{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T18:29:06Z","timestamp":1725992946728},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030014179"},{"type":"electronic","value":"9783030014186"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","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\/978-3-030-01418-6_22","type":"book-chapter","created":{"date-parts":[[2018,9,26]],"date-time":"2018-09-26T10:57:36Z","timestamp":1537959456000},"page":"222-234","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Supervised Multi-spike Learning Algorithm for Recurrent Spiking Neural Networks"],"prefix":"10.1007","author":[{"given":"Xianghong","family":"Lin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoyong","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,9,27]]},"reference":[{"issue":"4","key":"22_CR1","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":"1","key":"22_CR2","first-page":"40","volume":"88","author":"N Adnanshiltagh","year":"2014","unstructured":"Adnanshiltagh, N.: Recurrent spiking neural networks the third generation in identification of systems. Int. J. Comput. Appl. 88(1), 40\u201343 (2014)","journal-title":"Int. J. Comput. Appl."},{"issue":"3","key":"22_CR3","first-page":"577","volume":"43","author":"X Lin","year":"2015","unstructured":"Lin, X., Wang, X., Zhang, N., et al.: Supervised learning algorithms for spiking neural networks: a review. Acta Electron. Sin. 43(3), 577\u2013586 (2015)","journal-title":"Acta Electron. Sin."},{"issue":"4","key":"22_CR4","doi-asserted-by":"publisher","first-page":"258","DOI":"10.22452\/mjcs.vol30no4.1","volume":"30","author":"J Woo","year":"2017","unstructured":"Woo, J., Botzheim, J., Kubota, N.: Emotional empathy model for robot partners using recurrent spiking neural network model with Hebbian-LMS learning. Malays. J. Comput. Sci. 30(4), 258\u2013285 (2017)","journal-title":"Malays. J. Comput. Sci."},{"key":"22_CR5","doi-asserted-by":"crossref","unstructured":"Allen, J.N., Abdel-Aty-Zohdy, H.S., Ewing, R.L.: Plasticity recurrent spiking neural networks for olfactory pattern recognition. In: Midwest Symposium on Circuits and Systems, pp. 1741\u20131744. IEEE (2005)","DOI":"10.1109\/MWSCAS.2005.1594457"},{"key":"22_CR6","doi-asserted-by":"crossref","unstructured":"Shen, J., Lin, K., Wang, Y., et al.: Character recognition from trajectory by recurrent spiking neural networks. In: The 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2900\u20132903. IEEE (2017)","DOI":"10.1109\/EMBC.2017.8037463"},{"issue":"2","key":"22_CR7","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1142\/S0129065789000037","volume":"1","author":"AW Smith","year":"2011","unstructured":"Smith, A.W., Zipser, D.: Learning sequential structure with the real-time recurrent learning algorithm. Int. J. Neural Syst. 1(2), 125\u2013131 (2011)","journal-title":"Int. J. Neural Syst."},{"issue":"10","key":"22_CR8","doi-asserted-by":"publisher","first-page":"1550","DOI":"10.1109\/5.58337","volume":"78","author":"PJ Werbos","year":"1990","unstructured":"Werbos, P.J.: Backpropagation through time: what it does and how to do it. Proc. IEEE 78(10), 1550\u20131560 (1990)","journal-title":"Proc. IEEE"},{"issue":"3","key":"22_CR9","first-page":"95","volume":"13","author":"K Selvaratnam","year":"2000","unstructured":"Selvaratnam, K., Kuroe, Y., Mori, T.: Learning methods of recurrent spiking neural networks-transient and oscillatory spike trains. Trans. Inst. Syst. Control Inf. Eng. 13(3), 95\u2013104 (2000)","journal-title":"Trans. Inst. Syst. Control Inf. Eng."},{"key":"22_CR10","doi-asserted-by":"crossref","unstructured":"Kuroe Y., Ueyama T.: Learning methods of recurrent spiking neural networks based on adjoint equations approach. In: International Joint Conference on Neural Networks, pp. 1\u20138. IEEE (2010)","DOI":"10.1109\/IJCNN.2010.5596914"},{"issue":"3","key":"22_CR11","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1162\/neco.2006.18.3.591","volume":"18","author":"P Ti\u0148o","year":"2006","unstructured":"Ti\u0148o, P., Mills, A.J.S.: Learning beyond finite memory in recurrent networks of spiking neurons. Neural Comput. 18(3), 591\u2013613 (2006)","journal-title":"Neural Comput."},{"key":"22_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1007\/978-3-642-33269-2_69","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2012","author":"S Brodeur","year":"2012","unstructured":"Brodeur, S., Rouat, J.: Regulation toward self-organized criticality in a recurrent spiking neural reservoir. In: Villa, A.E.P., Duch, W., \u00c9rdi, P., Masulli, F., Palm, G. (eds.) ICANN 2012. LNCS, vol. 7552, pp. 547\u2013554. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33269-2_69"},{"key":"22_CR13","unstructured":"Bourdoukan, R., Deneve, S.: Enforcing balance allows local supervised learning in spiking recurrent networks. In: International Conference on Neural Information Processing Systems, pp. 982\u2013990. MIT Press (2015)"},{"key":"22_CR14","doi-asserted-by":"crossref","unstructured":"Diehl, P.U., Zarrella, G., Cassidy, A., et al.: Conversion of artificial recurrent neural networks to spiking neural networks for low-power neuromorphic hardware. In: IEEE International Conference on Rebooting Computing, pp. 1\u20138. IEEE (2016)","DOI":"10.1109\/ICRC.2016.7738691"},{"key":"22_CR15","doi-asserted-by":"publisher","first-page":"e28295","DOI":"10.7554\/eLife.28295","volume":"6","author":"A Gilra","year":"2017","unstructured":"Gilra, A., Gerstner, W.: Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network. Elife 6, e28295 (2017)","journal-title":"Elife"},{"issue":"2","key":"22_CR16","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1162\/neco.2008.09-07-614","volume":"21","author":"AR Paiva","year":"2009","unstructured":"Paiva, A.R., Park, I., Pr\u00edncipe, J.C.: A reproducing kernel Hilbert space framework for spike train signal processing. Neural Comput. 21(2), 424\u2013449 (2009)","journal-title":"Neural Comput."},{"key":"22_CR17","unstructured":"Carnell, A., Richardson, D.: Linear algebra for time series of spikes. In: Proceedings of European Symposium on Artificial Neural Networks, pp. 363\u2013368. DBLP (2005)"},{"issue":"2","key":"22_CR18","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1162\/NECO_a_00396","volume":"25","author":"I Sporea","year":"2013","unstructured":"Sporea, I., Gr\u00fcning, A.: Supervised learning in multilayer spiking neural networks. Neural Comput. 25(2), 473\u2013509 (2013)","journal-title":"Neural Comput."},{"key":"22_CR19","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, Cambridge (2002)"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2018"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-01418-6_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,24]],"date-time":"2019-10-24T18:37:38Z","timestamp":1571942258000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-01418-6_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030014179","9783030014186"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-01418-6_22","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":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rhodes","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","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":"4 October 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2018\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Open","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"easyacademia.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"360","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"139","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"28","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"39% - 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"}},{"value":"2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"In addition there are 41 full poster papers and 11 short poster papers included in the proceedings","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}}]}}