{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T22:26:46Z","timestamp":1749508006233,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030304928"},{"type":"electronic","value":"9783030304935"}],"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\/978-3-030-30493-5_6","type":"book-chapter","created":{"date-parts":[[2019,9,10]],"date-time":"2019-09-10T20:03:41Z","timestamp":1568145821000},"page":"62-75","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Reservoir Topology in Deep Echo State Networks"],"prefix":"10.1007","author":[{"given":"Claudio","family":"Gallicchio","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alessio","family":"Micheli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,9,9]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Bacciu, D., Bongiorno, A.: Concentric ESN: assessing the effect of modularity in cycle reservoirs. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2018)","key":"6_CR1","DOI":"10.1109\/IJCNN.2018.8489462"},{"unstructured":"Boedecker, J., Obst, O., Mayer, N.M., Asada, M.: Studies on reservoir initialization and dynamics shaping in echo state networks. In: Proceedings of the 17th European Symposium on Artificial Neural Networks (ESANN), pp. 227\u2013232. d-side publi. (2009)","key":"6_CR2"},{"key":"6_CR3","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.neunet.2016.07.012","volume":"83","author":"I Farka\u0161","year":"2016","unstructured":"Farka\u0161, I., Bos\u00e1k, R., Gergel\u2019, P.: Computational analysis of memory capacity in echo state networks. Neural Netw. 83, 109\u2013120 (2016). https:\/\/doi.org\/10.1016\/j.neunet.2016.07.012","journal-title":"Neural Netw."},{"issue":"3","key":"6_CR4","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1016\/0167-2789(82)90042-2","volume":"4","author":"JD Farmer","year":"1982","unstructured":"Farmer, J.D.: Chaotic attractors of an infinite-dimensional dynamical system. Physica D 4(3), 366\u2013393 (1982). https:\/\/doi.org\/10.1016\/0167-2789(82)90042-2","journal-title":"Physica D"},{"unstructured":"Gallicchio, C.: Short-term memory of deep RNN. In: Proceedings of the 26th European Symposium on Artificial Neural Networks (ESANN), pp. 633\u2013638 (2018)","key":"6_CR5"},{"issue":"3","key":"6_CR6","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1007\/s12559-017-9461-9","volume":"9","author":"C Gallicchio","year":"2017","unstructured":"Gallicchio, C., Micheli, A.: Echo state property of deep reservoir computing networks. Cogn. Comput. 9(3), 337\u2013350 (2017). https:\/\/doi.org\/10.1007\/s12559-017-9461-9","journal-title":"Cogn. Comput."},{"unstructured":"Gallicchio, C., Micheli, A.: Why layering in RNN? A DeepESN survey. In: Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2018)","key":"6_CR7"},{"key":"6_CR8","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). https:\/\/doi.org\/10.1016\/j.neucom.2016.12.089","journal-title":"Neurocomputing"},{"unstructured":"Gallicchio, C., Micheli, A., Ti\u0148o, P.: Randomized recurrent neural networks. In: 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018), pp. 415\u2013424. i6doc.com publication (2018)","key":"6_CR9"},{"doi-asserted-by":"crossref","unstructured":"Gallicchio, C., Micheli, A.: Deep echo state network (DeepESN): a brief survey. arXiv preprint arXiv:1712.04323 (2017)","key":"6_CR10","DOI":"10.1109\/IJCNN.2018.8489464"},{"key":"6_CR11","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.ins.2018.12.052","volume":"480","author":"C Gallicchio","year":"2019","unstructured":"Gallicchio, C., Micheli, A.: Deep reservoir neural networks for trees. Inf. Sci. 480, 174\u2013193 (2019). https:\/\/doi.org\/10.1016\/j.ins.2018.12.052","journal-title":"Inf. Sci."},{"key":"6_CR12","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.neunet.2018.08.002","volume":"108","author":"C Gallicchio","year":"2018","unstructured":"Gallicchio, C., Micheli, A., Pedrelli, L.: Design of deep echo state networks. Neural Netw. 108, 33\u201347 (2018). https:\/\/doi.org\/10.1016\/j.neunet.2018.08.002","journal-title":"Neural Netw."},{"key":"6_CR13","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.neucom.2017.11.073","volume":"298","author":"C Gallicchio","year":"2018","unstructured":"Gallicchio, C., Micheli, A., Silvestri, L.: Local lyapunov exponents of deep echo state networks. Neurocomputing 298, 34\u201345 (2018). https:\/\/doi.org\/10.1016\/j.neucom.2017.11.073","journal-title":"Neurocomputing"},{"key":"6_CR14","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1016\/j.neunet.2018.08.025","volume":"108","author":"L Grigoryeva","year":"2018","unstructured":"Grigoryeva, L., Ortega, J.P.: Echo state networks are universal. Neural Netw. 108, 495\u2013508 (2018). https:\/\/doi.org\/10.1016\/j.neunet.2018.08.025","journal-title":"Neural Netw."