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                      10.3389/fnins.2011.00026]
                 [189]  Emelyanov AV, Nikiruy KE, Demin VA, Rylkov VV, Belov AI, Korolev DS, Gryaznov EG, Pavlov DA, Gorshkov ON, Mikhaylov
                      AN, Dimitrakis P. Yttria-stabilized zirconia cross-point memristive devices for neuromorphic applications. Microelectronic Engineering,
                      2019, 215: 110988. [doi: 10.1016/j.mee.2019.110988]
                 [190]  Demin  VA,  Nekhaev  DV,  Surazhevsky  IA,  Nikiruy  KE,  Emelyanov  AV,  Nikolaev  SN,  Rylkov  VV,  Kovalchuk  MV.  Necessary
                      conditions for STDP-based pattern recognition learning in a memristive spiking neural network. Neural Networks, 2021, 134: 64–75.
                      [doi: 10.1016/j.neunet.2020.11.005]
                 [191]  Burr GW, Shelby RM, Sebastian A, Kim S, Kim S, Sidler S, Virwani K, Ishii M, Narayanan P, Fumarola A, Sanches LL, Boybat I, Le
                      Gallo M, Moon K, Woo J, Hwang H, Leblebici Y. Neuromorphic computing using non-volatile memory. Advances in Physics: X, 2017,
                      2(1): 89–124. [doi: 10.1080/23746149.2016.1259585]
                 [192]  Midya R, Wang ZR, Asapu S, Joshi S, Li YN, Zhuo Y, Song WH, Jiang H, Upadhay N, Rao MY, Lin P, Li C, Xia QF, Yang JJ.
                      Artificial  neural  network  (ANN)  to  spiking  neural  network  (SNN)  converters  based  on  diffusive  memristors.  Advanced  Electronic
                      Materials, 2019, 5(9): 1900060. [doi: 10.1002/aelm.201900060]
                 [193]  Wang ZR, Joshi S, Savel’Ev S, et al. Fully memristive neural networks for pattern classification with unsupervised learning. Nature
                      Electronics, 2018, 1(2): 137–145. [doi: 10.1038/s41928-018-0023-2]

                 [194]  Jo SH, Chang T, Ebong I, Bhadviya BB, Mazumder P, Lu W. Nanoscale memristor device as synapse in neuromorphic systems. Nano
                      Letters, 2010, 10(4): 1297–1301. [doi: 10.1021/nl904092h]
                 [195]  Chen  L,  Xiong  XZ,  Liu  J.  A  survey  of  intelligent  chip  design  research  based  on  spiking  neural  networks.  IEEE  Access,  2022,  10:
                      89663–89686. [doi: 10.1109/ACCESS.2022.3200454]
                 [196]  Yu SM. Resistive Random Access Memory (RRAM). Cham: Springer, 2016. [doi: 10.1007/978-3-031-02030-8]
                 [197]  Shukla A, Prasad S, Lashkare S, Ganguly U. A case for multiple and parallel RRAMs as synaptic model for training SNNs. In: Proc. of
                      the 2018 Int’l Joint Conf. on Neural Networks (IJCNN). Rio de Janeiro: IEEE, 2018. 1–8. [doi: 10.1109/IJCNN.2018.8489429]
                 [198]  El Arrassi A, Gebregiorgis A, El Haddadi A, Hamdioui S. Energy-efficient SNN implementation using RRAM-based computation in-
                      memory (CIM). In: Proc. of the 30th IFIP/IEEE Int’l Conf. on Very Large Scale Integration (VLSI-SoC). Patras: IEEE, 2022. 1–6. [doi:
                      10.1109/VLSI-SoC54400.2022.9939654]
                 [199]  Wong HSP, Raoux S, Kim SB, Liang JL, Reifenberg JP, Rajendran B, Asheghi M, Goodson KE. Phase change memory. Proc. of the
                      IEEE, 2010, 98(12): 2201–2227. [doi: 10.1109/JPROC.2010.2070050]
                 [200]  Bohnstingl T, Šurina A, Fabre M, Demirağ Y, Frenkel C, Payvand M, Indiveri G, Pantazi A. Biologically-inspired training of spiking
                      recurrent neural networks with neuromorphic hardware. In: Proc. of the 4th IEEE Int’l Conf. on Artificial Intelligence Circuits and
                      Systems (AICAS). Incheon: IEEE, 2022. 218–221. [doi: 10.1109/AICAS54282.2022.9869963]
                 [201]  Titirsha T, Song SH, Das A, Krichmar J, Dutt N, Kandasamy N, Catthoor F. Endurance-aware mapping of spiking neural networks to
                      neuromorphic hardware. IEEE Trans. on Parallel and Distributed Systems, 2022, 33(2): 288–301. [doi: 10.1109/TPDS.2021.3065591]
                 [202]  Bader SD, Parkin SSP. Spintronics. Annual Review of Condensed Matter Physics, 2010, 1: 71–88. [doi: 10.1146/annurev-conmatphys-
                      070909-104123]
                 [203]  Diao  ZT,  Li  ZJ,  Wang  S,  Ding  YF,  Panchula  A,  Chen  E,  Wang  LC,  Huai  YM.  Spin-transfer  torque  switching  in  magnetic  tunnel
                      junctions and spin-transfer torque random access memory. Journal of Physics: Condensed Matter, 2007, 19(16): 165209. [doi: 10.1088/
                      0953-8984/19/16/165209]
                 [204]  Vincent AF, Larroque J, Locatelli N, Ben Romdhane N, Bichler O, Gamrat C, Zhao WS, Klein JO, Galdin-Retailleau S, Querlioz D.
                      Spin-transfer  torque  magnetic  memory  as  a  stochastic  memristive  synapse  for  neuromorphic  systems.  IEEE  Trans.  on  Biomedical
                      Circuits and Systems, 2015, 9(2): 166–174. [doi: 10.1109/TBCAS.2015.2414423]
                 [205]  Fong  X,  Kim  Y,  Yogendra  K,  Fan  DL,  Sengupta  A,  Raghunathan  A,  Roy  K.  Spin-transfer  torque  devices  for  logic  and  memory:
                      Prospects and perspectives. IEEE Trans. on Computer-aided Design of Integrated Circuits and Systems, 2016, 35(1): 1–22. [doi: 10.
                      1109/TCAD.2015.2481793]
                 [206]  Sengupta A, Parsa M, Han B, Roy K. Probabilistic deep spiking neural systems enabled by magnetic tunnel junction. IEEE Trans. on
                      Electron Devices, 2016, 63(7): 2963–2970. [doi: 10.1109/TED.2016.2568762]
                 [207]  Kulkarni SR, Kadetotad DV, Yin SH, Seo JS, Rajendran B. Neuromorphic hardware accelerator for SNN inference based on STT-RAM
                      crossbar arrays. In: Proc. of the 26th IEEE Int’l Conf. on Electronics, Circuits and Systems (ICECS). Genoa: IEEE, 2019. 438–441. [doi:
                      10.1109/ICECS46596.2019.8964886]
                 [208]  Brette  R,  Rudolph  M,  Carnevale  T,  et  al.  Simulation  of  networks  of  spiking  neurons:  A  review  of  tools  and  strategies.  Journal  of
                      Computational Neuroscience, 2007, 23(3): 349–398. [doi: 10.1007/s10827-007-0038-6]
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