{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T12:18:13Z","timestamp":1773058693098,"version":"3.50.1"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030014230","type":"print"},{"value":"9783030014247","type":"electronic"}],"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\/978-3-030-01424-7_12","type":"book-chapter","created":{"date-parts":[[2018,10,1]],"date-time":"2018-10-01T17:07:37Z","timestamp":1538413657000},"page":"116-125","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Practical Fractional-Order Neuron Dynamics for Reservoir Computing"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3760-249X","authenticated-orcid":false,"given":"Taisuke","family":"Kobayashi","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,9,27]]},"reference":[{"issue":"3","key":"12_CR1","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1109\/72.846741","volume":"11","author":"AF Atiya","year":"2000","unstructured":"Atiya, A.F., Parlos, A.G.: New results on recurrent network training: unifying the algorithms and accelerating convergence. IEEE Trans. Neural Netw. 11(3), 697\u2013709 (2000)","journal-title":"IEEE Trans. Neural Netw."},{"issue":"6","key":"12_CR2","doi-asserted-by":"publisher","first-page":"1451","DOI":"10.1007\/s00521-013-1364-4","volume":"24","author":"D Bacciu","year":"2014","unstructured":"Bacciu, D., Barsocchi, P., Chessa, S., Gallicchio, C., Micheli, A.: An experimental characterization of reservoir computing in ambient assisted living applications. Neural Comput. Appl. 24(6), 1451\u20131464 (2014)","journal-title":"Neural Comput. Appl."},{"key":"12_CR3","unstructured":"Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)"},{"issue":"6\u20138","key":"12_CR4","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1016\/j.cma.2004.06.006","volume":"194","author":"K Diethelm","year":"2005","unstructured":"Diethelm, K., Ford, N.J., Freed, A.D., Luchko, Y.: Algorithms for the fractional calculus: a selection of numerical methods. Comput. Methods Appl. Mech. Eng. 194(6\u20138), 743\u2013773 (2005)","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"12_CR5","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.neucom.2016.12.089","volume":"268","author":"C Gallicchio","year":"2017","unstructured":"Gallicchio, C., Micheli, A., Pedrelli, L.: Deep reservoir computing: a critical experimental analysis. Neurocomputing 268, 87\u201399 (2017)","journal-title":"Neurocomputing"},{"key":"12_CR6","unstructured":"Hermans, M., Schrauwen, B.: Training and analysing deep recurrent neural networks. In: Advances in Neural Information Processing Systems, pp. 190\u2013198 (2013)"},{"issue":"8","key":"12_CR7","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"issue":"5667","key":"12_CR8","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1126\/science.1091277","volume":"304","author":"H Jaeger","year":"2004","unstructured":"Jaeger, H., Haas, H.: Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304(5667), 78\u201380 (2004)","journal-title":"Science"},{"issue":"3","key":"12_CR9","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1016\/j.neunet.2007.04.016","volume":"20","author":"H Jaeger","year":"2007","unstructured":"Jaeger, H., Luko\u0161evi\u010dius, M., Popovici, D., Siewert, U.: Optimization and applications of echo state networks with leaky-integrator neurons. Neural Netw. 20(3), 335\u2013352 (2007)","journal-title":"Neural Netw."},{"key":"12_CR10","unstructured":"Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: International Conference for Learning Representations, pp. 1\u201315 (2015)"},{"issue":"3","key":"12_CR11","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."},{"key":"12_CR12","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.ins.2016.07.065","volume":"370-371","author":"Shu-xian Lun","year":"2016","unstructured":"Lun, S.x., Yao, X.s., Hu, H.f.: A new echo state network with variable memory length. Inf. Sci. 370, 103\u2013119 (2016)","journal-title":"Information Sciences"},{"issue":"4","key":"12_CR13","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1016\/j.jcss.2004.04.001","volume":"69","author":"W Maass","year":"2004","unstructured":"Maass, W., Markram, H.: On the computational power of circuits of spiking neurons. J. Comput. Syst. Sci. 69(4), 593\u2013616 (2004)","journal-title":"J. Comput. Syst. Sci."},{"issue":"3","key":"12_CR14","doi-asserted-by":"publisher","first-page":"670","DOI":"10.2478\/s13540-013-0042-7","volume":"16","author":"T Marinov","year":"2013","unstructured":"Marinov, T., Ramirez, N., Santamaria, F.: Fractional integration toolbox. Fract. Calc. Appl. Anal. 16(3), 670\u2013681 (2013)","journal-title":"Fract. Calc. Appl. Anal."},{"key":"12_CR15","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1016\/j.ins.2017.08.046","volume":"418","author":"SMA Pahnehkolaei","year":"2017","unstructured":"Pahnehkolaei, S.M.A., Alfi, A., Machado, J.T.: Uniform stability of fractional order leaky integrator echo state neural network with multiple time delays. Inf. Sci. 418, 703\u2013716 (2017)","journal-title":"Inf. Sci."