{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,5]],"date-time":"2025-07-05T16:10:11Z","timestamp":1751731811182,"version":"3.41.0"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319959290"},{"type":"electronic","value":"9783319959306"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-319-95930-6_23","type":"book-chapter","created":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T06:12:40Z","timestamp":1530771160000},"page":"243-253","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Supervised Learning Algorithm for Multi-spike Liquid State Machines"],"prefix":"10.1007","author":[{"given":"Xianghong","family":"Lin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,7,6]]},"reference":[{"issue":"2","key":"23_CR1","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1023\/B:NACO.0000027755.02868.60","volume":"3","author":"SM Bohte","year":"2004","unstructured":"Bohte, S.M.: The evidence for neural information processing with precise spike-times: a survey. Nat. Comput. 3(2), 195\u2013206 (2004)","journal-title":"Nat. Comput."},{"key":"23_CR2","doi-asserted-by":"publisher","DOI":"10.1201\/b14756","volume-title":"Principles of Neural Coding","author":"RQ Quiroga","year":"2013","unstructured":"Quiroga, R.Q., Panzeri, S.: Principles of Neural Coding. CRC Press, Boca Raton (2013)"},{"issue":"5","key":"23_CR3","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1109\/TNN.2004.832719","volume":"15","author":"EM Izhikevich","year":"2004","unstructured":"Izhikevich, E.M.: Which model to use for cortical spiking neurons? IEEE Trans. Neural Netw. 15(5), 1063\u20131070 (2004)","journal-title":"IEEE Trans. Neural Netw."},{"issue":"1","key":"23_CR4","doi-asserted-by":"publisher","first-page":"e1001056","DOI":"10.1371\/journal.pcbi.1001056","volume":"7","author":"S Ostojic","year":"2011","unstructured":"Ostojic, S., Brunel, N.: From spiking neuron models to linear-nonlinear models. PLoS Comput. Biol. 7(1), e1001056 (2011)","journal-title":"PLoS Comput. Biol."},{"issue":"1","key":"23_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1162\/neco.1996.8.1.1","volume":"8","author":"W Maass","year":"2014","unstructured":"Maass, W.: Lower bounds for the computational power of networks of spiking neurons. Neural Comput. 8(1), 1\u201340 (2014)","journal-title":"Neural Comput."},{"issue":"4","key":"23_CR6","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1142\/S0129065709002002","volume":"19","author":"S Ghosh-Dastidar","year":"2009","unstructured":"Ghosh-Dastidar, S., Adeli, H.: Spiking neural networks. Int. J. Neural Syst. 19(4), 295\u2013308 (2009)","journal-title":"Int. J. Neural Syst."},{"issue":"11","key":"23_CR7","doi-asserted-by":"publisher","first-page":"2531","DOI":"10.1162\/089976602760407955","volume":"14","author":"W Maass","year":"2002","unstructured":"Maass, W., Natschl\u00e4ger, T., Markram, H.: Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput. 14(11), 2531\u20132560 (2002)","journal-title":"Neural Comput."},{"key":"23_CR8","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1142\/9781848162778_0008","volume-title":"Computability in Context","author":"Wolfgang Maass","year":"2011","unstructured":"Maass, W.: Liquid state machines: motivation, theory, and applications. In: Computability in Context: Computation and Logic in the Real World, pp. 275\u2013296. Imperial College Press, London (2011)"},{"issue":"5","key":"23_CR9","doi-asserted-by":"publisher","first-page":"1550036","DOI":"10.1142\/S0129065715500367","volume":"26","author":"JL Rossell\u00f3","year":"2016","unstructured":"Rossell\u00f3, J.L., Alomar, M.L., Morro, A., et al.: High-density liquid-state machine circuitry for time-series forecasting. Int. J. Neural Syst. 26(5), 1550036 (2016)","journal-title":"Int. J. Neural Syst."},{"issue":"3","key":"23_CR10","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.cosrev.2009.03.005","volume":"3","author":"M Luko\u0161evi\u010dius","year":"2009","unstructured":"Luko\u0161evi\u010dius, M., Jaeger, H.: Reservoir computing approaches to recurrent neural network training. Comput. Sci. Rev. 3(3), 127\u2013149 (2009)","journal-title":"Comput. Sci. Rev."},{"issue":"2","key":"23_CR11","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1007\/s10489-006-0007-1","volume":"26","author":"H Burgsteiner","year":"2007","unstructured":"Burgsteiner, H., Kr\u00f6ll, M., Leopold, A., et al.: Movement prediction from real-world images using a liquid state machine. Appl. Intell. 26(2), 99\u2013109 (2007)","journal-title":"Appl. Intell."