{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T20:43:53Z","timestamp":1782333833221,"version":"3.54.5"},"reference-count":83,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,11,11]],"date-time":"2023-11-11T00:00:00Z","timestamp":1699660800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,11]],"date-time":"2023-11-11T00:00:00Z","timestamp":1699660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001792","name":"James Cook University","doi-asserted-by":"publisher","award":["Rising Star Fellowship"],"award-info":[{"award-number":["Rising Star Fellowship"]}],"id":[{"id":"10.13039\/501100001792","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001792","name":"James Cook University","doi-asserted-by":"publisher","award":["Domestic Research Training Program Scholarship"],"award-info":[{"award-number":["Domestic Research Training Program Scholarship"]}],"id":[{"id":"10.13039\/501100001792","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001792","name":"James Cook University","doi-asserted-by":"publisher","award":["Domestic Prestige Research Training Program Scholarship (DPRTPS),"],"award-info":[{"award-number":["Domestic Prestige Research Training Program Scholarship (DPRTPS),"]}],"id":[{"id":"10.13039\/501100001792","id-type":"DOI","asserted-by":"publisher"}]},{"name":"IBM","award":["IBM PhD Fellowship"],"award-info":[{"award-number":["IBM PhD Fellowship"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s00521-023-09135-2","type":"journal-article","created":{"date-parts":[[2023,11,11]],"date-time":"2023-11-11T11:01:33Z","timestamp":1699700493000},"page":"1569-1584","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Unsupervised character recognition with graphene memristive synapses"],"prefix":"10.1007","volume":"36","author":[{"given":"Ben","family":"Walters","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Corey","family":"Lammie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuangming","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohan V","family":"Jacob","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7975-3985","authenticated-orcid":false,"given":"Mostafa","family":"Rahimi Azghadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,11,11]]},"reference":[{"issue":"5","key":"9135_CR1","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1109\/TCT.1971.1083337","volume":"18","author":"L Chua","year":"1971","unstructured":"Chua L (1971) Memristor\u2014the missing circuit element. IEEE Trans Circuit Theory 18(5):507\u2013519. https:\/\/doi.org\/10.1109\/TCT.1971.1083337","journal-title":"IEEE Trans Circuit Theory"},{"issue":"3","key":"9135_CR2","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1049\/cds2.12018","volume":"15","author":"G Maranh\u00e3o","year":"2021","unstructured":"Maranh\u00e3o G, Guimar\u00e3es JG (2021) Low-power hybrid memristor-CMOS spiking neuromorphic STDP learning system. IET Circuits Devices Syst 15(3):237\u2013250. https:\/\/doi.org\/10.1049\/cds2.12018","journal-title":"IET Circuits Devices Syst"},{"issue":"5","key":"9135_CR3","doi-asserted-by":"publisher","first-page":"1900189","DOI":"10.1002\/aisy.201900189","volume":"2","author":"M Rahimi Azghadi","year":"2020","unstructured":"Rahimi Azghadi M, Chen YC, Eshraghian JK, Chen J, Lin CY, Amirsoleimani A et al (2020) Complementary metal-oxide semiconductor and memristive hardware for neuromorphic computing. Adv Intell Syst 2(5):1900189. https:\/\/doi.org\/10.1002\/aisy.201900189","journal-title":"Adv Intell Syst"},{"issue":"7191","key":"9135_CR4","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1038\/nature06932","volume":"453","author":"DB Strukov","year":"2008","unstructured":"Strukov DB, Snider GS, Stewart DR, Williams RS (2008) The missing memristor found. Nature 453(7191):80\u201383. https:\/\/doi.org\/10.1038\/nature06932","journal-title":"Nature"},{"issue":"8","key":"9135_CR5","doi-asserted-by":"publisher","first-page":"2006773","DOI":"10.1002\/adfm.202006773","volume":"31","author":"K Sun","year":"2021","unstructured":"Sun K, Chen J, Yan X (2021) The future of memristors: materials engineering and neural networks. Adv Funct Mater 31(8):2006773. https:\/\/doi.org\/10.1002\/adfm.202006773","journal-title":"Adv Funct Mater"},{"key":"9135_CR6","doi-asserted-by":"publisher","first-page":"56","DOI":"10.3389\/fnins.2016.00056","volume":"10","author":"S Ambrogio","year":"2016","unstructured":"Ambrogio S, Ciocchini