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SCIENCE CHINA Information Sciences
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SCIENCE CHINA Information Sciences, Volume 59 , Issue 6 : 061404(2016) https://doi.org/10.1007/s11432-016-5565-1

Synaptic electronics and neuromorphic computing

Navnidhi K. UPADHYAY , Saumil JOSHI , J. Joshua YANG *
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  • ReceivedJan 26, 2016
  • AcceptedFeb 23, 2016
  • PublishedMay 11, 2016
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References

[1] Turing A. Proc London Math Soc, 1936, 42: :-265 Google Scholar

[2] von Neumann J. IEEE Ann Hist Comput, 1993, 15: 11-21 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. IEEE Potentials, 1989, 8: 13-16 Google Scholar

[5] Squire L R, Berg D, Bloom F, et al. 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. Neuron, 2004, 41: 495-511 Google Scholar

[8] Zamarre\ {n}o-Ramos C, Camu\ {n}as-Mesa L A, Pérez-Carrasco J A, et al. Front Neurosci, 2011, 5: 1-22 Google Scholar

[9] Pereda A E. Nat Rev Neurosci, 2014, 15: 250-63 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/\linebreak robotics/artificial-intelligence/moneta-a-mind-made-from-memristors. Google Scholar

[12] Chua L, Adamatzky A. {Memristor Networks}. Switzerland: Springer International Publishing, 2013. Google Scholar

[13] Mead C. Proc IEEE, 1990, 78: 1629-1636 Google Scholar

[14] Diorio C, Hasler P, Minch B A, et al. IEEE Trans Electron Dev, 1996, 43: 1972-1980 Google Scholar

[15] Wong H-S P, Raoux S, Kim S, et al. Proc IEEE, 2010, 98: 2201-2227 Google Scholar

[16] Waser R, Aono M. Nat Mater, 2007, 6: 833-840 Google Scholar

[17] Versace M, Chandler B. IEEE Spectr, 2010, 47: 30-37 Google Scholar

[18] Snider G. Amerson R, Carter D, et al. Computer, 2011, 44: 21-28 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. Google Scholar

[21] Merolla P A, Arthur J V, Alvarez-Icaza R, et al. Science, 2014, 345: 668-673 Google Scholar

[22] Furber S B, Lester D R, Plana L A, et al. IEEE Trans Comput, 2013, 62: 2454-2467 Google Scholar

[23] Markram H. Nat Rev Neurosci, 2006, 7: 153-160 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. 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. Proc IEEE, 2014, 102: 699-716 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, Sjöström P J. Front Synaptic Neurosci, 2011, 3: :-24 Google Scholar

[29] Markram H, Lübke J, Frotscher M, et al. Science, 1997, 275: 213-215 Google Scholar

[30] Levy W B, Steward O. Neuroscience, 1983, 8: 791-797 Google Scholar

[31] Cooper L N, Bear M F. Nat Rev Neurosci, 2012, 13: :-810 Google Scholar

[32] Bi G Q, Poo M M. J Neurosci, 1998, 18: :-10472 Google Scholar

[33] Bienenstock E L, Cooper L N, Munro P W. 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. Physiol Rev, 2004, 84: 87-136 Google Scholar

[36] Bliss T V P, Lomo T. J Physiol, 1973, 232: 331-356 Google Scholar

[37] Mulkey R, Herron C, Malenka R. Science, 1993, 261: 1051-1055 Google Scholar

[38] Sjostrom P J, Gerstner W. Scholarpedia, 2010, 5: 1362-1055 Google Scholar

[39] Gütig R, Aharonov R, Rotter S, et al. J Neurosci, 2003, 23: 3697-3714 Google Scholar

[40] Rubin J, Lee D D, Sompolinsky H. Physl Rev Lett, 2001, 86: 364-367 Google Scholar

[41] van Rossum M C, Bi G Q, Turrigiano G G. J Neurosci, 2000, 20: :-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. Nat Mater, 2011, 10: 625-630 Google Scholar

[44] Chanthbouala A, Garcia V, Cherifi R O, et al. Nat Mater, 2012, 11: 860-864 Google Scholar

[45] Yang J J, Pickett M D, Li X, et al. Nat Nanotechnol, 2008, 3: :-433 Google Scholar

