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Synaptic electronics and neuromorphic computing

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Abstract

In order to map the computing architecture and intelligent functions of the human brain on hardware, we need electronic devices that can emulate biological synapses and even neurons, preferably at the physical level. Beginning with the history of neuromorphic computation, in this article, we will briefly review the architecture of the brain and the learning mechanisms responsible for its plasticity. We will also introduce several memristive devices that have been used to implement electronic synapses, presenting some important milestones in this area of research and discussing their advantages, disadvantages, and future prospects.

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References

  1. Turing A. On computable numbers, with an application to the entscheidungs problem. Proc London Math Soc, 1936, 42: 230–265

    MathSciNet  MATH  Google Scholar 

  2. von Neumann J. First draft of a report on the EDVAC. IEEE Ann Hist Comput, 1993, 15: 11–21

    Article  MathSciNet  Google Scholar 

  3. Turing A. Intelligent machinery. In: Copeland B J, ed. The Essential Turing: Seminal Writings in Computing, Logic, Philosophy, Artificial Intelligence, and Artificial Life plus The Secrets of Enigma. New York: Oxford University Press, 2004. 406–443

    Google Scholar 

  4. Anderson H C. Neural network machines. IEEE Potentials, 1989, 8: 13–16

    Article  Google Scholar 

  5. Squire L R, Berg D, Bloom F, et al. Fundamental neuroscience. Curr Opin Neurobiol, 2008, 10: 649–654

    Google Scholar 

  6. Kandel E R, Schwartz J H, Jessell T M. Principles of Neural Science. 4th ed. New York: McGraw-Hill Medical, 2000

    Google Scholar 

  7. Bennett M V L, Zukin R S. Electrical coupling and neuronal synchronization in the mammalian brain. Neuron, 2004, 41: 495–511

    Article  Google Scholar 

  8. Zamarreno-Ramos C, Camunas-Mesa L A, Pérez-Carrasco J A, et al. On spike-timing-dependent-plasticity, memristive devices, and building a self-learning visual cortex. Front Neurosci, 2011, 5: 1–22

    Article  Google Scholar 

  9. Pereda A E. Electrical synapses and their functional interactions with chemical synapses. Nat Rev Neurosci, 2014, 15: 250–63

    Article  Google Scholar 

  10. Noback C R, Ruggiero D A, Demarest R J, The Human Nervous System: Structure and Function, 6th ed. Totowa: Humana Press, 2005

    Google Scholar 

  11. Versace M, Chandler B. MoNETA: a mind made from memristors. IEEE Spectr, 2010. http://spectrum.ieee.org/robotics/artificial-intelligence/moneta-a-mind-made-from-memristors

    Google Scholar 

  12. Chua L, Adamatzky A. Memristor Networks. Switzerland: Springer International Publishing, 2013

    MATH  Google Scholar 

  13. Mead C. Neuromorphic electronic systems. Proc IEEE, 1990, 78: 1629–1636

    Article  Google Scholar 

  14. Diorio C, Hasler P, Minch B A, et al. Single-transistor silicon synapse. IEEE Trans Electron Dev, 1996, 43: 1972–1980

    Article  Google Scholar 

  15. Wong H-S P, Raoux S, Kim S, et al. Phase change memory. Proc IEEE, 2010, 98: 2201–2227

    Article  Google Scholar 

  16. Waser R, Aono M. Nanoionics-based resistive switching memories. Nat Mater, 2007, 6: 833–840

    Article  Google Scholar 

  17. Versace M, Chandler B. The brain of a new machine. IEEE Spectr, 2010, 47: 30–37

    Article  Google Scholar 

  18. Snider G. Amerson R, Carter D, et al. From synapses to circuitry: using memristive memory to explore the electronic brain. Computer, 2011, 44: 21–28

    Article  Google Scholar 

  19. Hylton T. DARPA SyNAPSE Project. Arlington, 2009

    Google Scholar 

  20. Ananthanarayanan R, Esser S K, Simon H D, et al. The cat is out of the bag: cortical simulations with 109 neurons, 1013 synapses. In: Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis, Portland, 2009. 1–12

