{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T12:07:53Z","timestamp":1773058073151,"version":"3.50.1"},"publisher-location":"Cham","reference-count":26,"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_76","type":"book-chapter","created":{"date-parts":[[2018,10,1]],"date-time":"2018-10-01T17:07:37Z","timestamp":1538413657000},"page":"781-794","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Mass-Spring Damper Array as a Mechanical Medium for Computation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7966-8879","authenticated-orcid":false,"given":"Yuki","family":"Yamanaka","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9025-6015","authenticated-orcid":false,"given":"Takaharu","family":"Yaguchi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5589-4054","authenticated-orcid":false,"given":"Kohei","family":"Nakajima","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5634-7298","authenticated-orcid":false,"given":"Helmut","family":"Hauser","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,9,27]]},"reference":[{"issue":"3","key":"76_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":"7","key":"76_CR2","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1080\/01691864.2017.1402703","volume":"32","author":"M Eder","year":"2018","unstructured":"Eder, M., Hisch, F., Hauser, H.: Morphological computation-based control of a modular, pneumatically driven, soft robotic arm. Adv. Robot. 32(7), 375\u2013385 (2018). https:\/\/doi.org\/10.1080\/01691864.2017.1402703","journal-title":"Adv. Robot."},{"key":"76_CR3","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"588","DOI":"10.1007\/978-3-540-39432-7_63","volume-title":"Advances in Artificial Life","author":"C Fernando","year":"2003","unstructured":"Fernando, C., Sojakka, S.: Pattern recognition in a bucket. In: Banzhaf, W., Ziegler, J., Christaller, T., Dittrich, P., Kim, J.T. (eds.) ECAL 2003. LNCS (LNAI), vol. 2801, pp. 588\u2013597. Springer, Heidelberg (2003). https:\/\/doi.org\/10.1007\/978-3-540-39432-7_63"},{"key":"76_CR4","unstructured":"Hauser, H., F\u00fcchslin, R., Nakajima, K.: Morphological computation\u2014the physical body as a computational resource. In: Hauser, H.; F\u00fcchslin, R.M., Pfeifer, R. (eds.) Opinions and Outlooks on Morphological Computation, Chap. 20, pp. 226\u2013244 (2014). ISBN 978-3-033-04515-6"},{"issue":"5","key":"76_CR5","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1007\/s00422-012-0471-0","volume":"105","author":"H Hauser","year":"2011","unstructured":"Hauser, H., Ijspeert, A.J., F\u00fcchslin, R.M., Pfeifer, R., Maass, W.: Towards a theoretical foundation for morphological computation with compliant bodies. Biol. Cybern. 105(5), 355\u2013370 (2011)","journal-title":"Biol. Cybern."},{"issue":"10","key":"76_CR6","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1007\/s00422-012-0516-4","volume":"106","author":"H Hauser","year":"2012","unstructured":"Hauser, H., Ijspeert, A.J., F\u00fcchslin, R.M., Pfeifer, R., Maass, W.: The role of feedback in morphological computation with compliant bodies. Biol. Cybern. 106(10), 595\u2013613 (2012). https:\/\/doi.org\/10.1007\/s00422-012-0516-4","journal-title":"Biol. Cybern."},{"key":"76_CR7","unstructured":"Jaeger, H.: Adaptive nonlinear system identification with echo state networks. In: Advances in Neural Information Processing Systems, pp. 609\u2013616 (2003)"},{"issue":"5667","key":"76_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":"76_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":"76_CR10","doi-asserted-by":"publisher","unstructured":"Kang, R., et al.: Dynamic model of a hyper-redundant, octopus-like manipulator for underwater applications. In: 2011 IEEE\/RSJ International Conference on Intelligent Robots and Systems, pp. 4054\u20134059 (2011). https:\/\/doi.org\/10.1109\/IROS.2011.6094468","DOI":"10.1109\/IROS.2011.6094468"},{"issue":"1","key":"76_CR11","doi-asserted-by":"publisher","first-page":"eaah3690","DOI":"10.1126\/scirobotics.aah3690","volume":"1","author":"C Laschi","year":"2016","unstructured":"Laschi, C., Mazzolai, B., Cianchetti, M.: Soft robotics: technologies and systems pushing the boundaries of robot abilities. Sci. Robot. 1(1), eaah3690 (2016)","journal-title":"Sci. Robot."},{"issue":"3","key":"76_CR12","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":"11","key":"76_CR13","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."},{"issue":"100","key":"76_CR14","doi-asserted-by":"publisher","first-page":"20140437","DOI":"10.1098\/rsif.2014.0437","volume":"11","author":"K. Nakajima","year":"2014","unstructured":"Nakajima, K., Li, T., Hauser, H., Pfeifer, R.: Exploiting short-term memory in soft body dynamics as a computational resource. J. R. Soc. Interface 11(100) (2014)","journal-title":"Journal of The Royal Society Interface"},{"key":"76_CR15","doi-asserted-by":"publisher","first-page":"91","DOI":"10.3389\/fncom.2013.00091","volume":"7","author":"K Nakajima","year":"2013","unstructured":"Nakajima, K., Hauser, H., Kang, R., Guglielmino, E., Caldwell, D., Pfeifer, R.: A soft body as a reservoir: case studies in a dynamic model of octopus-inspired soft robotic arm. Front. Comput. Neurosci. 