N, Laudato M, Milo V, Pirovano A, Fantini P et al (2016) Unsupervised learning by spike timing dependent plasticity in phase change memory (PCM) synapses. Front Neurosci 10:56. https:\/\/doi.org\/10.3389\/fnins.2016.00056","journal-title":"Front Neurosci"},{"issue":"1","key":"9135_CR7","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1021\/nl803318d","volume":"9","author":"JP Velev","year":"2009","unstructured":"Velev JP, Duan CG, Burton JD, Smogunov A, Niranjan MK, Tosatti E et al (2009) Magnetic tunnel junctions with ferroelectric barriers: prediction of four resistance states from first principles. Nano Lett 9(1):427\u2013432. https:\/\/doi.org\/10.1021\/nl803318d","journal-title":"Nano Lett"},{"issue":"7251","key":"9135_CR8","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1038\/nature08128","volume":"460","author":"V Garcia","year":"2009","unstructured":"Garcia V, Fusil S, Bouzehouane K, Enouz-Vedrenne S, Mathur ND, Barth\u00e9l\u00e9my A et al (2009) Giant tunnel electroresistance for non-destructive readout of ferroelectric states. Nature 460(7251):81\u201384. https:\/\/doi.org\/10.1038\/nature08128","journal-title":"Nature"},{"issue":"2","key":"9135_CR9","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1021\/acs.jpclett.9b03181","volume":"11","author":"A Solanki","year":"2020","unstructured":"Solanki A, Guerrero A, Zhang Q, Bisquert J, Sum TC (2020) Interfacial mechanism for efficient resistive switching in ruddlesden-popper perovskites for non-volatile memories. J Phys Chem Lett 11(2):463\u2013470. https:\/\/doi.org\/10.1021\/acs.jpclett.9b03181","journal-title":"J Phys Chem Lett"},{"issue":"5","key":"9135_CR10","doi-asserted-by":"publisher","first-page":"2001258","DOI":"10.1002\/aelm.202001258","volume":"7","author":"M Ahn","year":"2021","unstructured":"Ahn M, Park Y, Lee SH, Chae S, Lee J, Heron JT et al (2021) Memristors based on (Zr, Hf, Nb, Ta, Mo, W) high-entropy oxides. Adv Electronic Mater 7(5):2001258. https:\/\/doi.org\/10.1002\/aelm.202001258","journal-title":"Adv Electronic Mater"},{"key":"9135_CR11","doi-asserted-by":"publisher","unstructured":"Zhang C, Zhou H, Chen S, Zhang G, Yu ZG, Chi D et al (2021) Recent progress on 2D materials-based artificial synapses. Critical Reviews in Solid State and Materials Sciences. 1\u201326. https:\/\/doi.org\/10.1080\/10408436.2021.1935212","DOI":"10.1080\/10408436.2021.1935212"},{"issue":"1","key":"9135_CR12","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1007\/s40820-021-00784-3","volume":"14","author":"KC Kwon","year":"2022","unstructured":"Kwon KC, Baek JH, Hong K, Kim SY, Jang HW (2022) Memristive devices based on two-dimensional transition metal chalcogenides for neuromorphic computing. Nano Micro Lett 14(1):58. https:\/\/doi.org\/10.1007\/s40820-021-00784-3","journal-title":"Nano Micro Lett"},{"issue":"2","key":"9135_CR13","doi-asserted-by":"publisher","first-page":"23204","DOI":"10.1007\/s11467-021-1114-5","volume":"17","author":"Z Zhou","year":"2021","unstructured":"Zhou Z, Yang F, Wang S, Wang L, Wang X, Wang C et al (2021) Emerging of two-dimensional materials in novel memristor. Front Phys 17(2):23204. https:\/\/doi.org\/10.1007\/s11467-021-1114-5","journal-title":"Front Phys"},{"key":"9135_CR14","doi-asserted-by":"publisher","unstructured":"Jacob MV, Rawat RS, Ouyang B, Bazaka K, Kumar DS, Taguchi D et\u00a0al (2015) Catalyst-free plasma enhanced growth of graphene from sustainable sources. Nano Lett 15(9):5702\u20135708. PMID: 26263025. https:\/\/doi.org\/10.1021\/acs.nanolett.5b01363","DOI":"10.1021\/acs.nanolett.5b01363"},{"issue":"1","key":"9135_CR15","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1038\/s42256-018-0007-y","volume":"1","author":"GW Burr","year":"2019","unstructured":"Burr GW (2019) A role for analogue memory in AI hardware. Nat Mach Intell 1(1):10\u201311. https:\/\/doi.org\/10.1038\/s42256-018-0007-y","journal-title":"Nat Mach Intell"},{"issue":"3","key":"9135_CR16","doi-asserted-by":"publisher","first-page":"1800544","DOI":"10.1002\/admt.201800544","volume":"4","author":"J Wang","year":"2019","unstructured":"Wang J, Zhuge F (2019) Memristive synapses for brain-inspired computing. Adv Mater Technol 4(3):1800544. https:\/\/doi.org\/10.1002\/admt.201800544","journal-title":"Adv Mater Technol"},{"key":"9135_CR17","doi-asserted-by":"publisher","unstructured":"Xiao Z, Yan B, Zhang T, Huang R, Yang Y (2022) Memristive devices