{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T00:09:41Z","timestamp":1777939781622,"version":"3.51.4"},"reference-count":49,"publisher":"American Association for the Advancement of Science (AAAS)","content-domain":{"domain":["spj.science.org"],"crossmark-restriction":true},"short-container-title":["Intell Comput"],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:p>Photonic technologies offer great prospects for novel, ultrafast, energy-efficient, and hardware-friendly neuromorphic (brain-like) computing platforms. Moreover, neuromorphic photonic approaches based on ubiquitous, technology-mature, and low-cost vertical-cavity surface-emitting lasers (VCSELs) (devices found in fiber-optic transmitters, mobile phones, and automotive sensors) are of particular interest. Given that VCSELs have shown the ability to realize neuronal optical spiking responses (at ultrafast GHz rates), their use in spike-based information-processing systems has been proposed. In this study, spiking neural network (SNN) operation, based on a hardware-friendly photonic system of just one VCSEL, is reported alongside a novel binary weight \u201csignificance\u201d training scheme that fully capitalizes on the discrete nature of the optical spikes used by the SNN to process input information. The VCSEL-based photonic SNN was tested with a highly complex multivariate classification task (MADELON) before its performance was compared using a traditional least-squares training method and an alternative novel binary weighting scheme. Excellent classification accuracies of &gt;94% were achieved by both training methods, exceeding the benchmark performance of the dataset in a fraction of the processing time. The newly reported training scheme also dramatically reduces the training set size requirements and the number of trained nodes (\u22641% of the total network node count). This VCSEL-based photonic SNN, in combination with the reported \u201csignificance\u201d weighting scheme, therefore grants ultrafast spike-based optical processing highly reduced training requirements and hardware complexity for potential application in future neuromorphic systems and artificial intelligence applications.<\/jats:p>","DOI":"10.34133\/icomputing.0031","type":"journal-article","created":{"date-parts":[[2023,6,13]],"date-time":"2023-06-13T18:11:06Z","timestamp":1686679866000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark_01","source":"Crossref","is-referenced-by-count":20,"title":["Photonic Spiking Neural Networks with Highly Efficient Training Protocols for Ultrafast Neuromorphic Computing Systems"],"prefix":"10.34133","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6592-8465","authenticated-orcid":true,"given":"Dafydd","family":"Owen-Newns","sequence":"first","affiliation":[{"name":"Institute of Photonics, SUPA Department of Physics, \rUniversity of Strathclyde, Glasgow, UK."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6316-5265","authenticated-orcid":false,"given":"Joshua","family":"Robertson","sequence":"additional","affiliation":[{"name":"Institute of Photonics, SUPA Department of Physics, \rUniversity of Strathclyde, Glasgow, UK."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4493-9426","authenticated-orcid":false,"given":"Mat\u011bj","family":"Hejda","sequence":"additional","affiliation":[{"name":"Institute of Photonics, SUPA Department of Physics, \rUniversity of Strathclyde, Glasgow, UK."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4448-9034","authenticated-orcid":false,"given":"Antonio","family":"Hurtado","sequence":"additional","affiliation":[{"name":"Institute of Photonics, SUPA Department of Physics, \rUniversity of Strathclyde, Glasgow, UK."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"221","published-online":{"date-parts":[[2023,8,29]]},"reference":[{"issue":"1","key":"e_1_3_3_2_2","doi-asserted-by":"crossref","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions","volume":"8","author":"Alzubaidi L","year":"2021","unstructured":"AlzubaidiL, ZhangJ, HumaidiAJ, Al-DujailiA, DuanY, Al-ShammaO, SantamariaJ, FadhelMA, Al-AmidieM, FarhanL. Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. J Big Data. 2021;8(1): Article 53.","journal-title":"J Big Data"},{"issue":"13","key":"e_1_3_3_3_2","doi-asserted-by":"crossref","DOI":"10.3390\/nano12132171","article-title":"Optical computing: Status and perspectives","volume":"12","author":"Kazanskiy NL","year":"2022","unstructured":"KazanskiyNL, ButtMA, KhoninaSN. Optical computing: Status and perspectives. Nanomaterials (Basel). 2022;12(13): Article 2171.","journal-title":"Nanomaterials (Basel)"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/MM.2018.112130359"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2019.2903009"},{"key":"e_1_3_3_6_2","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2022.795876","article-title":"The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity","volume":"16","author":"Pehle C","year":"2022","unstructured":"PehleC, BillaudelleS, CramerB, KaiserJ, SchreiberK, StradmannY, WeisJ, LeibfriedA, M\u00fcllerE, SchemmelJ. The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity. Front Neurosci. 2022;16: Article 795876.","journal-title":"Front Neurosci"},{"issue":"6","key":"e_1_3_3_7_2","doi-asserted-by":"crossref","first-page":"948","DOI":"10.1109\/49.57798","article-title":"Dense wavelength division multiplexing networks: Principles and applications","volume":"8","author":"Brackett CA","year":"1990","unstructured":"BrackettCA. Dense wavelength division multiplexing networks: Principles and applications. IEEE J Selec Areas Commun. 1990;8(6):948\u2013964.","journal-title":"IEEE J Selec Areas Commun"},{"key":"e_1_3_3_8_2","doi-asserted-by":"crossref","unstructured":"Prucnal PR Shastri BJ Teich MC. In: Prucnal R Shastri BJ editors. Neuromorphic photonics. Boca Raton (FL): CRC Press; 2017.","DOI":"10.1201\/9781315370590"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/JLT.2017.2647779"},{"issue":"7755","key":"e_1_3_3_10_2","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1038\/s41586-019-1157-8","article-title":"All-optical spiking neurosynaptic networks with self-learning capabilities","volume":"569","author":"Feldmann J","year":"2019","unstructured":"FeldmannJ, YoungbloodN, WrightCD, BhaskaranH, PerniceWHP. All-optical spiking neurosynaptic networks with self-learning capabilities. Nature. 2019;569(7755):208\u2013214.","journal-title":"Nature"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-020-03070-1"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1364\/OE.27.019778"},{"issue":"1","key":"e_1_3_3_13_2","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-017-07754-z","article-title":"Neuromorphic photonic networks using silicon photonic weight banks","volume":"7","author":"Tait AN","year":"2017","unstructured":"TaitAN, de LimaTF, ZhouE, WuAX, NahmiasMA, ShastriBJ, PrucnalPR. Neuromorphic photonic networks using silicon photonic weight banks. Sci Rep. 2017;7(1): Article 7430.","journal-title":"Sci Rep"},{"issue":"5","key":"e_1_3_3_14_2","doi-asserted-by":"crossref","first-page":"1515","DOI":"10.1109\/JLT.2019.2903474","article-title":"Machine learning with neuromorphic photonics","volume":"37","author":"de TF","year":"2019","unstructured":"deLimaTF, PengHT, TaitAN, NahmiasMA, MillerHB, ShastriBJ, PrucnalPR. Machine learning with neuromorphic photonics. J Lightwave Technol. 2019;37(5):1515\u20131534.","journal-title":"J Lightwave Technol"},{"key":"e_1_3_3_15_2","doi-asserted-by":"crossref","unstructured":"Mehrabian A Al-Kabani Y Sorger VJ El-Ghazawi T. PCNNA: A photonic convolutional neural network accelerator. Paper presented at: 31st IEEE International System-on-Chip Conference; 2018 Sep 4\u20137; Arlington USA.","DOI":"10.1109\/SOCC.2018.8618542"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-022-04714-0"},{"issue":"1","key":"e_1_3_3_17_2","doi-asserted-by":"crossref","DOI":"10.1038\/s41467-020-20719-7","article-title":"An optical neural chip for implementing complex-valued neural network","volume":"12","author":"Zhang H","year":"2021","unstructured":"ZhangH, GuM, JiangXD, ThompsonJ, CaiH, PaesaniS, SantagatiR, LaingA, ZhangY, YungMH, et al. An optical neural chip for implementing complex-valued neural network. Nat Commun. 