7, 91 (2013). https:\/\/doi.org\/10.3389\/fncom.2013.00091","journal-title":"Front. Comput. Neurosci."},{"key":"76_CR16","doi-asserted-by":"crossref","unstructured":"Nakajima, K., Hauser, H., Li, T., Pfeifer, R.: Information processing via physical soft body. Sci. Rep.5 (2015)","DOI":"10.1038\/srep10487"},{"issue":"3","key":"76_CR17","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1089\/soro.2017.0075","volume":"5","author":"K Nakajima","year":"2018","unstructured":"Nakajima, K., Hauser, H., Li, T., Pfeifer, R.: Exploiting the dynamics of soft materials for machine learning. Soft Robot. 5(3), 339\u2013347 (2018)","journal-title":"Soft Robot."},{"key":"76_CR18","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1038\/srep00287","volume":"2","author":"Y Paquot","year":"2012","unstructured":"Paquot, Y., et al.: Optoelectronic reservoir computing. Sci. Rep. 2, 287 (2012)","journal-title":"Sci. Rep."},{"issue":"5","key":"76_CR19","doi-asserted-by":"publisher","first-page":"944","DOI":"10.1109\/TRO.2006.878980","volume":"22","author":"C Paul","year":"2006","unstructured":"Paul, C., Valero-Cuevas, F.J., Lipson, H.: Design and control of tensegrity robots for locomotion. IEEE Trans. Robot. 22(5), 944\u2013957 (2006)","journal-title":"IEEE Trans. Robot."},{"key":"76_CR20","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1007\/978-3-642-00616-6_5","volume-title":"Creating Brain-Like Intelligence","author":"R Pfeifer","year":"2009","unstructured":"Pfeifer, R., G\u00f3mez, G.: Morphological computation \u2013 connecting brain, body, and environment. In: Sendhoff, B., K\u00f6rner, E., Sporns, O., Ritter, H., Doya, K. (eds.) Creating Brain-Like Intelligence. LNCS (LNAI), vol. 5436, pp. 66\u201383. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-00616-6_5"},{"issue":"7553","key":"76_CR21","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1038\/nature14543","volume":"521","author":"D Rus","year":"2015","unstructured":"Rus, D., Tolley, M.T.: Design, fabrication and control of soft robots. Nature 521(7553), 467\u2013475 (2015)","journal-title":"Nature"},{"key":"76_CR22","doi-asserted-by":"publisher","first-page":"16","DOI":"10.3389\/fnbot.2017.00016","volume":"11","author":"G Urbain","year":"2017","unstructured":"Urbain, G., Degrave, J., Carette, B., Dambre, J., Wyffels, F.: Morphological properties of mass-spring networks for optimal locomotion learning. Front. Neurorobotics 11, 16 (2017). https:\/\/doi.org\/10.3389\/fnbot.2017.00016","journal-title":"Front. Neurorobotics"},{"issue":"3","key":"76_CR23","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1016\/j.neunet.2007.04.003","volume":"20","author":"D Verstraeten","year":"2007","unstructured":"Verstraeten, D., Schrauwen, B., d\u2019 Haene, M., Stroobandt, D.: An experimental unification of reservoir computing methods. Neural Netw. 20(3), 391\u2013403 (2007)","journal-title":"Neural Netw."},{"key":"76_CR24","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1152\/jn.00684.2004","volume":"94","author":"Y Yekutieli","year":"2005","unstructured":"Yekutieli, Y., Sagiv-Zohar, R., Aharonov, R., Engel, Y., Hochner, B., Flash, T.: Dynamic model of the octopus arm.I. biomechanics of the octopus reaching movement. J. Neurophysiol. 94, 1443\u20131458 (2005)","journal-title":"J. Neurophysiol."},{"key":"76_CR25","doi-asserted-by":"publisher","first-page":"1459","DOI":"10.1152\/jn.00685.2004","volume":"94","author":"Y Yekutieli","year":"2005","unstructured":"Yekutieli, Y., Sagiv-Zohar, R., Aharonov, R., Engel, Y., Hochner, B., Flash, T.: Dynamic model of the octopus arm.II. control of reaching movements. J. Neurophysiol. 94, 1459\u20131468 (2005)","journal-title":"J. Neurophysiol."},{"key":"76_CR26","doi-asserted-by":"crossref","unstructured":"Zhao, Q., Nakajima, K., Sumioka, H., Hauser, H., Pfeifer, R.: Spine dynamics as a computational resource in spine-driven quadruped locomotion. In: IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS 2013), pp. 1445\u20131451. IEEE (2013)","DOI":"10.1109\/IROS.2013.6696539"}],"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_76","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T09:29:42Z","timestamp":1773048582000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-01424-7_76"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030014230","9783030014247"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-01424-7_76","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)"}}]}}