based hardware for unlabeled data processing. Neuromorphic Comput Eng 2(2):022003. https:\/\/doi.org\/10.1088\/2634-4386\/ac734a","DOI":"10.1088\/2634-4386\/ac734a"},{"issue":"10","key":"9135_CR18","doi-asserted-by":"publisher","first-page":"2200077","DOI":"10.1002\/aisy.202200077","volume":"4","author":"M Huang","year":"2022","unstructured":"Huang M, Li Z, Zhu H (2022) Recent advances of graphene and related materials in artificial intelligence. Adv Intell Syst 4(10):2200077","journal-title":"Adv Intell Syst"},{"issue":"3","key":"9135_CR19","doi-asserted-by":"publisher","first-page":"433","DOI":"10.3390\/mi13030433","volume":"13","author":"R Li","year":"2022","unstructured":"Li R, Huang P, Feng Y, Zhou Z, Zhang Y, Ding X et al (2022) Hardware demonstration of srdp neuromorphic computing with online unsupervised learning based on memristor synapses. Micromachines 13(3):433","journal-title":"Micromachines"},{"issue":"11","key":"9135_CR20","doi-asserted-by":"publisher","first-page":"4517","DOI":"10.1109\/TED.2019.2931135","volume":"66","author":"Y Xiang","year":"2019","unstructured":"Xiang Y, Huang P, Zhao Y, Zhao M, Gao B, Wu H et al (2019) Impacts of state instability and retention failure of filamentary analog rram on the performance of deep neural network. IEEE Trans Electron Dev 66(11):4517\u20134522. https:\/\/doi.org\/10.1109\/TED.2019.2931135","journal-title":"IEEE Trans Electron Dev"},{"issue":"1","key":"9135_CR21","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1109\/TETCI.2020.3035164","volume":"6","author":"R Vaila","year":"2020","unstructured":"Vaila R, Chiasson J, Saxena V (2020) A deep unsupervised feature learning spiking neural network with binarized classification layers for the emnist classification. IEEE Trans Emerg Top Comput Intell 6(1):124\u2013135. https:\/\/doi.org\/10.1109\/TETCI.2020.3035164","journal-title":"IEEE Trans Emerg Top Comput Intell"},{"issue":"5","key":"9135_CR22","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1109\/JPROC.2021.3072172","volume":"109","author":"R Chellappa","year":"2021","unstructured":"Chellappa R, Theodoridis S, van Schaik A (2021) Advances in machine learning and deep neural networks. Proc IEEE 109(5):607\u2013611. https:\/\/doi.org\/10.1109\/JPROC.2021.3072172","journal-title":"Proc IEEE"},{"key":"9135_CR23","doi-asserted-by":"publisher","first-page":"31","DOI":"10.3389\/fnins.2019.00031","volume":"13","author":"W Zhang","year":"2019","unstructured":"Zhang W, Li P (2019) Information-theoretic intrinsic plasticity for online unsupervised learning in spiking neural networks. Front Neurosci 13:31. https:\/\/doi.org\/10.3389\/fnins.2019.00031","journal-title":"Front Neurosci"},{"key":"9135_CR24","doi-asserted-by":"publisher","first-page":"987","DOI":"10.3389\/fnins.2018.00987","volume":"12","author":"D Zambrano","year":"2019","unstructured":"Zambrano D, Nusselder R, Scholte HS, Boht\u00e9 SM (2019) Sparse computation in adaptive spiking neural networks. Front Neurosci 12:987. https:\/\/doi.org\/10.3389\/fnins.2018.00987","journal-title":"Front Neurosci"},{"key":"9135_CR25","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1162\/neco_a_01086","volume":"05","author":"F Zenke","year":"2017","unstructured":"Zenke F, Ganguli S (2017) SuperSpike: supervised learning in multilayer spiking neural networks. Neural Comput 05:30. https:\/\/doi.org\/10.1162\/neco_a_01086","journal-title":"Neural Comput"},{"issue":"6","key":"9135_CR26","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1109\/MSP.2019.2931595","volume":"36","author":"EO Neftci","year":"2019","unstructured":"Neftci EO, Mostafa H, Zenke F (2019) Surrogate gradient learning in spiking neural networks: bringing the power of gradient-based optimization to spiking neural networks. IEEE Signal Process Mag 36(6):51\u201363. https:\/\/doi.org\/10.1109\/MSP.2019.2931595","journal-title":"IEEE Signal Process Mag"},{"key":"9135_CR27","unstructured":"Eshraghian JK, Ward M, Neftci E, Wang X, Lenz G, Dwivedi G, et\u00a0al. (2021) Training spiking neural networks using lessons from deep learning. arXiv preprint arXiv:2109.12894"},{"issue":"2","key":"9135_CR28","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1109\/TBCAS.2016.2618351","volume":"11","author":"MR Azghadi","year":"2017","unstructured":"Azghadi MR, Linares-Barranco B, Abbott D, Leong PH (2017) A hybrid CMOS-memristor neuromorphic synapse. IEEE Trans Biomed Circuits Syst 