Chem. Res. 52:521–22
    [Google Scholar]
  86. 86. 
    Paulsen BD, Tybrandt K, Stavrinidou E, Rivnay J. 2020. Organic mixed ionic–electronic conductors. Nat. Mater. 19:13–26
    [Google Scholar]
  87. 87. 
    van Doremaele ERW, Gkoupidenis P, van de Burgt Y. 2019. Towards organic neuromorphic devices for adaptive sensing and novel computing paradigms in bioelectronics. J. Mater. Chem. C 7:12754–60
    [Google Scholar]
  88. 88. 
    Maya-Vetencourt JF, Ghezzi D, Antognazza MR, Colombo E, Mete M et al. 2017. A fully organic retinal prosthesis restores vision in a rat model of degenerative blindness. Nat. Mater. 16:681–89
    [Google Scholar]
  89. 89. 
    Maya-Vetencourt JF, Manfredi G, Mete M, Colombo E, Bramini M et al. 2020. Subretinally injected semiconducting polymer nanoparticles rescue vision in a rat model of retinal dystrophy. Nat. Nanotechnol. 15:698–708
    [Google Scholar]
  90. 90. 
    Dickey AS, Suminski A, Amit Y, Hatsopoulos NG. 2009. Single-unit stability using chronically implanted multielectrode arrays. J. Neurophysiol. 102:1331–39
    [Google Scholar]
  91. 91. 
    Taunyazov T, Sng W, See HH, Lim B, Kuan J, Ansari FA et al. 2020. Event-driven visual-tactile sensing and learning for robots. arXiv:2009.07083 [cs.RO]
  92. 92. 
    Lee WW, Tan YJ, Yao H, Li S, See HH et al. 2019. A neuro-inspired artificial peripheral nervous system for scalable electronic skins. Sci. Robot. 4:eaax2198
    [Google Scholar]
  93. 93. 
    Li P, Anwar Ali HP, Cheng W, Yang J, Tee BCK 2020. Bioinspired prosthetic interfaces. Adv. Mater. Technol. 5:1900856
    [Google Scholar]
  94. 94. 
    Lee Y, Lee T-W. 2019. Organic synapses for neuromorphic electronics: from brain-inspired computing to sensorimotor nervetronics. Acc. Chem. Res. 52:964–74
    [Google Scholar]
  95. 95. 
    Mikhaylov A, Pimashkin A, Pigareva Y, Gerasimova S, Gryaznov E et al. 2020. Neurohybrid memristive CMOS-integrated systems for biosensors and neuroprosthetics. Front. Neurosci. 14:358
    [Google Scholar]
  96. 96. 
    Tarabella G, D'Angelo P, Cifarelli A, Dimonte A, Romeo A et al. 2015. A hybrid living/organic electrochemical transistor based on the Physarum polycephalum cell endowed with both sensing and memristive properties. Chem. Sci. 6:2859–68
    [Google Scholar]
  97. 97. 
    Juzekaeva E, Nasretdinov A, Battistoni S, Berzina T, Iannotta S et al. 2019. Coupling cortical neurons through electronic memristive synapse. Adv. Mater. Technol. 4:1800350
    [Google Scholar]
  98. 98. 
    Gkoupidenis P, Koutsouras DA, Lonjaret T, Fairfield JA, Malliaras GG. 2016. Orientation selectivity in a multi-gated organic electrochemical transistor. Sci. Rep. 6:27007
    [Google Scholar]
  99. 99. 
    Qian C, Kong L-A, Yang J, Gao Y, Sun J. 2017. Multi-gate organic neuron transistors for spatiotemporal information processing. Appl. Phys. Lett. 110:083302
    [Google Scholar]
  100. 100. 
    Rivnay J, Inal S, Salleo A, Owens RM, Berggren M, Malliaras GG. 2018. Organic electrochemical transistors. Nat. Rev. Mater. 3:17086
    [Google Scholar]
  101. 101. 
    Khodagholy D, Gelinas JN, Thesen T, Doyle W, Devinsky O et al. 2015. NeuroGrid: recording action potentials from the surface of the brain. Nat. Neurosci. 18:310–15
    [Google Scholar]
  102. 102. 
    Keene ST, Lubrano C, Kazemzadeh S, Melianas A, Tuchman Y et al. 2020. A biohybrid synapse with neurotransmitter-mediated plasticity. Nat. Mater. 19:969–73
    [Google Scholar]
  103. 103. 
    Iino Y, Sawada T, Yamaguchi K, Tajiri M, Ishii S et al. 2020. Dopamine D2 receptors in discrimination learning and spine enlargement. Nature 579:555–60
    [Google Scholar]
  104. 104. 
    Lee Y, Oh JY, Xu W, Kim O, Kim TR et al. 2018. Stretchable organic optoelectronic sensorimotor synapse. Sci. Adv. 4:eaat7387
    [Google Scholar]
  105. 105. 
    Kim Y, Chortos A, Xu W, Liu Y, Oh JY et al. 2018. A bioinspired flexible organic artificial afferent nerve. Science 360:998–1003
    [Google Scholar]
  106. 106. 
    Jörntell H, Bengtsson F, Geborek P, Spanne A, Terekhov AV, Hayward V. 2014. Segregation of tactile input features in neurons of the cuneate nucleus. Neuron 83:1444–52
    [Google Scholar]
/content/journals/10.1146/annurev-matsci-080619-111402
Loading
/content/journals/10.1146/annurev-matsci-080619-111402
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error