    Chapter  Google Scholar 

  21. Merolla P A, Arthur J V, Alvarez-Icaza R, et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science, 2014, 345: 668–673

    Article  Google Scholar 

  22. Furber S B, Lester D R, Plana L A, et al. Overview of the SpiNNaker system architecture. IEEE Trans Comput, 2013, 62: 2454–2467

    Article  MathSciNet  Google Scholar 

  23. Markram H. The Blue Brain Project. Nat Rev Neurosci, 2006, 7: 153–160

    Article  Google Scholar 

  24. Schemmel J, Grubl A, Hartmann S, et al. Live demonstration: a scaled-down version of the BrainScaleS wafer-scale neuromorphic system. In: Proceedings of 2012 IEEE International Symposium on Circuits and Systems, Seoul, 2012. 702

    Chapter  Google Scholar 

  25. Boahen K. Neurogrid: Emulating a Million Neurons in the Cortex. In: Proceedings of 28th IEEE Engineering in Medicine and Biology Society Annual International Conference, New York, 2006. Supp: 6702

    Google Scholar 

  26. Benjamin B V, Gao P, McQuinn E, et al. Neurogrid: a mixed-analog-digital multichip system for large-scale neural simulations. Proc IEEE, 2014, 102: 699–716

    Article  Google Scholar 

  27. Hebb D O. The first stage of perception: growth of the assembly. In: The Organization of Behavior. Hoboken: John Wiley & Sons Inc., 1949. 60–78

    Google Scholar 

  28. Markram H, Gerstner W, Sjüstrüm P J. A history of spike-timing-dependent plasticity. Front Synaptic Neurosci, 2011, 3: 1–24

    Article  Google Scholar 

  29. Markram H, Lübke J, Frotscher M, et al. Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science, 1997, 275: 213–215

    Article  Google Scholar 

  30. Levy W B, Steward O. Temporal contiguity requirements for long-term associative potentiation/depression in the hippocampus. Neuroscience, 1983, 8: 791–797

    Article  Google Scholar 

  31. Cooper L N, Bear M F. The BCM theory of synapse modification at 30: interaction of theory with experiment. Nat Rev Neurosci, 2012, 13: 798–810

    Article  Google Scholar 

  32. Bi G Q, Poo M M. Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J Neurosci, 1998, 18: 10464–10472

    Google Scholar 

  33. Bienenstock E L, Cooper L N, Munro P W. Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex. J Neurosci, 1982, 2: 32–48

    Google Scholar 

  34. Sejnowski T, Chattarji S, Sfanton P. Induction of synaptic plasticity by hebbian covariance in the hippocampus. In: The Computing Neuron. Boston: Addison-Wesley Longman Publishing Co., 1989. 105–124

    Google Scholar 

  35. Lynch M A. Long-term potentiation and memory. Physiol Rev, 2004, 84: 87–136

    Article  Google Scholar 

  36. Bliss T V P, Lomo T. Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path. J Physiol, 1973, 232: 331–356

    Article  Google Scholar 

  37. Mulkey R, Herron C, Malenka R. An essential role for protein phosphatases in hippocampal long-term depression. Science, 1993, 261: 1051–1055

    Article  Google Scholar 

  38. Sjostrom P J, Gerstner W. Spike-timing-dependent plasticity. Scholarpedia, 2010, 5: 1362

    Article  Google Scholar 

  39. Gütig R, Aharonov R, Rotter S, et al. Learning input correlations through nonlinear temporally asymmetric Hebbian plasticity. J Neurosci, 2003, 23: 3697–3714

    Google Scholar 

  40. Rubin J, Lee D D, Sompolinsky H. Equilibrium properties of temporally asymmetric Hebbian plasticity. Physl Rev Lett, 2001, 86: 364–367

    Article  Google Scholar 

  41. van Rossum M C, Bi G Q, Turrigiano G G. Stable Hebbian learning from spike timing-dependent plasticity. J Neurosci, 2000, 20: 8812–8821