2021;12(1): Article 457.","journal-title":"Nat Commun"},{"key":"e_1_3_3_18_2","doi-asserted-by":"crossref","unstructured":"Chen Z Sludds A Davis R Christen I Bernstein L Heuser T Heermerier N Lott JA Reitzenstein S Hamerly R et\u00a0al. Deep learning with coherent VCSEL neural networks. ArXiv. 2022. https:\/\/doi.org\/10.48550\/arXiv.2207.05329","DOI":"10.1117\/12.2648628"},{"issue":"2","key":"e_1_3_3_19_2","doi-asserted-by":"crossref","DOI":"10.1364\/AOP.8.000228","article-title":"Recent progress in semiconductor excitable lasers for photonic spike processing","volume":"8","author":"Prucnal PR","year":"2016","unstructured":"PrucnalPR, ShastriBJ, Ferreira de LimaT, NahmiasMA, TaitAN. Recent progress in semiconductor excitable lasers for photonic spike processing. Adv Opt Photon. 2016;8(2): Article 228.","journal-title":"Adv Opt Photon"},{"issue":"24","key":"e_1_3_3_20_2","doi-asserted-by":"crossref","DOI":"10.1063\/1.4937730","article-title":"Controllable spiking patterns in long-wavelength vertical cavity surface emitting lasers for neuromorphic photonics systems","volume":"107","author":"Hurtado A","year":"2015","unstructured":"HurtadoA, JavaloyesJ. Controllable spiking patterns in long-wavelength vertical cavity surface emitting lasers for neuromorphic photonics systems. Appl Phys Lett. 2015;107(24): Article 241103.","journal-title":"Appl Phys Lett"},{"issue":"6","key":"e_1_3_3_21_2","doi-asserted-by":"crossref","DOI":"10.1109\/JSTQE.2019.2899040","article-title":"Electrically controlled neuron-like spiking regimes in vertical-cavity surface-emitting lasers at ultrafast rates","volume":"25","author":"Robertson J","year":"2019","unstructured":"RobertsonJ, WadeE, HurtadoA. Electrically controlled neuron-like spiking regimes in vertical-cavity surface-emitting lasers at ultrafast rates. IEEE J Sel Top Quantum Electron. 2019;25(6): Article 5100307.","journal-title":"IEEE J Sel Top Quantum Electron"},{"issue":"1","key":"e_1_3_3_22_2","article-title":"Towards neuromorphic photonic networks of ultrafast spiking laser neurons","volume":"26","author":"Robertson J","year":"2019","unstructured":"RobertsonJ, WadeE, KoppY, BuenoJ, HurtadoA. Towards neuromorphic photonic networks of ultrafast spiking laser neurons. IEEE J Sel Top Quantum Electron. 2019;26(1): Article 7700715.","journal-title":"IEEE J Sel Top Quantum Electron"},{"issue":"1","key":"e_1_3_3_23_2","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-020-62945-5","article-title":"Ultrafast optical integration and pattern classification for neuromorphic photonics based on spiking VCSEL neurons","volume":"10","author":"Robertson J","year":"2020","unstructured":"RobertsonJ, HejdaM, BuenoJ, HurtadoA. Ultrafast optical integration and pattern classification for neuromorphic photonics based on spiking VCSEL neurons. Sci Rep. 2020;10(1): Article 6098.","journal-title":"Sci Rep"},{"issue":"4","key":"e_1_3_3_24_2","doi-asserted-by":"crossref","DOI":"10.1088\/2515-7647\/aba670","article-title":"Spike-based information encoding in vertical cavity surface emitting lasers for neuromorphic photonic systems","volume":"2","author":"Hejda M","year":"2020","unstructured":"HejdaM, RobertsonJ, BuenoJ, HurtadoA. Spike-based information encoding in vertical cavity surface emitting lasers for neuromorphic photonic systems. J Phys Photonics. 2020;2(4): Article 44001.","journal-title":"J Phys Photonics"},{"issue":"25","key":"e_1_3_3_25_2","doi-asserted-by":"crossref","first-page":"37526","DOI":"10.1364\/OE.408747","article-title":"Image edge detection with a photonic spiking VCSEL-neuron","volume":"28","author":"Robertson J","year":"2020","unstructured":"RobertsonJ, ZhangY, HejdaM, BuenoJ, XiangS, HurtadoA. Image edge detection with a photonic spiking VCSEL-neuron. Opt Express. 