{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T23:26:28Z","timestamp":1742945188817,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"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_9","type":"book-chapter","created":{"date-parts":[[2019,9,10]],"date-time":"2019-09-10T20:03:41Z","timestamp":1568145821000},"page":"89-93","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hyper-spherical Reservoirs for Echo State Networks"],"prefix":"10.1007","author":[{"given":"Pietro","family":"Verzelli","sequence":"first","affiliation":[]},{"given":"Cesare","family":"Alippi","sequence":"additional","affiliation":[]},{"given":"Lorenzo","family":"Livi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,9,9]]},"reference":[{"issue":"7","key":"9_CR1","doi-asserted-by":"publisher","first-page":"1413","DOI":"10.1162\/089976604323057443","volume":"16","author":"N Bertschinger","year":"2004","unstructured":"Bertschinger, N., Natschl\u00e4ger, T.: Real-time computation at the edge of chaos in recurrent neural networks. Neural Comput. 16(7), 1413\u20131436 (2004). https:\/\/doi.org\/10.1162\/089976604323057443","journal-title":"Neural Comput."},{"key":"9_CR2","unstructured":"Bianchi, F.M., Scardapane, S., L\u00f8kse, S., Jenssen, R.: Reservoir computing approaches for representation and classification of multivariate time series. arXiv preprint arXiv:1803.07870 (2018)"},{"key":"9_CR3","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1016\/j.neunet.2015.08.010","volume":"71","author":"FM Bianchi","year":"2015","unstructured":"Bianchi, F.M., Scardapane, S., Uncini, A., Rizzi, A., Sadeghian, A.: Prediction of telephone calls load using echo state network with exogenous variables. Neural Netw. 71, 204\u2013213 (2015). https:\/\/doi.org\/10.1016\/j.neunet.2015.08.010","journal-title":"Neural Netw."},{"key":"9_CR4","doi-asserted-by":"publisher","unstructured":"Ceni, A., Ashwin, P., Livi, L.: Interpreting recurrent neural networks behaviour via excitable network attractors. Cogn. Comput. (2019). https:\/\/doi.org\/10.1007\/s12559-019-09634-2","DOI":"10.1007\/s12559-019-09634-2"},{"key":"9_CR5","doi-asserted-by":"publisher","unstructured":"Dambre, J., Verstraeten, D., Schrauwen, B., Massar, S.: Information processing capacity of dynamical systems. Sci. Rep. 2 (2012). https:\/\/doi.org\/10.1038\/srep00514","DOI":"10.1038\/srep00514"},{"key":"9_CR6","unstructured":"Gallicchio, C.: Chasing the echo state property. arXiv preprint arXiv:1811.10892 (2018)"},{"key":"9_CR7","unstructured":"Gallicchio, C., Micheli, A., Pedrelli, L.: Comparison between DeepESNs and gated RNNs on multivariate time-series prediction. arXiv preprint arXiv:1812.11527 (2018)"},{"issue":"48","key":"9_CR8","doi-asserted-by":"publisher","first-page":"18970","DOI":"10.1073\/pnas.0804451105","volume":"105","author":"S Ganguli","year":"2008","unstructured":"Ganguli, S., Huh, D., Sompolinsky, H.: Memory traces in dynamical systems. Proc. Nat. Acad. Sci. 105(48), 18970\u201318975 (2008). https:\/\/doi.org\/10.1073\/pnas.0804451105","journal-title":"Proc. Nat. Acad. Sci."},{"key":"9_CR9","unstructured":"Goudarzi, A., Marzen, S., Banda, P., Feldman, G., Teuscher, C., Stefanovic, D.: Memory and information processing in recurrent neural networks. arXiv preprint arXiv:1604.06929 (2016)"},{"key":"9_CR10","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."},{"issue":"1","key":"9_CR11","doi-asserted-by":"publisher","first-page":"10199","DOI":"10.1038\/s41598-017-10257-6","volume":"7","author":"M Inubushi","year":"2017","unstructured":"Inubushi, M., Yoshimura, K.: Reservoir computing beyond memory-nonlinearity trade-off. Sci. Rep. 7(1), 10199 (2017). https:\/\/doi.org\/10.1038\/s41598-017-10257-6","journal-title":"Sci. Rep."