    Google Scholar 

  42. Purves D, Augustine G J, Fitzpatrick D, et al. Neuroscience. 2nd ed. Sunderland: Sinauer Associates, 2001

    Google Scholar 

  43. Lee M-J, Lee C B, Lee D, et al. A fast, high-endurance and scalable non-volatile memory device made from asymmetric Ta2O5-x /TaO2-x bilayer structures. Nat Mater, 2011, 10: 625–630

    Article  Google Scholar 

  44. Chanthbouala A, Garcia V, Cherifi R O, et al. A ferroelectric memristor. Nat Mater, 2012, 11: 860–864

    Article  Google Scholar 

  45. Yang J J, Pickett M D, Li X, et al. Memristive switching mechanism for metal/oxide/metal nanodevices. Nat Nanotechnol, 2008, 3: 429–433

    Article  Google Scholar 

  46. Wuttig M, Yamada N. Phase-change materials for rewriteable data storage. Nat Mater, 2007, 6: 824–832

    Article  Google Scholar 

  47. Kuzum D, Jeyasingh R G D, Lee B, et al. Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing. Nano Lett, 2012, 12: 2179–2186

    Article  Google Scholar 

  48. Yang J J, Strukov D B, Stewart D R. Memristive devices for computing. Nat Nanotechnol, 2013, 8: 13–24

    Article  Google Scholar 

  49. Strukov D B, Snider G S, Stewart D R, et al. The missing memristor found. Nature, 2008, 453: 80–83

    Article  Google Scholar 

  50. Chua L O, Kang S M. Memristive devices and systems. Proc IEEE, 1976, 64: 209–223

    Article  MathSciNet  Google Scholar 

  51. Hickmott T W, Hiatt W R. Bistable switching in Niobium oxide diodes. Appl Phys Lett, 1965, 6: 106–108

    Article  Google Scholar 

  52. Hickmott T W. Low-frequency negative resistance in thin anodic oxide films. J Appl Phys, 1962, 33: 2669

    Article  Google Scholar 

  53. Chua L. Resistance switching memories are memristors. Appl Phys A-Mater Sci Process, 2011, 102: 765–783

    Article  MATH  Google Scholar 

  54. Rajendran B, Liu Y, Seo J S, et al. Specifications of nanoscale devices and circuits for neuromorphic computational systems. IEEE Trans Electron Dev, 2013, 60: 246–253

    Article  Google Scholar 

  55. Snider G S. Spike-timing-dependent learning in memristive nanodevices. In: Proceedings of 2008 IEEE/ACM International Symposium on Nanoscale Architectures NANOARCH 2008, Anaheim, 2008. 85–92

    Google Scholar 

  56. Wong H S P, Lee H Y, Yu S M, et al. Metal-oxide RRAM. Proc IEEE, 2012, 100: 1951–1970

    Article  Google Scholar 

  57. Yang J J, Miao F, Pickett M D, et al. The mechanism of electroforming of metal oxide memristive switches. Nanotechnology, 2009, 20: 215201

    Article  Google Scholar 

  58. Yang Y, Gao P, Li L, et al. Electrochemical dynamics of nanoscale metallic inclusions in dielectrics. Nat Commun, 2014, 5: 4232

    Google Scholar 

  59. Sarkar B, Lee B, Misra V. Understanding the gradual reset in Pt/Al2O3/Ni RRAM for synaptic applications. Semicond Sci Technol, 2015, 30: 105014

    Article  Google Scholar 

  60. Rolandi M, Josberger E E, Deng Y X. Two-terminal proton conducting devices with synaptic behavior and memory. In: Proceedings of 72nd Device Research Conference, Santa Barbara, 2014. 245–246

    Google Scholar 

  61. Yang R, Terabe K, Yao Y, et al. Synaptic plasticity and memory functions achieved in a WO3-x -based nanoionics device by using the principle of atomic switch operation. Nanotechnology, 2013, 24: 384003

    Article  Google Scholar 

  62. Jung J-W, Park S, Jeong Y-H. ReRAM-based synaptic device for neuromorphic computing. In: Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS), Melbourne VIC, 2014. 1054–1057

    Google Scholar 

  63. Mandal S, El-Amin A, Alexander K, et al. Novel synaptic memory device for neuromorphic computing. Sci Rep, 2014, 4: 5333

