{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T06:39:53Z","timestamp":1785393593910,"version":"3.55.0"},"reference-count":18,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2024,6,24]],"date-time":"2024-06-24T00:00:00Z","timestamp":1719187200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,24]],"date-time":"2024-06-24T00:00:00Z","timestamp":1719187200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci. China Inf. Sci."],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1007\/s11432-023-4000-x","type":"journal-article","created":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T12:01:37Z","timestamp":1719403297000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Spiking neural networks in intelligent control systems: a perspective"],"prefix":"10.1007","volume":"67","author":[{"given":"Anguo","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongduan","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,24]]},"reference":[{"key":"4000_CR1","doi-asserted-by":"publisher","first-page":"4890","DOI":"10.1109\/TNNLS.2020.3026078","volume":"32","author":"K Zhao","year":"2021","unstructured":"Zhao K, Song Y D, Meng W C, et al. Low-cost approximation-based adaptive tracking control of output-constrained nonlinear systems. IEEE Trans Neural Netw Learn Syst, 2021, 32: 4890\u20134900","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"4000_CR2","first-page":"119","volume":"2","author":"A Merabet","year":"2023","unstructured":"Merabet A, Kanukollu S, Al-Durra A, et al. Adaptive recurrent neural network for uncertainties estimation in feedback control system. J Autom Intell, 2023, 2: 119\u2013129","journal-title":"J Autom Intell"},{"key":"4000_CR3","doi-asserted-by":"publisher","first-page":"052201","DOI":"10.1007\/s11432-016-0439-4","volume":"60","author":"Y Zeng","year":"2017","unstructured":"Zeng Y, Zhang T L, Xu B. Improving multi-layer spiking neural networks by incorporating brain-inspired rules. Sci China Inf Sci, 2017, 60: 052201","journal-title":"Sci China Inf Sci"},{"key":"4000_CR4","doi-asserted-by":"publisher","first-page":"5841","DOI":"10.1109\/TNNLS.2021.3131356","volume":"34","author":"J R Shen","year":"2021","unstructured":"Shen J R, Zhao Y, Liu J K, et al. HybridSNN: combining bio-machine strengths by boosting adaptive spiking neural networks. IEEE Trans Neural Netw Learn Syst, 2021, 34: 5841\u20135855","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"4000_CR5","doi-asserted-by":"publisher","first-page":"eabk3268","DOI":"10.1126\/scirobotics.abk3268","volume":"6","author":"T DeWolf","year":"2021","unstructured":"DeWolf T. Spiking neural networks take control. Sci Robot, 2021, 6: eabk3268","journal-title":"Sci Robot"},{"key":"4000_CR6","doi-asserted-by":"publisher","first-page":"1659","DOI":"10.1016\/S0893-6080(97)00011-7","volume":"10","author":"W Maass","year":"1997","unstructured":"Maass W. Networks of spiking neurons: the third generation of neural network models. Neural Networks, 1997, 10: 1659\u20131671","journal-title":"Neural Networks"},{"key":"4000_CR7","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/MM.2018.112130359","volume":"38","author":"M Davies","year":"2018","unstructured":"Davies M, Srinivasa N, Lin T H, et al. Loihi: a neuromorphic manycore processor with on-chip learning. IEEE Micro, 2018, 38: 82\u201399","journal-title":"IEEE Micro"},{"key":"4000_CR8","doi-asserted-by":"publisher","first-page":"141401","DOI":"10.1007\/s11432-021-3336-8","volume":"65","author":"T Q Wan","year":"2022","unstructured":"Wan T Q, Ma S J, Liao F Y, et al. Neuromorphic sensory computing. Sci China Inf Sci, 2022, 65: 141401","journal-title":"Sci China Inf Sci"},{"key":"4000_CR9","doi-asserted-by":"publisher","unstructured":"Yu W, Yang N, Wang Z J, et al. Fault-tolerant attitude tracking control driven by spiking NNs for unmanned aerial vehicles. IEEE Trans Neural Netw Learn Syst, 2023. doi: https:\/\/doi.org\/10.1109\/TNNLS.2023.3342078","DOI":"10.1109\/TNNLS.2023.3342078"},{"key":"4000_CR10","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1109\/MSP.2019.2931595","volume":"36","author":"E