2020;28(25):37526\u201337537.","journal-title":"Opt Express"},{"issue":"5","key":"e_1_3_3_26_2","doi-asserted-by":"crossref","first-page":"B201","DOI":"10.1364\/PRJ.412141","article-title":"All-optical neuromorphic binary convolution with a spiking VCSEL neuron for image gradient magnitudes","volume":"9","author":"Zhang Y","year":"2021","unstructured":"ZhangY, RobertsonJ, XiangS, HejdaM, BuenoJ, HurtadoA. All-optical neuromorphic binary convolution with a spiking VCSEL neuron for image gradient magnitudes. Photon Res. 2021;9(5):B201\u2013B209.","journal-title":"Photon Res"},{"issue":"6","key":"e_1_3_3_27_2","doi-asserted-by":"crossref","DOI":"10.1364\/PRJ.422628","article-title":"Experimental demonstration of pyramidal neuron-like dynamics dominated by dendritic action potentials based on a VCSEL for all-optical XOR classification task","volume":"9","author":"Zhang Y","year":"2021","unstructured":"ZhangY, XiangS, CaoX, ZhaoS, GuoX, WenA, HaoY. Experimental demonstration of pyramidal neuron-like dynamics dominated by dendritic action potentials based on a VCSEL for all-optical XOR classification task. Photonics Res. 2021;9(6): Article 1055.","journal-title":"Photonics Res"},{"issue":"2","key":"e_1_3_3_28_2","doi-asserted-by":"crossref","DOI":"10.1088\/1674-4926\/42\/2\/023105","article-title":"A review: Photonics devices, architectures, and algorithms for optical neural computing","volume":"42","author":"Xiang S","year":"2021","unstructured":"XiangS, HanY, SomngZ, GuoX, ZhangY, RenZ, WangS, MaY, ZouW, MaB. A review: Photonics devices, architectures, and algorithms for optical neural computing. J Semiconduct. 2021;42(2): Article 023105.","journal-title":"J Semiconduct"},{"issue":"1","key":"e_1_3_3_29_2","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-022-08703-1","article-title":"Ultrafast neuromorphic photonic image processing with a VCSEL neuron","volume":"12","author":"Robertson J","year":"2022","unstructured":"RobertsonJ, KirklandP, AlanisJA, HejdaM, BuenoJ, di CaterinaG, HurtadoA. Ultrafast neuromorphic photonic image processing with a VCSEL neuron. Sci Rep. 2022;12(1): Article 4874.","journal-title":"Sci Rep"},{"key":"e_1_3_3_30_2","unstructured":"Jaeger H. The \u201cecho state\u201d approach to analysing and training recurrent neural networks\u2014with an erratum note. Bonn Germany: German National Research Center for Information Technology GMD Technical Report; 2001."},{"issue":"11","key":"e_1_3_3_31_2","doi-asserted-by":"crossref","first-page":"2531","DOI":"10.1162\/089976602760407955","article-title":"Real-time computing without stable states: A new framework for neural computation based on perturbations","volume":"14","author":"Maass W","year":"2002","unstructured":"MaassW, Natschl\u00e4gerT, MarkramH. Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Comput. 2002;14(11):2531\u20132560.","journal-title":"Neural Comput"},{"issue":"3","key":"e_1_3_3_32_2","doi-asserted-by":"crossref","DOI":"10.1364\/OME.451016","article-title":"Role of delay-times in delay-based photonic reservoir computing invited","volume":"12","author":"H\u00fclser T","year":"2022","unstructured":"H\u00fclserT, K\u00f6sterF, JaurigueL, L\u00fcdgeK. Role of delay-times in delay-based photonic reservoir computing invited. Opt Mater Express. 2022;12(3): Article 1214.","journal-title":"Opt Mater Express"},{"issue":"1","key":"e_1_3_3_33_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JSTQE.2019.2927578","article-title":"Reservoir computing using laser networks","volume":"26","author":"Rohm A","year":"2020","unstructured":"RohmA, JaurigueL, LudgeK. Reservoir computing using laser networks. IEEE J Sel Top Quantum Electron. 2020;26(1):1\u20138.","journal-title":"IEEE J Sel Top Quantum Electron"},{"issue":"2","key":"e_1_3_3_34_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JSTQE.2022.3216628","article-title":"Enhanced performance of reservoir computing using multiple self-injection and mutual injection VCSELs","volume":"29","author":"Huang Y","year":"2023","unstructured":"HuangY, ZhouP, YangYG, CaiDY, LiNQ. Enhanced performance of reservoir computing using multiple self-injection and mutual injection VCSELs. IEEE J Sel Top Quantum Electron. 