11(2):434\u2013445. https:\/\/doi.org\/10.1109\/TBCAS.2016.2618351","journal-title":"IEEE Trans Biomed Circuits Syst"},{"key":"9135_CR29","doi-asserted-by":"publisher","unstructured":"Rahimi Azghadi M, Al-Sarawi S, Abbott D, Iannella N (2013) A neuromorphic VLSI design for spike timing and rate based synaptic plasticity. Neural Netw 45:70\u201382. Neuromorphic Engineering: From Neural Systems to Brain-Like Engineered Systems. https:\/\/doi.org\/10.1016\/j.neunet.2013.03.003","DOI":"10.1016\/j.neunet.2013.03.003"},{"key":"9135_CR30","doi-asserted-by":"publisher","unstructured":"Gq Bi, Mm Poo (1998) Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J Neurosci 18(24):10464\u201310472. https:\/\/doi.org\/10.1523\/JNEUROSCI.18-24-10464.1998. www.jneurosci.org\/content\/18\/24\/10464.full.pdf","DOI":"10.1523\/JNEUROSCI.18-24-10464.1998"},{"key":"9135_CR31","doi-asserted-by":"publisher","first-page":"99","DOI":"10.3389\/fncom.2015.00099","volume":"9","author":"P Diehl","year":"2015","unstructured":"Diehl P, Cook M (2015) Unsupervised learning of digit recognition using spike-timing-dependent plasticity. Front Comput Neurosci 9:99. https:\/\/doi.org\/10.3389\/fncom.2015.00099","journal-title":"Front Comput Neurosci"},{"key":"9135_CR32","doi-asserted-by":"publisher","first-page":"27","DOI":"10.3389\/fnins.2021.580909","volume":"15","author":"S Brivio","year":"2021","unstructured":"Brivio S, Ly DRB, Vianello E, Spiga S (2021) Non-linear memristive synaptic dynamics for efficient unsupervised learning in spiking neural networks. Front Neurosci 15:27. https:\/\/doi.org\/10.3389\/fnins.2021.580909","journal-title":"Front Neurosci"},{"issue":"12","key":"9135_CR33","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1109\/TNANO.2013.2250995","volume":"05","author":"D Querlioz","year":"2013","unstructured":"Querlioz D, Bichler O, Dollfus P, Gamrat C (2013) Immunity to device variations in a spiking neural network with memristive nanodevices. IEEE Trans Nanotechnol 05(12):288\u2013295. https:\/\/doi.org\/10.1109\/TNANO.2013.2250995","journal-title":"IEEE Trans Nanotechnol"},{"issue":"45","key":"9135_CR34","doi-asserted-by":"publisher","first-page":"25479","DOI":"10.1021\/acsami.5b08541","volume":"7","author":"S Kim","year":"2015","unstructured":"Kim S, Yoon J, Kim HD, Choi SJ (2015) Carbon nanotube synaptic transistor network for pattern recognition. ACS Appl Mater Interfaces 7(45):25479\u201325486. https:\/\/doi.org\/10.1021\/acsami.5b08541","journal-title":"ACS Appl Mater Interfaces"},{"key":"9135_CR35","doi-asserted-by":"publisher","first-page":"91","DOI":"10.3389\/fnins.2017.00091","volume":"11","author":"M Hansen","year":"2017","unstructured":"Hansen M, Zahari F, Ziegler M, Kohlstedt H (2017) Double-barrier memristive devices for unsupervised learning and pattern recognition. Front Neurosci 11:91. https:\/\/doi.org\/10.3389\/fnins.2017.00091","journal-title":"Front Neurosci"},{"key":"9135_CR36","doi-asserted-by":"publisher","first-page":"812","DOI":"10.3389\/fnins.2019.00812","volume":"13","author":"Y Guo","year":"2019","unstructured":"Guo Y, Wu H, Gao B, Qian H (2019) Unsupervised learning on resistive memory array based spiking neural networks. Front Neurosci 13:812. https:\/\/doi.org\/10.3389\/fnins.2019.00812","journal-title":"Front Neurosci"},{"key":"9135_CR37","doi-asserted-by":"publisher","unstructured":"Nandakumar SR, Rajendran B (2020) Bio-mimetic synaptic plasticity and learning in a sub-500mV Cu\/SiO2\/W memristor. Microelectronic Eng, 226. https:\/\/doi.org\/10.1016\/j.mee.2020.111290","DOI":"10.1016\/j.mee.2020.111290"},{"issue":"1","key":"9135_CR38","doi-asserted-by":"publisher","first-page":"2514","DOI":"10.1038\/s41467-018-04933-y","volume":"9","author":"I Boybat","year":"2018","unstructured":"Boybat I, Le Gallo M, Nandakumar SR, Moraitis T, Parnell T, Tuma T et al (2018) Neuromorphic computing with multi-memristive synapses. Nat Commun 9(1):2514. https:\/\/doi.org\/10.1038\/s41467-018-04933-y","journal-title":"Nat Commun"},{"key":"9135_CR39","doi-asserted-by":"publisher","unstructured":"Bill J, Legenstein R (2014) A compound memristive synapse model for statistical learning through STDP in spiking neural networks. Front Neurosci, 8. https:\/\/doi.org\/10.3389\/fnins.2014.00412","DOI":"10.3389\/fnins.2014.00412"},{"key":"9135_CR40","doi-asserted-by":"publisher","first-page":"482","DOI":"10.3389\/fnins.2016.00482","volume":"10","author":"E