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Constructing efficient learning methods is a significant research field in spiking neural networks. In this paper, we present a supervised learning algorithm for multilayer feedforward spiking neural networks; all neurons can fire multiple spikes in all layers. The feedforward network consists of spiking neurons governed by biologically plausible long\u2010term memory spike response model, in which the effect of earlier spikes on the refractoriness is not neglected to incorporate adaptation effects. The gradient descent method is employed to derive synaptic weight updating rule for learning spike trains. The proposed algorithm is tested and verified on spatiotemporal pattern learning problems, including a set of spike train learning tasks and nonlinear pattern classification problems on four UCI datasets. Simulation results indicate that the proposed algorithm can improve learning accuracy in comparison with other supervised learning algorithms.<\/jats:p>","DOI":"10.1155\/2021\/8592824","type":"journal-article","created":{"date-parts":[[2021,11,24]],"date-time":"2021-11-24T22:20:10Z","timestamp":1637792410000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Supervised Learning Algorithm for Multilayer Spiking Neural Networks with Long\u2010Term Memory Spike Response Model"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7932-0546","authenticated-orcid":false,"given":"Xianghong","family":"Lin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengwei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangwen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,11,24]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-015-9478-6"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.plrev.2004.06.001"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/s0893-6080(97)00011-7"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/tcds.2019.2909355"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1142\/s0129065709002002"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1155\/2012\/946589"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/8361369"},{"key":"e_1_2_10_8_2","first-page":"577","article-title":"Supervised learning algorithms for spiking neural networks: a review","volume":"43","author":"Lin X.","year":"2015","journal-title":"Acta Electronica Sinica"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.1038\/323533a0"},{"key":"e_1_2_10_10_2","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511815706"},{"key":"e_1_2_10_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/s0925-2312(01)00658-0"},{"key":"e_1_2_10_12_2","doi-asserted-by":"crossref","unstructured":"McKennochS. 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