O Neftci","year":"2019","unstructured":"Neftci E O, Mostafa H, Zenke F. Surrogate gradient learning in spiking neural networks: bringing the power of gradient-based optimization to spiking neural networks. IEEE Signal Process Mag, 2019, 36: 51\u201363","journal-title":"IEEE Signal Process Mag"},{"key":"4000_CR11","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1109\/JPROC.2014.2304638","volume":"102","author":"S B Furber","year":"2014","unstructured":"Furber S B, Galluppi F, Temple S, et al. The SpiNNaker project. Proc IEEE, 2014, 102: 652\u2013665","journal-title":"Proc IEEE"},{"key":"4000_CR12","doi-asserted-by":"crossref","unstructured":"Webb A, Davies S, Lester D. Spiking neural PID controllers. In: Proceedings of International Conference on Neural Information Processing, 2011","DOI":"10.1007\/978-3-642-24965-5_28"},{"key":"4000_CR13","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1109\/JSSC.2022.3218573","volume":"58","author":"X Y Zhang","year":"2023","unstructured":"Zhang X Y, Basu A. A 915\u20131220 TOPS\/W, 976\u20131301 GOPS hybrid in-memory computing based always-on image processing for neuromorphic vision sensors. IEEE J Solid-State Circuits, 2023, 58: 589\u2013599","journal-title":"IEEE J Solid-State Circuits"},{"key":"4000_CR14","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1038\/nn.4241","volume":"19","author":"L F Abbott","year":"2016","unstructured":"Abbott L F, DePasquale B, Memmesheimer R M. Building functional networks of spiking model neurons. Nat Neurosci, 2016, 19: 350\u2013355","journal-title":"Nat Neurosci"},{"key":"4000_CR15","doi-asserted-by":"publisher","first-page":"5215","DOI":"10.1109\/TNNLS.2021.3069683","volume":"33","author":"M Bialas","year":"2023","unstructured":"Bialas M, Mandziuk J. Spike-timing-dependent plasticity with activation-dependent scaling for receptive fields development. IEEE Trans Neural Netw Learn Syst, 2023, 33: 5215\u20135228","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"4000_CR16","doi-asserted-by":"publisher","first-page":"1986","DOI":"10.1109\/TNNLS.2021.3084955","volume":"33","author":"A G Zhang","year":"2022","unstructured":"Zhang A G, Li X M, Gao Y M, et al. Event-driven intrinsic plasticity for spiking convolutional neural networks. IEEE Trans Neural Netw Learn Syst, 2022, 33: 1986\u20131995","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"4000_CR17","doi-asserted-by":"publisher","unstructured":"Zhang A G, Shi J M, Wu J Y, et al. Low latency and sparse computing spiking neural networks with self-driven adaptive threshold plasticity. IEEE Trans Neural Netw Learn Syst, 2024. doi: https:\/\/doi.org\/10.1109\/TNNLS.2023.3300514","DOI":"10.1109\/TNNLS.2023.3300514"},{"key":"4000_CR18","doi-asserted-by":"publisher","first-page":"1514","DOI":"10.1162\/neco_a_01086","volume":"30","author":"F Zenke","year":"2018","unstructured":"Zenke F, Ganguli S. SuperSpike: supervised learning in multilayer spiking neural networks. Neural Comput, 2018, 30: 1514\u20131541","journal-title":"Neural Comput"}],"container-title":["Science China Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-023-4000-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11432-023-4000-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-023-4000-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T21:16:07Z","timestamp":1757106967000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11432-023-4000-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,24]]},"references-count":18,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["4000"],"URL":"https:\/\/doi.org\/10.1007\/s11432-023-4000-x","relation":{},"ISSN":["1674-733X","1869-1919"],"issn-type":[{"value":"1674-733X","type":"print"},{"value":"1869-1919","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,24]]},"assertion":[{"value":"21 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 February 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 June 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"176201"}}