Covi","year":"2016","unstructured":"Covi E, Brivio S, Serb A, Prodromakis T, Fanciulli M, Spiga S (2016) Analog memristive synapse in spiking networks implementing unsupervised learning. Front Neurosci 10:482. https:\/\/doi.org\/10.3389\/fnins.2016.00482","journal-title":"Front Neurosci"},{"issue":"17","key":"9135_CR41","doi-asserted-by":"publisher","first-page":"13479","DOI":"10.1007\/s00521-020-04755-4","volume":"32","author":"L Qu","year":"2020","unstructured":"Qu L, Zhao Z, Wang L, Wang Y (2020) Efficient and hardware-friendly methods to implement competitive learning for spiking neural networks. Neural Comput Appl 32(17):13479\u201313490. https:\/\/doi.org\/10.1007\/s00521-020-04755-4","journal-title":"Neural Comput Appl"},{"key":"9135_CR42","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.neunet.2020.11.005","volume":"134","author":"VA Demin","year":"2021","unstructured":"Demin VA, Nekhaev DV, Surazhevsky IA, Nikiruy KE, Emelyanov AV, Nikolaev SN et al (2021) Necessary conditions for STDP-based pattern recognition learning in a memristive spiking neural network. Neural Netw 134:64\u201375. https:\/\/doi.org\/10.1016\/j.neunet.2020.11.005","journal-title":"Neural Netw"},{"issue":"4","key":"9135_CR43","doi-asserted-by":"publisher","first-page":"1625","DOI":"10.1007\/s10825-021-01719-2","volume":"20","author":"Z Hajiabadi","year":"2021","unstructured":"Hajiabadi Z, Shalchian M (2021) Memristor-based synaptic plasticity and unsupervised learning of spiking neural networks. J Comput Electronics 20(4):1625\u20131636. https:\/\/doi.org\/10.1007\/s10825-021-01719-2","journal-title":"J Comput Electronics"},{"key":"9135_CR44","doi-asserted-by":"publisher","DOI":"10.5402\/2012\/501686","volume":"2012","author":"Y Kopelevich","year":"2012","unstructured":"Kopelevich Y, Bud\u2019ko S, Cooper DR, D\u2019Anjou B, Ghattamaneni N, Harack B et al (2012) Experimental review of graphene. ISRN Condensed Matter Phys 2012:501686. https:\/\/doi.org\/10.5402\/2012\/501686","journal-title":"ISRN Condensed Matter Phys"},{"key":"9135_CR45","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.eml.2017.01.008","volume":"13","author":"D Akinwande","year":"2017","unstructured":"Akinwande D, Brennan CJ, Bunch JS, Egberts P, Felts JR, Gao H et al (2017) A review on mechanics and mechanical properties of 2D materials-Graphene and beyond. Extreme Mech Lett 13:42\u201377. https:\/\/doi.org\/10.1016\/j.eml.2017.01.008","journal-title":"Extreme Mech Lett"},{"key":"9135_CR46","doi-asserted-by":"publisher","unstructured":"Chen Y, Zhou Y, Zhuge F, Tian B, Yan M, Li Y, et\u00a0al (2019) Graphene-ferroelectric transistors as complementary synapses for supervised learning in spiking neural network. 2D Mater Appl 3(1):31. https:\/\/doi.org\/10.1038\/s41699-019-0114-6","DOI":"10.1038\/s41699-019-0114-6"},{"issue":"1","key":"9135_CR47","doi-asserted-by":"publisher","first-page":"5474","DOI":"10.1038\/s41467-020-19203-z","volume":"11","author":"TF Schranghamer","year":"2020","unstructured":"Schranghamer TF, Oberoi A, Das S (2020) Graphene memristive synapses for high precision neuromorphic computing. Nat Commun 11(1):5474. https:\/\/doi.org\/10.1038\/s41467-020-19203-z","journal-title":"Nat Commun"},{"key":"9135_CR48","doi-asserted-by":"publisher","unstructured":"Sun Y, Lin Y, Zubair A, Xie D, Palacios T (2021) WSe2\/graphene heterojunction synaptic phototransistor with both electrically and optically tunable plasticity. 2D Mater 8(3):035034. https:\/\/doi.org\/10.1088\/2053-1583\/abfa6a","DOI":"10.1088\/2053-1583\/abfa6a"},{"issue":"1","key":"9135_CR49","doi-asserted-by":"publisher","first-page":"9473","DOI":"10.1038\/s41598-020-66413-y","volume":"10","author":"H Abunahla","year":"2020","unstructured":"Abunahla H, Halawani Y, Alazzam A, Mohammad B (2020) NeuroMem: analog graphene-based resistive memory for artificial neural networks. Sci Rep 10(1):9473. https:\/\/doi.org\/10.1038\/s41598-020-66413-y","journal-title":"Sci Rep"},{"issue":"16","key":"9135_CR50","doi-asserted-by":"publisher","DOI":"10.1063\/5.0003696","volume":"116","author":"M Qi","year":"2020","unstructured":"Qi M, Cao S, Yang L, You Q, Shi L, Wu Z (2020) Uniform multilevel switching of graphene oxide-based RRAM achieved by embedding with gold nanoparticles for image pattern recognition. Appl Phys Lett 116(16):163503. https:\/\/doi.org\/10.1063\/5.0003696","journal-title":"Appl Phys