    Google Scholar 

  64. Gao B, Liu L, Kang J. Investigation of the synaptic device based on the resistive switching behavior in hafnium oxide. Prog Nat Sci Mater Int, 2015, 25: 47–50

    Article  Google Scholar 

  65. Wang Y-F, Lin Y-C, Wang I-T, et al. Characterization and modeling of nonfilamentary Ta/TaOx/TiO2/Ti analog synaptic device. Sci Rep, 2015, 5: 10150

    Article  Google Scholar 

  66. Yu S M, Wu Y, Jeyasingh R, et al. An electronic synapse device based on metal oxide resistive switching memory for neuromorphic computation. IEEE Trans Electron Dev, 2011, 58: 2729–2737

    Article  Google Scholar 

  67. Gao B, Bi Y, Chen H Y, et al. Ultra-low-energy three-dimensional oxide-based electronic synapses for implementation of robust high-accuracy neuromorphic computation systems. ACS Nano, 2014, 8: 6998–7004

    Article  Google Scholar 

  68. Choi H, Jung H, Lee J, et al. An electrically modifiable synapse array of resistive switching memory. Nanotechnology, 2009, 20: 345201

    Article  Google Scholar 

  69. Panwar N, Kumar D, Upadhyay N K, et al. Memristive synaptic plasticity in Pr0.7Ca0.3MnO3 RRAM by bio-mimetic programming. In: Proceedings of 72nd Device Research Conference, Santa Barbara, 2014. 135–136

    Google Scholar 

  70. Pershin Y V, Di Ventra M. Neuromorphic, digital, and quantum computation with memory circuit elements. Proc IEEE, 2012, 100: 2071–2080

    Article  Google Scholar 

  71. Kim S, Du C, Sheridan P, et al. Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity. Nano Lett, 2015, 15: 2203–2211

    Article  Google Scholar 

  72. Valov I, Waser R, Jameson J R, et al. Electrochemical metallization memories—fundamentals, applications, prospects. Nanotechnology, 2011, 22: 254003

    Article  Google Scholar 

  73. Kozicki M N, Gopalan C, Balakrishnan M, et al. Nonvolatile memory based on solid electrolytes. In: Proceedings of Symposium on Non-Volatile Memory Technology, Orlando, 2004. 10–17

    Google Scholar 

  74. Kund M, Beitel G, Pinnow C-U, et al. Conductive bridging RAM (CBRAM): an emerging non-volatile memory technology scalable to sub 20nm. In: Technical Digest of IEEE International Electron Devices Meeting, Washington DC, 2005. 754–757

    Google Scholar 

  75. Hirose Y, Hirose H. Polarity-dependent memory switching and behavior of Ag dendrite in Ag-photodoped amorphous As2S3 films. J Appl Phys, 1976, 47: 2767–2772

    Article  Google Scholar 

  76. Gopalan C, Ma Y, Gallo T, et al. Demonstration of conductive bridging random access memory (CBRAM) in logic CMOS process. In: Proceedings of 2010 IEEE International Memory Workshop, Seoul, 2010. 1–4

    Chapter  Google Scholar 

  77. Lu W, Jeong D S, Kozicki M, et al. Electrochemical metallization cellsblending nanoionics into nanoelectronics? MRS Bull, 2012, 37: 124–130

    Article  Google Scholar 

  78. Liu Q, Sun J, Lv H, et al. Resistive switching: real-time observation on dynamic growth/dissolution of conductive filaments in oxide-electrolyte-based ReRAM. Adv Mater, 2012, 24: 1774

    Article  Google Scholar 

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

    Article  Google Scholar 

  80. Atkinson R, Shiffrin R. Human Memory: a Proposed System and its Control Processes. 2nd ed. Psych Learn Motiv, 1968, 2: 89–195

    Article  Google Scholar 

  81. Yu S M, Wong H S P. Modeling the switching dynamics of programmable-metallization-cell (PMC) memory and its application as synapse device for a neuromorphic computation system. In: Proceedings of 2010 IEEE International Electron Devices Meeting (IEDM), San Francisco, 2010. 520–523

