{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T23:07:40Z","timestamp":1765667260738},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030304867"},{"type":"electronic","value":"9783030304874"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-30487-4_38","type":"book-chapter","created":{"date-parts":[[2019,9,8]],"date-time":"2019-09-08T23:02:47Z","timestamp":1567983767000},"page":"484-496","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Gradient-Based Learning of Compositional Dynamics with Modular RNNs"],"prefix":"10.1007","author":[{"given":"Sebastian","family":"Otte","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patricia","family":"Rubisch","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin V.","family":"Butz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,9,9]]},"reference":[{"key":"38_CR1","doi-asserted-by":"publisher","DOI":"10.1093\/acprof:oso\/9780198739692.001.0001","volume-title":"How the Mind Comes Into Being: Introducing Cognitive Science from a Functional and Computational Perspective","author":"MV Butz","year":"2017","unstructured":"Butz, M.V., Kutter, E.F.: How the Mind Comes Into Being: Introducing Cognitive Science from a Functional and Computational Perspective. Oxford University Press, Oxford (2017)"},{"issue":"7729","key":"38_CR2","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1038\/s41586-018-0633-x","volume":"563","author":"Z Gao","year":"2018","unstructured":"Gao, Z., et al.: A cortico-cerebellar loop for motor planning. Nature 563(7729), 113 (2018)","journal-title":"Nature"},{"key":"38_CR3","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016","DOI":"10.1109\/CVPR.2016.90"},{"issue":"8","key":"38_CR4","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). \n                      https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput."},{"key":"38_CR5","unstructured":"Jaeger, H.: The \u201cecho state\u201d approach to analysing and training recurrent neural networks. Technical report, GMD Report, 148, Fraunhofer Institute for Analysis and Information Systems AIS, Sankt Augustin, Germany (2001)"},{"key":"38_CR6","volume-title":"Principles of Neural Science","author":"ER Kandel","year":"2000","unstructured":"Kandel, E.R., Schwartz, J.H., Jessell, T.M., Jessell, M.B.T., Siegelbaum, S., Hudspeth, A.: Principles of Neural Science. McGraw-hill, New York (2000)"},{"key":"38_CR7","unstructured":"Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization. 3rd International Conference for Learning Representations abs\/1412.6980 (2015)"},{"key":"38_CR8","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.neunet.2012.08.008","volume":"36","author":"D Koryakin","year":"2012","unstructured":"Koryakin, D., Lohmann, J., Butz, M.V.: Balanced echo state networks. Neural Netw. 36, 35\u201345 (2012)","journal-title":"Neural Netw."},{"key":"38_CR9","unstructured":"Otte, S., Krechel, D., Liwicki, M.: JANNLab neural network framework for Java. In: Poster Proceedings of MLDM 2013, pp. 39\u201346. ibai-publishing, New York (2013)"},{"key":"38_CR10","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.neucom.2016.01.088","volume":"192","author":"S Otte","year":"2016","unstructured":"Otte, S., Butz, M.V., Koryakin, D., Becker, F., Liwicki, M., Zell, A.: Optimizing recurrent reservoirs with neuro-evolution. Neurocomputing 192, 128\u2013138 (2016)","journal-title":"Neurocomputing"},{"key":"38_CR11","doi-asserted-by":"publisher","first-page":"024102","DOI":"10.1103\/PhysRevLett.120.024102","volume":"120","author":"J Pathak","year":"2018","unstructured":"Pathak, J., Hunt, B., Girvan, M., Lu, Z., Ott, E.: Model-free prediction of large spatiotemporally chaotic systems from data: a reservoir computing approach. Phys. Rev. Lett. 120, 024102 (2018)","journal-title":"Phys. Rev. Lett."},{"key":"38_CR12","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 neural networks by evolino. Neural Comput. 19, 757\u2013779 (2007)","journal-title":"Neural Comput."},{"key":"38_CR13","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.conb.2017.10.023","volume":"49","author":"K Svoboda","year":"2018","unstructured":"Svoboda, K., Li, N.: Neural mechanisms of movement planning: motor cortex and beyond. Curr. Opin. Neurobiol. 49, 33\u201341 (2018)","journal-title":"Curr. Opin. Neurobiol."},{"issue":"10","key":"38_CR14","doi-asserted-by":"publisher","first-page":"1550","DOI":"10.1109\/5.58337","volume":"78","author":"P Werbos","year":"1990","unstructured":"Werbos, P.: Backpropagation through time: what it does and how to do it. Proc. IEEE 78(10), 1550\u20131560 (1990). \n                      https:\/\/doi.org\/10.1109\/5.58337","journal-title":"Proc. IEEE"},{"key":"38_CR15","unstructured":"Wu, Y., et al.: Google\u2019s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation. ArXiv e-prints \n                      1609.08144\n                      \n                     (2016)"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2019: Theoretical Neural Computation"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-30487-4_38","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,8]],"date-time":"2019-09-08T23:14:36Z","timestamp":1567984476000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-30487-4_38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030304867","9783030304874"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-30487-4_38","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"9 September 2019","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":"Munich","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}