Lett"},{"key":"9135_CR51","doi-asserted-by":"publisher","first-page":"17","DOI":"10.3389\/fmats.2020.00017","volume":"7","author":"FJ Romero","year":"2020","unstructured":"Romero FJ, Toral A, Medina-Rull A, Moraila-Martinez CL, Morales DP, Ohata A et al (2020) Resistive switching in graphene oxide. Front Mater 7:17. https:\/\/doi.org\/10.3389\/fmats.2020.00017","journal-title":"Front Mater"},{"key":"9135_CR52","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1016\/j.carbon.2015.01.011","volume":"85","author":"S Porro","year":"2015","unstructured":"Porro S, Accornero E, Pirri CF, Ricciardi C (2015) Memristive devices based on graphene oxide. Carbon 85:383\u2013396. https:\/\/doi.org\/10.1016\/j.carbon.2015.01.011","journal-title":"Carbon"},{"issue":"24","key":"9135_CR53","doi-asserted-by":"publisher","first-page":"20237","DOI":"10.1021\/acsami.8b04685","volume":"10","author":"B Liu","year":"2018","unstructured":"Liu B, Liu Z, Chiu IS, Di M, Wu Y, Wang JC et al (2018) Programmable synaptic metaplasticity and below femtojoule spiking energy realized in graphene-based neuromorphic memristor. ACS Appl Mater Interfaces 10(24):20237\u201320243. https:\/\/doi.org\/10.1021\/acsami.8b04685","journal-title":"ACS Appl Mater Interfaces"},{"issue":"10","key":"9135_CR54","doi-asserted-by":"publisher","DOI":"10.1063\/1.5108899","volume":"115","author":"A Krishnaprasad","year":"2019","unstructured":"Krishnaprasad A, Choudhary N, Das S, Dev D, Kalita H, Chung HS et al (2019) Electronic synapses with near-linear weight update using MoS2\/graphene memristors. Appl Phys Lett 115(10):103104. https:\/\/doi.org\/10.1063\/1.5108899","journal-title":"Appl Phys Lett"},{"key":"9135_CR55","doi-asserted-by":"publisher","unstructured":"Feng X, Liu X, Ang KW (2020) 2D photonic memristor beyond graphene: progress and prospects. Nanophotonics. https:\/\/doi.org\/10.1515\/nanoph-2019-0543","DOI":"10.1515\/nanoph-2019-0543"},{"issue":"4","key":"9135_CR56","doi-asserted-by":"publisher","first-page":"2005443","DOI":"10.1002\/adfm.202005443","volume":"31","author":"G Cao","year":"2021","unstructured":"Cao G, Meng P, Chen J, Liu H, Bian R, Zhu C et al (2021) 2D material based synaptic devices for neuromorphic computing. Adv Funct Mater 31(4):2005443. https:\/\/doi.org\/10.1002\/adfm.202005443","journal-title":"Adv Funct Mater"},{"key":"9135_CR57","doi-asserted-by":"publisher","unstructured":"Yalagala B, Khandelwal S, J D, Badhulika S (2019) Wirelessly destructible MgO-PVP-Graphene composite based flexible transient memristor for security applications. Mater Sci Semiconductor Process 104:104673. https:\/\/doi.org\/10.1016\/j.mssp.2019.104673","DOI":"10.1016\/j.mssp.2019.104673"},{"key":"9135_CR58","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1146\/annurev.neuro.24.1.139","volume":"24","author":"G Bi","year":"2001","unstructured":"Bi G, Poo M (2001) Synaptic modification by correlated activity: Hebb\u2019s postulate revisited. Annu Rev Neurosci 24:139\u201366. https:\/\/doi.org\/10.1146\/annurev.neuro.24.1.139","journal-title":"Annu Rev Neurosci"},{"key":"9135_CR59","doi-asserted-by":"publisher","unstructured":"Sahu DP, Jetty P, Jammalamadaka SN (2021) Graphene oxide based synaptic memristor device for neuromorphic computing. Nanotechnology 32(15):155701. https:\/\/doi.org\/10.1088\/1361-6528\/abd978","DOI":"10.1088\/1361-6528\/abd978"},{"key":"9135_CR60","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1109\/OJNANO.2020.3041198","volume":"1","author":"H Wang","year":"2020","unstructured":"Wang H, Laurenciu NC, Jiang Y, Cotofana SD (2020) Compact graphene-based spiking neural network with unsupervised learning capabilities. IEEE Open J Nanotechnol 1:135\u2013144. https:\/\/doi.org\/10.1109\/OJNANO.2020.3041198","journal-title":"IEEE Open J Nanotechnol"},{"key":"9135_CR61","doi-asserted-by":"publisher","unstructured":"Wang H, Laurenciu NC, Jiang Y, Cotofana S (2021) Graphene-based artificial synapses with tunable plasticity. J Emerg Technol Comput Syst 17(4). https:\/\/doi.org\/10.1145\/3447778","DOI":"10.1145\/3447778"},{"key":"9135_CR62","doi-asserted-by":"crossref","unstructured":"Wang Z, Liu C, Deng Y, Huang Z, He S, Guo D (2020) Carbon-based spiking neural network implemented with single-electron transistor and memristor for visual perception. In: 2020 IEEE 14th international conference on anti-counterfeiting, security, and identification (ASID), pp 