},{"key":"6_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"658","DOI":"10.1007\/11840817_69","volume-title":"Artificial Neural Networks \u2013 ICANN 2006","author":"MA Hajnal","year":"2006","unstructured":"Hajnal, M.A., L\u0151rincz, A.: Critical echo state networks. In: Kollias, S.D., Stafylopatis, A., Duch, W., Oja, E. (eds.) ICANN 2006. LNCS, vol. 4131, pp. 658\u2013667. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11840817_69"},{"unstructured":"Jaeger, H.: The \u201cecho state\u201d approach to analysing and training recurrent neural networks - with an erratum note. Technical report, GMD - German National Research Institute for Computer Science (2001)","key":"6_CR16"},{"unstructured":"Jaeger, H.: Short term memory in echo state networks. Technical report, German National Research Center for Information Technology (2001)","key":"6_CR17"},{"issue":"5667","key":"6_CR18","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). https:\/\/doi.org\/10.1126\/science.1091277","journal-title":"Science"},{"unstructured":"Jaeger, H.: Discovering multiscale dynamical features with hierarchical echo state networks. Technical report, Jacobs University Bremen (2007)","key":"6_CR19"},{"key":"6_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.01.002","author":"Y Kawai","year":"2019","unstructured":"Kawai, Y., Park, J., Asada, M.: A small-world topology enhances the echo state property and signal propagation in reservoir computing. Neural Netw. (2019). https:\/\/doi.org\/10.1016\/j.neunet.2019.01.002","journal-title":"Neural Netw."},{"issue":"3","key":"6_CR21","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). https:\/\/doi.org\/10.1016\/j.cosrev.2009.03.005","journal-title":"Comput. Sci. Rev."},{"issue":"4300","key":"6_CR22","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1126\/science.267326","volume":"197","author":"MC Mackey","year":"1977","unstructured":"Mackey, M.C., Glass, L.: Oscillation and chaos in physiological control systems. Science 197(4300), 287\u2013289 (1977). https:\/\/doi.org\/10.1126\/science.267326","journal-title":"Science"},{"unstructured":"Pascanu, R., Gulcehre, C., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. arXiv preprint arXiv:1312.6026v5 (2014)","key":"6_CR23"},{"issue":"1","key":"6_CR24","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 Networks 22(1), 131\u2013144 (2011). https:\/\/doi.org\/10.1109\/TNN.2010.2089641","journal-title":"IEEE Trans. Neural Networks"},{"issue":"7","key":"6_CR25","doi-asserted-by":"publisher","first-page":"1822","DOI":"10.1162\/NECO\\_a_00297","volume":"24","author":"A Rodan","year":"2012","unstructured":"Rodan, A., Ti\u0148o, P.: Simple deterministically constructed cycle reservoirs with regular jumps. Neural Comput. 24(7), 1822\u20131852 (2012). https:\/\/doi.org\/10.1162\/NECO_a_00297","journal-title":"Neural Comput."},{"issue":"12","key":"6_CR26","doi-asserted-by":"publisher","first-page":"3246","DOI":"10.1162\/NECO\\_a\\_00374","volume":"24","author":"T Strauss","year":"2012","unstructured":"Strauss, T., Wustlich, W., Labahn, R.: Design strategies for weight matrices of echo state networks. Neural Comput. 24(12), 3246\u20133276 (2012). https:\/\/doi.org\/10.1162\/NECO_a_00374","journal-title":"Neural Comput."},{"unstructured":"Triefenbach, F., Jalalvand, A., Schrauwen, B., Martens, J.P.: Phoneme recognition with large hierarchical reservoirs. In: Advances in Neural Information Processing Systems, pp. 2307\u20132315 (2010)","key":"6_CR27"},{"issue":"3","key":"6_CR28","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\u2019Haene, M., Stroobandt, D.: An experimental unification of reservoir computing methods. Neural Netw. 20(3), 391\u2013403 (2007). https:\/\/doi.org\/10.1016\/j.neunet.2007.04.003","journal-title":"Neural Netw."},{"key":"6_CR29","doi-asserted-by":"publisher","DOI":"10.4324\/9780429492648","volume-title":"Time Series Prediction: Forecasting the Future and Understanding the Past","author":"AS Weigend","year":"2018","unstructured":"Weigend, A.S.: Time Series Prediction: Forecasting the Future and Understanding the Past. Routledge, Abingdon (2018)"},{"issue":"14","key":"6_CR30","doi-asserted-by":"publisher","first-page":"148102","DOI":"10.1103\/PhysRevLett.92.148102","volume":"92","author":"OL White","year":"2004","unstructured":"White, O.L., Lee, D.D., Sompolinsky, H.: Short-term memory in orthogonal neural networks. Phys. Rev. Lett. 92(14), 148102 (2004). https:\/\/doi.org\/10.1103\/PhysRevLett.92.148102","journal-title":"Phys. Rev. Lett."}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2019: Workshop and Special Sessions"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-30493-5_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T16:48:35Z","timestamp":1710348515000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-30493-5_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030304928","9783030304935"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-30493-5_6","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"}}]}}