},{"key":"9_CR12","unstructured":"Jaeger, H.: Short term memory in echo state networks, vol. 5. GMD-Forschungszentrum Informationstechnik (2002)"},{"issue":"5667","key":"9_CR13","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"},{"issue":"3","key":"9_CR14","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1016\/j.neunet.2007.04.017","volume":"20","author":"R Legenstein","year":"2007","unstructured":"Legenstein, R., Maass, W.: Edge of chaos and prediction of computational performance for neural circuit models. Neural Netw. 20(3), 323\u2013334 (2007). https:\/\/doi.org\/10.1016\/j.neunet.2007.04.017","journal-title":"Neural Netw."},{"issue":"3","key":"9_CR15","doi-asserted-by":"publisher","first-page":"706","DOI":"10.1109\/TNNLS.2016.2644268","volume":"29","author":"L Livi","year":"2018","unstructured":"Livi, L., Bianchi, F.M., Alippi, C.: Determination of the edge of criticality in echo state networks through Fisher information maximization. IEEE Trans. Neural Netw. Learn. Syst. 29(3), 706\u2013717 (2018). https:\/\/doi.org\/10.1109\/TNNLS.2016.2644268","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"11","key":"9_CR16","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). https:\/\/doi.org\/10.1162\/089976602760407955","journal-title":"Neural Comput."},{"issue":"3","key":"9_CR17","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1162\/NECO\\_a_00411","volume":"25","author":"G Manjunath","year":"2013","unstructured":"Manjunath, G., Jaeger, H.: Echo state property linked to an input: exploring a fundamental characteristic of recurrent neural networks. Neural Comput. 25(3), 671\u2013696 (2013). https:\/\/doi.org\/10.1162\/NECO_a_00411","journal-title":"Neural Comput."},{"issue":"3","key":"9_CR18","doi-asserted-by":"publisher","first-page":"032308","DOI":"10.1103\/PhysRevE.96.032308","volume":"96","author":"S Marzen","year":"2017","unstructured":"Marzen, S.: Difference between memory and prediction in linear recurrent networks. Phys. Rev. E 96(3), 032308 (2017). https:\/\/doi.org\/10.1103\/PhysRevE.96.032308","journal-title":"Phys. Rev. E"},{"issue":"2","key":"9_CR19","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":"2","key":"9_CR20","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(2), 024102 (2018)","journal-title":"Phys. Rev. Lett."},{"issue":"12","key":"9_CR21","doi-asserted-by":"publisher","first-page":"121102","DOI":"10.1063\/1.5010300","volume":"27","author":"J Pathak","year":"2017","unstructured":"Pathak, J., Lu, Z., Hunt, B.R., Girvan, M., Ott, E.: Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data. Chaos: Interdisc. J. Nonlinear Sci. 27(12), 121102 (2017). https:\/\/doi.org\/10.1063\/1.5010300","journal-title":"Chaos: Interdisc. J. Nonlinear Sci."},{"issue":"4","key":"9_CR22","doi-asserted-by":"publisher","first-page":"041101","DOI":"10.1063\/1.5028373","volume":"28","author":"J Pathak","year":"2018","unstructured":"Pathak, J., et al.: Hybrid forecasting of chaotic processes: using machine learning in conjunction with a knowledge-based model. Chaos: Interdisc. J. Nonlinear Sci. 28(4), 041101 (2018). https:\/\/doi.org\/10.1063\/1.5028373","journal-title":"Chaos: Interdisc. J. Nonlinear Sci."