143\u2013146","DOI":"10.1109\/ASID50160.2020.9271721"},{"key":"9135_CR63","doi-asserted-by":"publisher","unstructured":"Wang H, Cucu\u00a0Laurenciu N, Cotofana S (2021) A reconfigurable graphene-based spiking neural network architecture. IEEE Open J Nanotechnol, p 1. https:\/\/doi.org\/10.1109\/OJNANO.2021.3094761","DOI":"10.1109\/OJNANO.2021.3094761"},{"key":"9135_CR64","doi-asserted-by":"publisher","first-page":"168963","DOI":"10.1109\/ACCESS.2019.2954753","volume":"7","author":"B Hajri","year":"2019","unstructured":"Hajri B, Aziza H, Mansour MM, Chehab A (2019) RRAM device models: a comparative analysis with experimental validation. IEEE Access 7:168963\u2013168980. https:\/\/doi.org\/10.1109\/ACCESS.2019.2954753","journal-title":"IEEE Access"},{"key":"9135_CR65","first-page":"22","volume":"04","author":"Y Pershin","year":"2012","unstructured":"Pershin Y, Di Ventra M (2012) SPICE model of memristive devices with threshold. Radioengineering 04:22","journal-title":"Radioengineering"},{"issue":"8","key":"9135_CR66","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1109\/tcsii.2015.2433536","volume":"62","author":"S Kvatinsky","year":"2015","unstructured":"Kvatinsky S, Ramadan M, Friedman EG, Kolodny A (2015) VTEAM: a general model for voltage-controlled memristors. IEEE Trans Circuits Syst II Exp Briefs 62(8):786\u2013790. https:\/\/doi.org\/10.1109\/tcsii.2015.2433536","journal-title":"IEEE Trans Circuits Syst II Exp Briefs"},{"issue":"12","key":"9135_CR67","doi-asserted-by":"publisher","first-page":"3151","DOI":"10.1109\/TCAD.2018.2791468","volume":"37","author":"I Messaris","year":"2018","unstructured":"Messaris I, Serb A, Stathopoulos S, Khiat A, Nikolaidis S, Prodromakis T (2018) A data-driven verilog-A ReRAM model. IEEE Trans Comput Aided Design Integrated Circuits Syst 37(12):3151\u20133162. https:\/\/doi.org\/10.1109\/TCAD.2018.2791468","journal-title":"IEEE Trans Comput Aided Design Integrated Circuits Syst"},{"key":"9135_CR68","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.carbon.2016.11.005","volume":"112","author":"MV Jacob","year":"2017","unstructured":"Jacob MV, Taguchi D, Iwamoto M, Bazaka K, Rawat RS (2017) Resistive switching in graphene-organic device: charge transport properties of graphene-organic device through electric field induced optical second harmonic generation and charge modulation spectroscopy [Journal Article]. Carbon 112:111\u2013116. https:\/\/doi.org\/10.1016\/j.carbon.2016.11.005","journal-title":"Carbon"},{"key":"9135_CR69","doi-asserted-by":"publisher","unstructured":"Rahimi\u00a0Azghadi M, Moradi S, Fasnacht D, Ozdas M, Indiveri G (2015) Programmable spike-timing-dependent plasticity learning circuits in neuromorphic VLSI architectures. ACM J Emerg Technol Comput Syst 12(2). https:\/\/doi.org\/10.1145\/2658998","DOI":"10.1145\/2658998"},{"issue":"6","key":"9135_CR70","doi-asserted-by":"publisher","first-page":"1569","DOI":"10.1109\/TNN.2003.820440","volume":"14","author":"EM Izhikevich","year":"2003","unstructured":"Izhikevich EM (2003) Simple model of spiking neurons. IEEE Trans Neural Netw 14(6):1569\u20131572. https:\/\/doi.org\/10.1109\/TNN.2003.820440","journal-title":"IEEE Trans Neural Netw"},{"issue":"1","key":"9135_CR71","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/BF02459568","volume":"52","author":"AL Hodgkin","year":"1990","unstructured":"Hodgkin AL, Huxley AF (1990) A quantitative description of membrane current and its application to conduction and excitation in nerve. Bull Math Biol 52(1):25\u201371. https:\/\/doi.org\/10.1007\/BF02459568","journal-title":"Bull Math Biol"},{"key":"9135_CR72","first-page":"620","volume":"9","author":"L Lapique","year":"1907","unstructured":"Lapique L (1907) Recherches quantitatives sur l\u2019excitation electrique des nerfs traitee comme une polarization. J Physiol Pathol 9:620\u2013635","journal-title":"J Physiol Pathol"},{"key":"9135_CR73","doi-asserted-by":"publisher","first-page":"26","DOI":"10.3389\/fnins.2011.00026","volume":"5","author":"C Zamarreno-Ramos","year":"2011","unstructured":"Zamarreno-Ramos C, Camunas-Mesa LA, Perez-Carrasco JA, Masquelier T, Serrano-Gotarredona T, Linares-Barranco B (2011) On spike-timing-dependent-plasticity, memristive devices, and building a self-learning visual cortex. Front Neurosci 5:26. https:\/\/doi.org\/10.3389\/fnins.2011.00026","journal-title":"Front Neurosci"},{"key":"9135_CR74","doi-asserted-by":"publisher","unstructured":"Lammie C, Hamilton