2023;29(2):1\u20139.","journal-title":"IEEE J Sel Top Quantum Electron"},{"issue":"1","key":"e_1_3_3_35_2","doi-asserted-by":"crossref","DOI":"10.1038\/ncomms4541","article-title":"Experimental demonstration of reservoir computing on a silicon photonics chip","volume":"5","author":"Vandoorne K","year":"2014","unstructured":"VandoorneK, MechetP, van VaerenberghT, FiersM, MorthierG, VerstraetenD, SchrauwenB, DambreJ, BienstmanP. Experimental demonstration of reservoir computing on a silicon photonics chip. Nat Commun. 2014;5(1): Article 3541.","journal-title":"Nat Commun"},{"issue":"1","key":"e_1_3_3_36_2","doi-asserted-by":"crossref","DOI":"10.1038\/ncomms2368","article-title":"Parallel photonic information processing at gigabyte per second data rates using transient states","volume":"4","author":"Brunner D","year":"2013","unstructured":"BrunnerD, SorianoMC, MirassoCR, FischerI. Parallel photonic information processing at gigabyte per second data rates using transient states. Nat Commun. 2013;4(1): Article 1364.","journal-title":"Nat Commun"},{"issue":"5","key":"e_1_3_3_37_2","doi-asserted-by":"crossref","DOI":"10.1364\/OPTICA.2.000438","article-title":"High-performance photonic reservoir computer based on a coherently driven passive cavity","volume":"2","author":"Vinckier Q","year":"2015","unstructured":"VinckierQ, DuportF, SmerieriA, VandoorneK, BienstmanP, HaeltermanM, MassarS. High-performance photonic reservoir computer based on a coherently driven passive cavity. Optica. 2015;2(5): Article 438.","journal-title":"Optica"},{"issue":"3","key":"e_1_3_3_38_2","doi-asserted-by":"crossref","DOI":"10.1364\/OE.25.002401","article-title":"Conditions for reservoir computing performance using semiconductor lasers with delayed optical feedback","volume":"25","author":"Bueno J","year":"2017","unstructured":"BuenoJ, BrunnerD, SorianoMC, FischerI. Conditions for reservoir computing performance using semiconductor lasers with delayed optical feedback. Opt Express. 2017;25(3): Article 2401.","journal-title":"Opt Express"},{"key":"e_1_3_3_39_2","doi-asserted-by":"crossref","first-page":"37017","DOI":"10.1109\/ACCESS.2019.2905422","article-title":"PAM-4 transmission at 1550 nm using photonic reservoir computing post-processing","volume":"7","author":"Argyris A","year":"2019","unstructured":"ArgyrisA, BuenoJ, FischerI. PAM-4 transmission at 1550 nm using photonic reservoir computing post-processing. IEEE Access. 2019;7:37017\u201337025.","journal-title":"IEEE Access"},{"issue":"18","key":"e_1_3_3_40_2","doi-asserted-by":"crossref","DOI":"10.1364\/OL.43.004497","article-title":"Enhanced performance of a reservoir computer using polarization dynamics in VCSELs","volume":"43","author":"Vatin J","year":"2018","unstructured":"VatinJ, RontaniD, SciamannaM. Enhanced performance of a reservoir computer using polarization dynamics in VCSELs. Opt Lett. 2018;43(18): Article 4497.","journal-title":"Opt Lett"},{"issue":"13","key":"e_1_3_3_41_2","doi-asserted-by":"crossref","DOI":"10.1364\/OE.27.018579","article-title":"Experimental reservoir computing using VCSEL polarization dynamics","volume":"27","author":"Vatin J","year":"2019","unstructured":"VatinJ, RontaniD, SciamannaM. Experimental reservoir computing using VCSEL polarization dynamics. Optics Express. 2019;27(13): Article 18579.","journal-title":"Optics Express"},{"issue":"8","key":"e_1_3_3_42_2","doi-asserted-by":"crossref","DOI":"10.1063\/5.0017574","article-title":"Experimental realization of dual task processing with a photonic reservoir computer","volume":"5","author":"Vatin J","year":"2020","unstructured":"VatinJ, Rontani, SciamannaM. Experimental realization of dual task processing with a photonic reservoir computer. APL Photonics. 2020;5(8): Article 086105.","journal-title":"APL Photonics"},{"issue":"16","key":"e_1_3_3_43_2","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1109\/LPT.2021.3075095","article-title":"Comprehensive performance analysis of a VCSEL-based photonic reservoir computer","volume":"33","author":"Bueno J","year":"2021","unstructured":"BuenoJ, RobertsonJ, HejdaM, HurtadoA. Comprehensive performance analysis of a VCSEL-based photonic reservoir computer. IEEE Photon Technol Lett. 2021;33(16):920\u2013923.","journal-title":"IEEE Photon Technol Lett"},{"issue":"2","key":"e_1_3_3_44_2","doi-asserted-by":"crossref","DOI":"10.1088\/2515-7647\/abf6bd","article-title":"A complete, parallel and autonomous photonic neural network in a semiconductor multimode laser","volume":"3","author":"Porte X","year":"2021","unstructured":"PorteX, SkalliA, HaghighiN, ReitzensteinS, LottJA, BrunnerD. A complete, parallel and autonomous photonic neural network in a semiconductor multimode laser. J Phys Photonics. 