[46] Wuttig M, Yamada N. Nat Mater, 2007, 6: :-832 Google Scholar

[47] Kuzum D, Jeyasingh R G D, Lee B, et al. Nano Lett, 2012, 12: :-2186 Google Scholar

[48] Yang J J, Strukov D B, Stewart D R. Nat Nanotechnol, 2013, 8: :-24 Google Scholar

[49] Strukov D B, Snider G S, Stewart D R, et al. Nature, 2008, 453: 80-83 Google Scholar

[50] Chua L O, Kang S M. Proc IEEE, 1976, 64: :-223 Google Scholar

[51] Hickmott T W, Hiatt W R. Appl Phys Lett, 1965, 6: 106-108 Google Scholar

[52] Hickmott T W. J Appl Phys, 1962, 33: 2669-108 Google Scholar

[53] Chua L. Appl Phys A-Mater Sci Process, 2011, 102: 765-783 Google Scholar

[54] Rajendran B, Liu Y, Seo J S, et al. IEEE Trans Electron Dev, 2013, 60: :-253 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. Proc IEEE, 2012, 100: 1951-1970 Google Scholar

[57] Yang J J, Miao F, Pickett M D, et al. Nanotechnology, 2009, 20: 215201-1970 Google Scholar

[58] Yang Y, Gao P, Li L, et al. Nat Commun, 2014, 5: 4232-1970 Google Scholar

[59] Sarkar B, Lee B, Misra V. Semicond Sci Technol, 2015, 30: 105014-1970 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. Nanotechnology, 2013, 24: 384003-1970 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. Sci Rep, 2014, 4: 5333-1970 Google Scholar

[64] Gao B, Liu L, Kang J. Prog Nat Sci Mater Int, 2015, 25: 47-50 Google Scholar

[65] Wang Y-F, Lin Y-C, Wang I-T, et al. Sci Rep, 2015, 5: 10150-50 Google Scholar

[66] Yu S M, Wu Y, Jeyasingh R, et al. IEEE Trans Electron Dev, 2011, 58: 2729-2737 Google Scholar

[67] Gao B, Bi Y, Chen H Y, et al. ACS Nano, 2014, 8: 6998-7004 Google Scholar

[68] Choi H, Jung H, Lee J, et al. Nanotechnology, 2009, 20: 345201-7004 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. Proc IEEE, 2012, 100: :-2080 Google Scholar

[71] Kim S, Du C, Sheridan P, et al. Nano Lett, 2015, 15: 2203-2211 Google Scholar

[72] Valov I, Waser R, Jameson J R, et al. Nanotechnology, 2011, 22: 254003-2211 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. J Appl Phys, 1976, 47: 2767-2772 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. Google Scholar

[77] Lu W, Jeong D S, Kozicki M, et al. Electrochemical metallization cells-blending nanoionics into nanoelectronics? MRS Bull, 2012, 37: 124--130. Google Scholar

[78] Liu Q, Sun J, Lv H, et al. Adv Mater, 2012, 24: 1774-2772 Google Scholar

[79] Ohno T, Hasegawa T, Tsuruoka T, et al. Nat Mater, 2011, 10: 591-595 Google Scholar

[80] Atkinson R, Shiffrin R. 2nd ed. Psych Learn Motiv, 1968, 2: 89-195 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. IEEE Trans Electron Dev, 2011, 58: 1352-1360 Google Scholar

[83] Suri M, Querlioz D, Bichler O, et al. IEEE Trans Electron Dev, 2013, 60: 2402-2409 Google Scholar

[84] Mahalanabis D, Barnaby H J, Gonzalez-Velo Y, et al. Solid State Electron, 2014, 100: 39-44 Google Scholar

[85] Jo S H, Chang T, Ebong I, et al. Nano Lett, 2010, 10: 1297-1301 Google Scholar

[86] Kim K H, Gaba S, Wheeler D, et al. Nano Lett, 2012, 12: 389-395 Google Scholar

[87] Petersen C C, Malenka R C, Nicoll R A, et al. Proc Nat Acad Sci USA, 1998, 95: 4732-4737 Google Scholar

[88] O'Connor D H, Wittenberg G M, Wang S S-H. Proc Nat Acad Sci USA, 2005, 102: 9679-9684 Google Scholar

[89] Suri M, Bichler O, Querlioz D, et al. IEEE Trans Electron Dev, 2013, 60: 2402-2409 Google Scholar