T, Azghadi MR (2018 ) Unsupervised character recognition with a simplified FPGA neuromorphic system. IEEE Explore, pp 1\u20135. https:\/\/doi.org\/10.1109\/ISCAS.2018.8351532","DOI":"10.1109\/ISCAS.2018.8351532"},{"issue":"9","key":"9135_CR75","doi-asserted-by":"publisher","first-page":"919","DOI":"10.1038\/78829","volume":"3","author":"S Song","year":"2000","unstructured":"Song S, Miller KD, Abbott LF (2000) Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat Neurosci 3(9):919\u2013926. https:\/\/doi.org\/10.1038\/78829","journal-title":"Nat Neurosci"},{"issue":"5","key":"9135_CR76","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1109\/TETCI.2018.2829914","volume":"2","author":"X Shi","year":"2018","unstructured":"Shi X, Zeng Z, Yang L, Huang Y (2018) Memristor-based circuit design for neuron with homeostatic plasticity. IEEE Trans Emer Top Comput Intell 2(5):359\u2013370. https:\/\/doi.org\/10.1109\/TETCI.2018.2829914","journal-title":"IEEE Trans Emer Top Comput Intell"},{"key":"9135_CR77","doi-asserted-by":"publisher","unstructured":"Lazar A, Pipa G, Triesch J (2007) Fading memory and time series prediction in recurrent networks with different forms of plasticity. Neural Netw 20(3):312\u2013322. Echo State Networks and Liquid State Machines. https:\/\/doi.org\/10.1016\/j.neunet.2007.04.020","DOI":"10.1016\/j.neunet.2007.04.020"},{"key":"9135_CR78","doi-asserted-by":"publisher","unstructured":"Mihalas S, Niebur E (2009) A generalized linear integrate-and-fire neural model produces diverse spiking behaviors. Neural Comput 21(3):704\u2013718. PMID: 18928368. https:\/\/doi.org\/10.1162\/neco.2008.12-07-680","DOI":"10.1162\/neco.2008.12-07-680"},{"key":"9135_CR79","doi-asserted-by":"publisher","unstructured":"Akiba T, Sano S, Yanase T, Ohta T, Koyama M (2019) Optuna: a next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery; data mining. KDD \u201919. New York, NY, USA: Association for Computing Machinery, pp 2623\u20132631. Available from: https:\/\/doi.org\/10.1145\/3292500.3330701","DOI":"10.1145\/3292500.3330701"},{"issue":"1","key":"9135_CR80","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1109\/TNANO.2015.2493960","volume":"15","author":"R Naous","year":"2016","unstructured":"Naous R, Al-Shedivat M, Salama KN (2016) Stochasticity modeling in memristors. IEEE Trans Nanotechnol 15(1):15\u201328. https:\/\/doi.org\/10.1109\/TNANO.2015.2493960","journal-title":"IEEE Trans Nanotechnol"},{"key":"9135_CR81","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1039\/C8FD00114F","volume":"213","author":"M Payvand","year":"2019","unstructured":"Payvand M, Nair MV, M\u00fcller LK, Indiveri G (2019) A neuromorphic systems approach to in-memory computing with non-ideal memristive devices: from mitigation to exploitation. Faraday Discussions 213:487\u2013510","journal-title":"Faraday Discussions"},{"issue":"2","key":"9135_CR82","doi-asserted-by":"publisher","DOI":"10.1063\/5.0035707","volume":"9","author":"JL Rieck","year":"2021","unstructured":"Rieck JL, Hensling FV, Dittmann R (2021) Trade-off between variability and retention of memristive epitaxial SrTiO3 devices. APL Mater 9(2):021110","journal-title":"APL Mater"},{"key":"9135_CR83","doi-asserted-by":"crossref","unstructured":"Ford AJ, Jha R (2020) Memristive device variability performance impact on neuromorphic machine learning hardware. In: (2020) 11th International green and sustainable computing workshops (IGSC). IEEE, pp 1\u20137","DOI":"10.1109\/IGSC51522.2020.9291114"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09135-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09135-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09135-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T18:14:30Z","timestamp":1704996870000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09135-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,11]]},"references-count":83,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["9135"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09135-2","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,11]]},"assertion":[{"value":"20 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 November 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not Applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not Applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"All authors.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}