2021;3(2): Article 024017.","journal-title":"J Phys Photonics"},{"key":"e_1_3_3_45_2","doi-asserted-by":"crossref","unstructured":"Skalli A Porte X Haghighi N Reitzenstein S Lott JA Brinner D. Computational metrics and parameters of an injection-locked large area semiconductor laser for neural network computing. ArXiv. 2021. https:\/\/doi.org\/10.48550\/arXiv.2112.08947","DOI":"10.1117\/12.2633381"},{"issue":"6","key":"e_1_3_3_46_2","doi-asserted-by":"crossref","DOI":"10.1364\/OME.450926","article-title":"Photonic neuromorphic computing using vertical cavity semiconductor lasers","volume":"12","author":"Skalli A","year":"2022","unstructured":"SkalliA, RobertsonJ, Owen-NewnsD, HejdaM, PorteX, ReitzensteinS, HurtadoA, BrunnerD. Photonic neuromorphic computing using vertical cavity semiconductor lasers. Opt Mater Express. 2022;12(6): Article 2395.","journal-title":"Opt Mater Express"},{"issue":"2","key":"e_1_3_3_47_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JSTQE.2022.3205716","article-title":"GHz rate neuromorphic photonic spiking neural network with a single vertical-cavity surface-emitting laser (VCSEL)","volume":"29","author":"Owen-Newns D","year":"2023","unstructured":"Owen-NewnsD, RobertsonJ, HejdaM, HurtadoA. GHz rate neuromorphic photonic spiking neural network with a single vertical-cavity surface-emitting laser (VCSEL). IEEE J Sel Top Quantum Electron. 2023;29(2):1\u201310.","journal-title":"IEEE J Sel Top Quantum Electron"},{"issue":"2","key":"e_1_3_3_48_2","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1111\/j.1469-1809.1936.tb02137.x","article-title":"The use of multiple measurements in taxonomic problems","volume":"7","author":"Fisher RA","year":"1936","unstructured":"FisherRA. The use of multiple measurements in taxonomic problems. Ann Eugenics. 1936;7(2):179\u2013188.","journal-title":"Ann Eugenics"},{"issue":"12","key":"e_1_3_3_49_2","doi-asserted-by":"crossref","first-page":"1438","DOI":"10.1016\/j.patrec.2007.02.014","article-title":"Competitive baseline methods set new standards for the nips 2003 feature selection benchmark","volume":"28","author":"Guyon I","year":"2007","unstructured":"GuyonI, LiJ, MaderT, PletscherPA, SchneiderG, UhrM. Competitive baseline methods set new standards for the nips 2003 feature selection benchmark. Pattern Recogn Lett. 2007;28(12):1438\u20131444.","journal-title":"Pattern Recogn Lett"},{"key":"e_1_3_3_50_2","doi-asserted-by":"crossref","unstructured":"Bueno J Robertson J Hejda M Hurtado A. Experimental implementation of a photonic neural network with a 1550nm-VCSEL subject to optical injection and delayed optical feedback. Paper presented at: 2020 IEEE Photonics Conference IPC 2020\u2014Proceedings; Sep 2020; online.","DOI":"10.1109\/IPC47351.2020.9252399"}],"container-title":["Intelligent Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/spj.science.org\/doi\/pdf\/10.34133\/icomputing.0031","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T14:15:50Z","timestamp":1704809750000},"score":1,"resource":{"primary":{"URL":"https:\/\/spj.science.org\/doi\/10.34133\/icomputing.0031"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1]]},"references-count":49,"alternative-id":["10.34133\/icomputing.0031"],"URL":"https:\/\/doi.org\/10.34133\/icomputing.0031","relation":{},"ISSN":["2771-5892"],"issn-type":[{"value":"2771-5892","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1]]},"assertion":[{"value":"2022-11-11","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-05-09","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-08-29","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"0031"}}