},{"issue":"1","key":"9_CR23","doi-asserted-by":"publisher","first-page":"011903","DOI":"10.1103\/PhysRevE.82.011903","volume":"82","author":"K Rajan","year":"2010","unstructured":"Rajan, K., Abbott, L.F., Sompolinsky, H.: Stimulus-dependent suppression of chaos in recurrent neural networks. Phys. Rev. E 82(1), 011903 (2010). https:\/\/doi.org\/10.1103\/PhysRevE.82.011903","journal-title":"Phys. Rev. E"},{"key":"9_CR24","doi-asserted-by":"publisher","first-page":"258101","DOI":"10.1103\/PhysRevLett.118.258101","volume":"118","author":"A Rivkind","year":"2017","unstructured":"Rivkind, A., Barak, O.: Local dynamics in trained recurrent neural networks. Phys. Rev. Lett. 118, 258101 (2017). https:\/\/doi.org\/10.1103\/PhysRevLett.118.258101","journal-title":"Phys. Rev. Lett."},{"issue":"3","key":"9_CR25","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1103\/PhysRevLett.61.259","volume":"61","author":"H Sompolinsky","year":"1988","unstructured":"Sompolinsky, H., Crisanti, A., Sommers, H.J.: Chaos in random neural networks. Phys. Rev. Lett. 61(3), 259 (1988). https:\/\/doi.org\/10.1103\/PhysRevLett.61.259","journal-title":"Phys. Rev. Lett."},{"issue":"3","key":"9_CR26","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1162\/NECO\\_a\\_00409","volume":"25","author":"D Sussillo","year":"2013","unstructured":"Sussillo, D., Barak, O.: Opening the black box: low-dimensional dynamics in high-dimensional recurrent neural networks. Neural Comput. 25(3), 626\u2013649 (2013). https:\/\/doi.org\/10.1162\/NECO_a_00409","journal-title":"Neural Comput."},{"key":"9_CR27","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.neucom.2012.12.041","volume":"112","author":"P Ti\u0148o","year":"2013","unstructured":"Ti\u0148o, P., Rodan, A.: Short term memory in input-driven linear dynamical systems. Neurocomputing 112, 58\u201363 (2013). https:\/\/doi.org\/10.1016\/j.neucom.2012.12.041","journal-title":"Neurocomputing"},{"key":"9_CR28","doi-asserted-by":"crossref","unstructured":"Verstraeten, D., Dambre, J., Dutoit, X., Schrauwen, B.: Memory versus non-linearity in reservoirs. In: IEEE International Joint Conference on Neural Networks, pp. 1\u20138. IEEE, Barcelona (2010)","DOI":"10.1109\/IJCNN.2010.5596492"},{"key":"9_CR29","doi-asserted-by":"crossref","unstructured":"Verzelli, P., Alippi, C., Livi, L.: Echo state networks with self-normalizing activations on the hyper-sphere. arXiv preprint arXiv:1903.11691 (2019)","DOI":"10.1038\/s41598-019-50158-4"},{"key":"9_CR30","doi-asserted-by":"publisher","unstructured":"Verzelli, P., Livi, L., Alippi, C.: A characterization of the edge of criticality in binary echo state networks. In: 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1\u20136. IEEE (2018). https:\/\/doi.org\/10.1109\/MLSP.2018.8516959","DOI":"10.1109\/MLSP.2018.8516959"},{"key":"9_CR31","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.neunet.2015.12.013","volume":"76","author":"G Wainrib","year":"2016","unstructured":"Wainrib, G., Galtier, M.N.: A local echo state property through the largest Lyapunov exponent. Neural Netw. 76, 39\u201345 (2016). https:\/\/doi.org\/10.1016\/j.neunet.2015.12.013","journal-title":"Neural Netw."},{"key":"9_CR32","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). https:\/\/doi.org\/10.1016\/j.neunet.2012.07.005","journal-title":"Neural Netw."}],"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_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T16:48:45Z","timestamp":1710348525000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-30493-5_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030304928","9783030304935"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-30493-5_9","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"}}]}}