    Google Scholar 

  82. Yu S M, Wong H S P. compact modeling of conducting-bridge random-access memory (CBRAM). IEEE Trans Electron Dev, 2011, 58: 1352–1360

    Article  Google Scholar 

  83. Suri M, Querlioz D, Bichler O, et al. Bio-inspired stochastic computing using binary CBRAM synapses. IEEE Trans Electron Dev, 2013, 60: 2402–2409

    Article  Google Scholar 

  84. Mahalanabis D, Barnaby H J, Gonzalez-Velo Y, et al. Incremental resistance programming of programmable metallization cells for use as electronic synapses. Solid State Electron, 2014, 100: 39–44

    Article  Google Scholar 

  85. Jo S H, Chang T, Ebong I, et al. Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett, 2010, 10: 1297–1301

    Article  Google Scholar 

  86. Kim K H, Gaba S, Wheeler D, et al. A functional hybrid memristor crossbar-array/CMOS system for data storage and neuromorphic applications. Nano Lett, 2012, 12: 389–395

    Article  Google Scholar 

  87. Petersen C C, Malenka R C, Nicoll R A, et al. All-or-none potentiation of CA3-CA1 synapses. Proc Nat Acad Sci USA, 1998, 95: 4732–4737

    Article  Google Scholar 

  88. O’Connor D H, Wittenberg G M, Wang S S-H. Graded bidirectional synaptic plasticity is composed of switch-like unitary events. Proc Nat Acad Sci USA, 2005, 102: 9679–9684

    Article  Google Scholar 

  89. Suri M, Bichler O, Querlioz D, et al. Bio-inspired computing with binary stochastic CBRAM synapses. IEEE Trans Electron Dev, 2013, 60: 2402–2409

    Article  Google Scholar 

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

    Article  Google Scholar 

  91. Yang Y, Chen B, Lu WD. Memristive physically evolving networks enabling the emulation of heterosynaptic plasticity. Adv Mater, 2015, 27: 7720–7727

    Article  Google Scholar 

  92. Ielmini D. Filamentary-switching model in RRAM for time, energy and scaling projections. In: Proceedings of 2011 IEEE International Electron Devices Meeting (IEDM), Washington DC, 2011. 17.2.1–17.2.4

    Google Scholar 

  93. Belmonte A, Kim W, Chan B T, et al. A thermally stable and high-performance 90-nm-based 1T1R CBRAM cell. IEEE Trans Electron Dev, 2013, 60: 3690–3695

    Article  Google Scholar 

  94. Russo U, Kamalanathan D, Ielmini D, et al. Study of multilevel programming in programmable metallization cell (PMC) memory. IEEE Trans Electron Dev, 2009, 56: 1040–1047

    Article  Google Scholar 

  95. Akerman J. Toward a universal memory. Science, 2005, 308: 508–510

    Article  Google Scholar 

  96. Wang K L, Alzate J G, Amiri P K. Low-power non-volatile spintronic memory: STT-RAM and beyond. J Phys D Appl Phys, 2013, 46: 074003

    Article  Google Scholar 

  97. Augustine C, Mojumder N N, Fong X, et al. Spin-transfer torque MRAMs for low power memories: perspective and prospective. IEEE Sens J, 2012, 12: 756–766

    Article  Google Scholar 

  98. Roy K, Fan D, Fong X, et al. Exploring spin transfer torque devices for unconventional computing. IEEE J Emerg Sel Top Circuits Syst, 2015, 5: 5–16

    Article  Google Scholar 

  99. Devolder T, Hayakawa J, Ito K, et al. Single-shot time-resolved measurements of nanosecond-scale spin-transfer induced switching: stochastic versus deterministic aspects. Phys Rev Lett, 2008, 100: 057206

    Article  Google Scholar 

  100. Zhang Y, Zhao W, Prenat G, et al. Electrical modeling of stochastic spin transfer torque writing in magnetic tunnel junctions for memory and logic applications. IEEE Trans Magn, 2013, 49: 4375–4378

    Article  Google Scholar 

  101. Vincent A F, Larroque J, Locatelli N, et al. Spin-transfer torque magnetic memory as a stochastic memristive synapse for neuromorphic systems. IEEE Trans Biomed Circuits Syst, 2015, 9: 166–174