},{"issue":"2","key":"12_CR16","doi-asserted-by":"publisher","first-page":"87","DOI":"10.3233\/AIS-160372","volume":"8","author":"F Palumbo","year":"2016","unstructured":"Palumbo, F., Gallicchio, C., Pucci, R., Micheli, A.: Human activity recognition using multisensor data fusion based on reservoir computing. J. Ambient. Intell. Smart Environ. 8(2), 87\u2013107 (2016)","journal-title":"J. Ambient. Intell. Smart Environ."},{"issue":"1","key":"12_CR17","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1109\/TNN.2010.2089641","volume":"22","author":"A Rodan","year":"2011","unstructured":"Rodan, A., Tino, P.: Minimum complexity echo state network. IEEE Trans. Neural Netw. 22(1), 131\u2013144 (2011)","journal-title":"IEEE Trans. Neural Netw."},{"issue":"3","key":"12_CR18","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1162\/neco.2007.19.3.757","volume":"19","author":"J Schmidhuber","year":"2007","unstructured":"Schmidhuber, J., Wierstra, D., Gagliolo, M., Gomez, F.: Training recurrent networks by evolino. Neural Comput. 19(3), 757\u2013779 (2007)","journal-title":"Neural Comput."},{"issue":"3","key":"12_CR19","doi-asserted-by":"publisher","first-page":"e1003526","DOI":"10.1371\/journal.pcbi.1003526","volume":"10","author":"W Teka","year":"2014","unstructured":"Teka, W., Marinov, T.M., Santamaria, F.: Neuronal spike timing adaptation described with a fractional leaky integrate-and-fire model. PLoS Comput. Biol. 10(3), e1003526 (2014)","journal-title":"PLoS Comput. Biol."},{"key":"12_CR20","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.neunet.2017.05.007","volume":"93","author":"WW Teka","year":"2017","unstructured":"Teka, W.W., Upadhyay, R.K., Mondal, A.: Fractional-order leaky integrate-and-fire model with long-term memory and power law dynamics. Neural Netw. 93, 110\u2013125 (2017)","journal-title":"Neural Netw."},{"issue":"6","key":"12_CR21","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1016\/j.ipl.2005.05.019","volume":"95","author":"D Verstraeten","year":"2005","unstructured":"Verstraeten, D., Schrauwen, B., Stroobandt, D., Van Campenhout, J.: Isolated word recognition with the liquid state machine: a case study. Inf. Process. Lett. 95(6), 521\u2013528 (2005)","journal-title":"Inf. Process. Lett."},{"issue":"7","key":"12_CR22","doi-asserted-by":"publisher","first-page":"e0181816","DOI":"10.1371\/journal.pone.0181816","volume":"12","author":"F Xue","year":"2017","unstructured":"Xue, F., Li, Q., Li, X.: The combination of circle topology and leaky integrator neurons remarkably improves the performance of echo state network on time series prediction. PloS one 12(7), e0181816 (2017)","journal-title":"PloS one"},{"key":"12_CR23","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.neunet.2013.01.019","volume":"47","author":"T Yamazaki","year":"2013","unstructured":"Yamazaki, T., Igarashi, J.: Realtime cerebellum: a large-scale spiking network model of the cerebellum that runs in realtime using a graphics processing unit. Neural Netw. 47, 103\u2013111 (2013)","journal-title":"Neural Netw."},{"key":"12_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neunet.2012.07.005","volume":"35","author":"IB Yildiz","year":"2012","unstructured":"Yildiz, I.B., Jaeger, H., Kiebel, S.J.: Re-visiting the echo state property. Neural Netw. 35, 1\u20139 (2012)","journal-title":"Neural Netw."}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2018"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-01424-7_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T09:34:12Z","timestamp":1773048852000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-01424-7_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030014230","9783030014247"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-01424-7_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"27 September 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"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 (provided by the conference organizers)"}},{"value":"easyacademia.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"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 (provided by the conference organizers)"}},{"value":"139","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":"28","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":"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 (provided by the conference organizers)"}},{"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 (provided by the conference organizers)"}},{"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 (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"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 (provided by the conference organizers)"}}]}}