[90] Li S Z, Zeng F, Chen C, et al. J Mater Chem C, 2013, 1: 5292-5298 Google Scholar

[91] Yang Y, Chen B, Lu W D. Adv Mater, 2015, 27: 7720-7727 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. IEEE Trans Electron Dev, 2013, 60: 3690-3695 Google Scholar

[94] Russo U, Kamalanathan D, Ielmini D, et al. IEEE Trans Electron Dev, 2009, 56: 1040-1047 Google Scholar

[95] {Å}kerman J. Science, 2005, 308: 508-510 Google Scholar

[96] Wang K L, Alzate J G, Amiri P K. J Phys D Appl Phys, 2013, 46: 074003-510 Google Scholar

[97] Augustine C, Mojumder N N, Fong X, et al. IEEE Sens J, 2012, 12: 756-766 Google Scholar

[98] Roy K, Fan D, Fong X, et al. IEEE J Emerg Sel Top Circuits Syst, 2015, 5: 5-16 Google Scholar

[99] Devolder T, Hayakawa J, Ito K, et al. Phys Rev Lett, 2008, 100: 057206-16 Google Scholar

[100] Zhang Y, Zhao W, Prenat G, et al. IEEE Trans Magn, 2013, 49: 4375-4378 Google Scholar

[101] Vincent A F, Larroque J, Locatelli N, et al. IEEE Trans Biomed Circuits Syst, 2015, 9: 166-174 Google Scholar

[102] Zeng Z M, Amiri P K, Rowlands G, et al. Appl Phys Lett, 2011, 98: 072512-174 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. Google Scholar

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

[105] Ovshinsky S R. Phys Rev Lett, 1968, 21: :-1453 Google Scholar

[106] Wong H S P, Raoux S, Kim S, et al. Proc IEEE, 2010, 98: 2201-2227 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. Nat Mater, 2005, 4: 347-352 Google Scholar

[109] Park J-B, Park G-S, Baik H-S, et al. J Electrochem Soc, 2007, 154: H139-H141 Google Scholar

[110] Loke D, Lee T H, Wang W J, et al. Science, 2012, 336: 1566-1569 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. J Emerg Technol Comput Syst, 2013, 9: 12-20 Google Scholar

[114] Eryilmaz S B, Kuzum D, Jeyasingh R, et al. Front Neurosci, 2014, 8: :-11 Google Scholar

[115] Schaller R R. IEEE Spectr, 1997, 34: 52-59 Google Scholar

[116] Mead C. Proc IEEE, 1990, 78: 1629-1636 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. 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. Google Scholar

[120] Bartolozzi C, Indiveri G. Neural Comput, 2007, 19: 2581-2603 Google Scholar

[121] Mack C A. IEEE Trans Semicond Manuf, 2011, 24: 202-207 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. Ann N Y Acad Sci, 2003, 1006: 146-163 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. Google Scholar

[125] Feldheim D L, Keating C D. Chem Soc Rev, 1998, 27: 1-12 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. Nature, 1986, 323: 533-536 Google Scholar

[128] Maass W. Neural Netw, 1997, 10: 1659-1671 Google Scholar

[129] Hodgkin A, Huxley A. Bull Math Biol, 1990, 52: 25-71 Google Scholar

[130] Izhikevich E M. Philos Trans A Math Phys Eng Sci, 2010, 368: 5061-5070 Google Scholar

[131] O'Reilly R C. Science, 2006, 314: :-94 Google Scholar

[132] Herz A V M, Gollisch T, Machens C K, et al. Science, 2006, 314: 80-85 Google Scholar

[133] Brüderle D, Petrovici M A, Vogginger B, et al. Biol Cybern, 2011, 104: 263-296 Google Scholar

[134] Arthur J V, Boahen K. IEEE Trans Circuits Syst I-Regul Pap, 2011, 58: 1034-1043 Google Scholar

[135] Rachmuth G, Poon C-S. HFSP J, 2008, 2: 156-166 Google Scholar

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

[137] Pickett M D, Medeiros-Ribeiro G, Williams R S. Nat Mater, 2013, 12: 114-117 Google Scholar

[138] Park S, Noh J, Choo M-L, et al. Nanotechnology, 2013, 24: 384009-117 Google Scholar

[139] Serrano-Gotarredona T, Prodromakis T, Linares-Barranco B. IEEE Circuits Syst Mag, 2013, 13: 74-88 Google Scholar