    Article  Google Scholar 

  102. Zeng Z M, Amiri P K, Rowlands G, et al. Effect of resistance-area product on spin-transfer switching in MgO-based magnetic tunnel junction memory cells. Appl Phys Lett, 2011, 98: 072512

    Article  Google Scholar 

  103. Zhou P, Zhao B, Yang J, et al. Energy reduction for STT-RAM using early write termination. In: Digest of Technical Papers of 2009 IEEE/ACM International Conference on Computer-Aided Design, San Jose, 2009. 264–268

    Chapter  Google Scholar 

  104. Daughton J M. Advanced MRAM Concepts. 2001. http://www.nve.com/Downloads/mram2.pdf

    Google Scholar 

  105. Ovshinsky S R. Reversible electrical switching phenomena in disordered structures. Phys Rev Lett, 1968, 21: 1450–1453

    Article  Google Scholar 

  106. Wong H S P, Raoux S, Kim S, et al. Phase change memory. Proc IEEE, 2010, 98: 2201–2227

    Article  Google Scholar 

  107. Lai S. Current status of the phase change memory and its future. In: Technical Digest of IEEE International Electron Devices Meeting, Washington DC, 2003. 10.1.1–10.1.4

    Google Scholar 

  108. Lankhorst M H R, Ketelaars B W S M M, Wolters R A M. Low-cost and nanoscale non-volatile memory concept for future silicon chips. Nat Mater, 2005, 4: 347–352

    Article  Google Scholar 

  109. Park J-B, Park G-S, Baik H-S, et al. Phase-change behavior of stoichiometric Ge2Sb2Te5 in phase-change random access memory. J Electrochem Soc, 2007, 154: H139–H141

    Article  Google Scholar 

  110. Loke D, Lee T H, Wang W J, et al. Breaking the speed limits of phase-change memory. Science, 2012, 336: 1566–1569

    Article  Google Scholar 

  111. Ovshinsky S R, Pashmakov B. Innovation providing new multiple functions in phase-change materials to achieve cognitive computing. MRS Proc, 2003, 803

    Google Scholar 

  112. Suri M, Bichler O, Querlioz D, et al. Phase change memory as synapse for ultra-dense neuromorphic systems: application to complex visual pattern extraction. In: Proceedings of 2011 IEEE International Electron Devices Meeting (IEDM), Washington DC, 2011. 4.4.1–4.4.4

    Google Scholar 

  113. Jackson B L, Rajendran B, Corrado G S, et al. Nanoscale electronic synapses using phase change devices. J Emerg Technol Comput Syst, 2013, 9: 12:1–12:20

    Article  Google Scholar 

  114. Eryilmaz S B, Kuzum D, Jeyasingh R, et al. Brain-like associative learning using a nanoscale non-volatile phase change synaptic device array. Front Neurosci, 2014, 8: 1–11

    Article  Google Scholar 

  115. Schaller R R. Moore’s law: past, present and future. IEEE Spectr, 1997, 34: 52–59

    Article  Google Scholar 

  116. Mead C. Neuromorphic electronic systems. Proc IEEE, 1990, 78: 1629–1636

    Article  Google Scholar 

  117. Hasler P, Diorio C, Minch B A, et al. Single transistor learning synapse with long term storage. In: Proceedings of 1995 IEEE International Symposium on Circuits and Systems, Seattle, 1995. 3: 1660–1663

    Google Scholar 

  118. Merolla P, Arthur J, Akopyan F, et al. A digital neurosynaptic core using embedded crossbar memory with 45pJ per spike in 45nm. In: Proceedings of 2011 IEEE Custom Integrated Circuits Conference (CICC), San Jose, 2011. 1–4

    Chapter  Google Scholar 

  119. Seo J, Brezzo B, Liu Y, et al. A 45nm CMOS neuromorphic chip with a scalable architecture for learning in networks of spiking neurons. In: Proceedings of 2011 IEEE Custom Integrated Circuits Conference (CICC), San Jose, 2011. 1–4

