{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T19:58:25Z","timestamp":1760731105304},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319950976"},{"type":"electronic","value":"9783319950983"}],"license":[{"start":{"date-parts":[[2018,7,22]],"date-time":"2018-07-22T00:00:00Z","timestamp":1532217600000},"content-version":"unspecified","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\/978-3-319-95098-3_11","type":"book-chapter","created":{"date-parts":[[2018,7,21]],"date-time":"2018-07-21T10:22:26Z","timestamp":1532168546000},"page":"119-129","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Hierarchical Temporal Representation in Linear Reservoir Computing"],"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"}]},{"given":"Luca","family":"Pedrelli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,7,22]]},"reference":[{"key":"11_CR1","unstructured":"Angelov, P., Sperduti, A.: Challenges in deep learning. In: Proceedings of the 24th European Symposium on Artificial Neural Networks (ESANN), pp. 489\u2013495. i6doc.com (2016)"},{"key":"11_CR2","first-page":"778","volume":"2008","author":"M \u010cer\u0148ansk\u1ef3","year":"2008","unstructured":"\u010cer\u0148ansk\u1ef3, M., Ti\u0148o, P.: Predictive modeling with echo state networks. Artif. Neural Netw ICANN 2008, 778\u2013787 (2008)","journal-title":"Artif. Neural Netw ICANN"},{"key":"11_CR3","doi-asserted-by":"crossref","unstructured":"Frigo, M., Johnson, S.G.: FFTW: An adaptive software architecture for the FFT. In: Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 3, pp. 1381\u20131384. IEEE (1998)","DOI":"10.1109\/ICASSP.1998.681704"},{"key":"11_CR4","unstructured":"Gallicchio, C., Martin-Guerrero, J., Micheli, A., Soria-Olivas, E.: Randomized machine learning approaches: Recent developments and challenges. In: Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN), pp. 77\u201386. i6doc.com (2017)"},{"key":"11_CR5","unstructured":"Gallicchio, C., Micheli, A.: Deep reservoir computing: a critical analysis. In: Proceedings of the 24th European Symposium on Artificial Neural Networks (ESANN), pp. 497\u2013502. i6doc.com (2016)"},{"issue":"3","key":"11_CR6","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1007\/s12559-017-9461-9","volume":"9","author":"Claudio Gallicchio","year":"2017","unstructured":"Gallicchio, C., Micheli, A.: Echo state property of deep reservoir computing networks. Cogn. Comput. 337\u2013350 (2017). \nhttps:\/\/doi.org\/10.1007\/s12559-017-9461-9","journal-title":"Cognitive Computation"},{"key":"11_CR7","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.neucom.2016.12.089","volume":"268","author":"Claudio Gallicchio","year":"2017","unstructured":"Gallicchio, C., Micheli, A., Pedrelli, L.: Deep reservoir computing: a critical experimental analysis. Neurocomputing 87\u201399 (2017). \nhttps:\/\/doi.org\/10.1016\/j.neucom.2016.12.089","journal-title":"Neurocomputing"},{"key":"11_CR8","unstructured":"Gallicchio, C., Micheli, A., Silvestri, L.: Local Lyapunov Exponents of Deep RNN. In: Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN), pp. 559\u2013564. i6doc.com (2017)"},{"key":"11_CR9","unstructured":"Hermans, M., Schrauwen, B.: Training and analysing deep recurrent neural networks. In: NIPS, pp. 190\u2013198 (2013)"},{"key":"11_CR10","unstructured":"Hihi, S.E., Bengio, Y.: Hierarchical recurrent neural networks for long-term dependencies. In: NIPS, pp. 493\u2013499 (1995)"},{"issue":"2","key":"11_CR11","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1016\/j.neunet.2009.07.004","volume":"23","author":"G Holzmann","year":"2010","unstructured":"Holzmann, G., Hauser, H.: Echo state networks with filter neurons and a delay & sum readout. Neural Netw. 23(2), 244\u2013256 (2010)","journal-title":"Neural Netw."},{"issue":"5667","key":"11_CR12","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":"11_CR13","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":"11_CR14","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.: Balanced echo state networks. Neural Netw. 36, 35\u201345 (2012)","journal-title":"Neural Netw."},{"issue":"3","key":"11_CR15","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":"11_CR16","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":"11_CR17","unstructured":"Pasa, L., Sperduti, A.: Pre-training of recurrent neural networks via linear autoencoders. In: Advances in Neural Information Processing Systems, pp. 3572\u20133580 (2014)"},{"key":"11_CR18","unstructured":"Pascanu, R., G\u00fcl\u00e7ehre, \u00c7., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks, pp. 1\u201313. arXiv preprint \narXiv:1312.6026v5\n\n (2014)"},{"key":"11_CR19","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85\u2013117 (2015)","journal-title":"Neural Netw."},{"issue":"3","key":"11_CR20","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":"11_CR21","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)","journal-title":"Neural Netw."},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Wierstra, D., Gomez, F.J., Schmidhuber, J.: Modeling systems with internal state using evolino. In: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation, pp. 1795\u20131802. ACM (2005)","DOI":"10.1145\/1068009.1068315"},{"issue":"3","key":"11_CR23","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1016\/j.neunet.2007.04.014","volume":"20","author":"Y Xue","year":"2007","unstructured":"Xue, Y., Yang, L., Haykin, S.: Decoupled echo state networks with lateral inhibition. Neural Netw. 20(3), 365\u2013376 (2007)","journal-title":"Neural Netw."}],"container-title":["Smart Innovation, Systems and Technologies","Neural Advances in Processing Nonlinear Dynamic Signals"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-95098-3_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2018,7,21]],"date-time":"2018-07-21T10:27:27Z","timestamp":1532168847000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-95098-3_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7,22]]},"ISBN":["9783319950976","9783319950983"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-95098-3_11","relation":{},"ISSN":["2190-3018","2190-3026"],"issn-type":[{"type":"print","value":"2190-3018"},{"type":"electronic","value":"2190-3026"}],"subject":[],"published":{"date-parts":[[2018,7,22]]}}}