},{"key":"23_CR12","doi-asserted-by":"crossref","unstructured":"Sala, D.A., Brusamarello, V.J., Azambuja, R.D., et al.: Positioning control on a collaborative robot by sensor fusion with liquid state machines. In: 2017 IEEE International Instrumentation and Measurement Technology Conference, pp. 1\u20136. IEEE, Turin, Italy (2017)","DOI":"10.1109\/I2MTC.2017.7969728"},{"issue":"11","key":"23_CR13","doi-asserted-by":"publisher","first-page":"2635","DOI":"10.1109\/TNNLS.2015.2388544","volume":"26","author":"Y Zhang","year":"2015","unstructured":"Zhang, Y., Li, P., Jin, Y., et al.: A digital liquid state machine with biologically inspired learning and its application to speech recognition. IEEE Trans. Neural Netw. Learn. Syst. 26(11), 2635\u20132649 (2015)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"23_CR14","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.neucom.2016.11.045","volume":"226","author":"Y Jin","year":"2017","unstructured":"Jin, Y., Li, P.: Performance and robustness of bio-inspired digital liquid state machines: a case study of speech recognition. Neurocomputing 226, 145\u2013160 (2017)","journal-title":"Neurocomputing"},{"key":"23_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.artmed.2018.01.001","volume":"86","author":"OA Zoubi","year":"2018","unstructured":"Zoubi, O.A., Awad, M., Kasabov, N.K.: Anytime multipurpose emotion recognition from EEG data using a liquid state machine based framework. Artif. Intell. Med. 86, 1\u20138 (2018)","journal-title":"Artif. Intell. Med."},{"key":"23_CR16","doi-asserted-by":"crossref","unstructured":"Xue, F., Guan, H., Li, X.: Improving liquid state machine with hybrid plasticity. In: Advanced Information Management, Communicates, Electronic and Automation Control Conference, pp. 1955\u20131959. IEEE, Xi\u2019an, China (2017)","DOI":"10.1109\/IMCEC.2016.7867559"},{"key":"23_CR17","volume-title":"An Introduction to Neural Networks","author":"B Kroese","year":"1996","unstructured":"Kroese, B., van der Smagt, P.: An Introduction to Neural Networks, 8th edn. The University of Amsterdam, Amsterdam (1996)","edition":"8"},{"issue":"2","key":"23_CR18","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1162\/neco.2009.11-08-901","volume":"22","author":"F Ponulak","year":"2010","unstructured":"Ponulak, F., Kasinski, A.: Supervised learning in spiking neural networks with ReSuMe: sequence learning, classification, and spike shifting. Neural Comput. 22(2), 467\u2013510 (2010)","journal-title":"Neural Comput."},{"issue":"2","key":"23_CR19","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1016\/S0896-6273(03)00847-X","volume":"41","author":"CY Li","year":"2004","unstructured":"Li, C.Y., Lu, J.T., Wu, C.P., et al.: Bidirectional modification of presynaptic neuronal excitability accompanying spike timing-dependent synaptic plasticity. Neuron 41(2), 257\u2013268 (2004)","journal-title":"Neuron"},{"key":"23_CR20","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.neucom.2016.08.087","volume":"237","author":"X Lin","year":"2017","unstructured":"Lin, X., Wang, X., Hao, Z.: Supervised learning in multilayer spiking neural networks with inner products of spike trains. Neurocomputing 237, 59\u201370 (2017)","journal-title":"Neurocomputing"},{"key":"23_CR21","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511815706","volume-title":"Spiking Neuron Models: Single Neurons, Populations, Plasticity","author":"W Gerstner","year":"2002","unstructured":"Gerstner, W., Kistler, W.M.: Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge University Press, New York (2002)"}],"container-title":["Lecture Notes in Computer Science","Intelligent Computing Theories and Application"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-95930-6_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,5]],"date-time":"2025-07-05T15:38:30Z","timestamp":1751729910000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-95930-6_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783319959290","9783319959306"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-95930-6_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"6 July 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Wuhan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 August 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 August 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ic-ic.tongji.edu.cn\/2018\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"LOD","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"632","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"275","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"72","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"44% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.46","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}