    Chapter  Google Scholar 

  120. Bartolozzi C, Indiveri G. Synaptic dynamics in analog VLSI. Neural Comput, 2007, 19: 2581–2603

    Article  MATH  Google Scholar 

  121. Mack C A. Fifty years of Moore’s law. IEEE Trans Semicond Manuf, 2011, 24: 202–207

    Article  Google Scholar 

  122. Likharev K K. Neuromorphic CMOL circuits. In: Proceedings of 2003 3rd IEEE Conference on Nanotechnology, San Francisco, 2003. 2: 339–342

    Google Scholar 

  123. Likharev K, Mayr A, Muckra I, et al. CrossNets: high-performance neuromorphic architectures for CMOL circuits. Ann N Y Acad Sci, 2003, 1006: 146–163

    Article  Google Scholar 

  124. Likharev K K, Strukov D. B. CMOL: Devices, Circuits, and Architectures. In: Cuniberti G, Richter K, Fagas G, eds. Introducing Molecular Electronics. Berlin/Heidelberg: Springer, 2006. 447–477

    Chapter  Google Scholar 

  125. Feldheim D L, Keating C D. Self-assembly of single electron transistors and related devices. Chem Soc Rev, 1998, 27: 1–12

    Article  Google Scholar 

  126. Ma X, Strukov D B, Lee J H, et al. Afterlife for silicon: CMOL circuit architectures. In: Proceedings of 2005 5th IEEE Conference on Nanotechnology, Nagoya, 2005. 175–178

    Google Scholar 

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

    Article  Google Scholar 

  128. Maass W. Networks of spiking neurons: the third generation of neural network models. Neural Netw, 1997, 10: 1659–1671

    Article  Google Scholar 

  129. Hodgkin A, Huxley A. A quantitative description of membrane current and its application to conduction and excitation in nerve. Bull Math Biol, 1990, 52: 25–71

    Article  Google Scholar 

  130. Izhikevich E M. Hybrid spiking models. Philos Trans A Math Phys Eng Sci, 2010, 368: 5061–5070

    Article  MathSciNet  MATH  Google Scholar 

  131. O’Reilly R C. Biologically based computational models of high-level cognition. Science, 2006, 314: 91–94

    Article  MathSciNet  MATH  Google Scholar 

  132. Herz A V M, Gollisch T, Machens C K, et al. Modeling single-neuron dynamics and computations: a balance of detail and abstraction. Science, 2006, 314: 80–85

    Article  MathSciNet  MATH  Google Scholar 

  133. Brüderle D, Petrovici M A, Vogginger B, et al. A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems. Biol Cybern, 2011, 104: 263–296

    Article  Google Scholar 

  134. Arthur J V, Boahen K. Silicon-neuron design: a dynamical systems approach. IEEE Trans Circuits Syst I-Regul Pap, 2011, 58: 1034–1043

    Article  MathSciNet  Google Scholar 

  135. Rachmuth G, Poon C-S. Transistor analogs of emergent iono-neuronal dynamics. HFSP J, 2008, 2: 156–166

    Article  Google Scholar 

  136. Mead C. Analog VLSI and Neural Systems. Boston: Addison-Wesley Longman Publishing Co., Inc., 1989. 179–186

    Book  MATH  Google Scholar 

  137. Pickett M D, Medeiros-Ribeiro G, Williams R S. A scalable neuristor built with Mott memristors. Nat Mater, 2013, 12: 114–117

    Article  Google Scholar 

  138. Park S, Noh J, Choo M-L, et al. Nanoscale RRAM-based synaptic electronics: toward a neuromorphic computing device. Nanotechnology, 2013, 24: 384009

    Article  Google Scholar 

  139. Serrano-Gotarredona T, Prodromakis T, Linares-Barranco B. A proposal for hybrid memristor-CMOS spiking neuromorphic learning systems. IEEE Circuits Syst Mag, 2013, 13: 74–88

    Article  Google Scholar 

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Upadhyay, N.K., Joshi, S. & Yang, J.J. Synaptic electronics and neuromorphic computing. Sci. China Inf. Sci. 59, 061404 (2016). https://doi.org/10.1007/s11432-016-5565-1

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