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Matsutani, \u201cAn area-efficient implementation of recurrent neural network core for unsupervised anomaly detection,\u201d Proc. IEEE Symposium in Low-Power and High-Speed Chips and Systems (COOL CHIPS), pp.1-3, April 2020. 10.1109\/COOLCHIPS49199.2020.9097631","DOI":"10.1109\/COOLCHIPS49199.2020.9097631"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] H. Hotelling, \u201cThe generalization of student&apos;s ratio,\u201d Ann. Math. Statist., vol.2, no.3, pp.360-378, Aug. 1931. 10.1214\/aoms\/1177732979","DOI":"10.1214\/aoms\/1177732979"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] M.I. Jordan, \u201cChapter 25-serial order: A parallel distributed processing approach,\u201d in Neural-Network Models of Cognition, ed. J.W. Donahoe and V.P. Dorsel, Advances in Psychology, vol.121, pp.471-495, North-Holland, 1997. 10.1016\/S0166-4115(97)80111-2","DOI":"10.1016\/S0166-4115(97)80111-2"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] J.L. Elman, \u201cFinding structure in time,\u201d Cognitive Science, vol.14, no.2, pp.179-211, March 1990. 10.1207\/s15516709cog1402_1","DOI":"10.1207\/s15516709cog1402_1"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] M. Arnold, R.K.E. Bellamy, M. Hind, S. Houde, S. Mehta, A. Mojsilovi\u0107, R. Nair, K.N. Ramamurthy, A. Olteanu, D. Piorkowski, D. Reimer, J. Richards, J. Tsay, and K.R. Varshney, \u201cFactSheets: Increasing trust in AI services through supplier&apos;s declarations of conformity,\u201d IBM J. Research and Development, vol.63, no.4\/5, pp.6:1-6:13, July-Sept. 2019. 10.1147\/JRD.2019.2942288","DOI":"10.1147\/JRD.2019.2942288"},{"key":"10","unstructured":"[11] H. Jaeger, \u201cAdaptive nonlinear system identification with echo state networks,\u201d Proc. Int. Conf. Neural Information Processing Systems, pp.609-616, Jan. 2002."},{"key":"11","doi-asserted-by":"publisher","unstructured":"[12] S. Hochreiter and J. Schmidhuber, \u201cLong short-term memory,\u201d Neural Computation, vol.9, no.8, pp.1735-1780, Nov. 1997. 10.1162\/neco.1997.9.8.1735","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[13] P.R. Vlachas, J. Pathak, B.R. Hunt, T.P. Sapsis, M. Girvan, E. Ott, and P. Koumoutsakos, \u201cBackpropagation algorithms and reservoir computing in recurrent neural networks for the forecasting of complex spatiotemporal dynamics,\u201d Neural Networks, vol.126, pp.191-217, June 2020. 10.1016\/j.neunet.2020.02.016","DOI":"10.1016\/j.neunet.2020.02.016"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[14] N. Liang, G. Huang, P. Saratchandran, and N. Sundararajan, \u201cA Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks,\u201d IEEE Trans. Neural Networks, vol.17, no.6, pp.1411-1423, Nov. 2006. 10.1109\/TNN.2006.880583","DOI":"10.1109\/TNN.2006.880583"},{"key":"14","doi-asserted-by":"publisher","unstructured":"[15] D. Sussillo and L.F. Abbott, \u201cGenerating coherent patterns of activity from chaotic neural networks,\u201d Neuron, vol.63, no.4, pp.544-557, Aug. 2009. 10.1016\/j.neuron.2009.07.018","DOI":"10.1016\/j.neuron.2009.07.018"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[16] M.S. Kulkarni and C. Teuscher, \u201cMemristor-based reservoir computing,\u201d Proc. IEEE\/ACM Int. Symp. Nanoscale Architectures (NANOARCH), pp.226-232, July 2012. 10.1145\/2765491.2765531","DOI":"10.1145\/2765491.2765531"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[17] M.L. Alomar, V. Canals, V. Mart\u00ednez-Moll, and J.L. Rossell\u00f3, \u201cLow-cost hardware implementation of reservoir computers,\u201d Proc. 24th International Workshop on Power and Timing Modeling, Optimization and Simulation (PATMOS), pp.1-5, Sept. 2014. 10.1109\/PATMOS.2014.6951899","DOI":"10.1109\/PATMOS.2014.6951899"},{"key":"17","doi-asserted-by":"publisher","unstructured":"[18] Y. Yi, Y. Liao, B. Wang, X. Fu, F. Shen, H. Hou, and L. Liu, \u201cFPGA based spike-time dependent encoder and reservoir design in neuromorphic computing processors,\u201d Microprocessors and Microsystems, vol.46, pp.175-183, Oct. 2016. 10.1016\/j.micpro.2016.03.009","DOI":"10.1016\/j.micpro.2016.03.009"},{"key":"18","unstructured":"[19] H. Jaeger, \u201cTutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the \u201cecho state network\u201d approach,\u201d German National Research Center for Information Technology, GMD Report, vol.159, Oct. 2002."},{"key":"19","doi-asserted-by":"publisher","unstructured":"[20] M. 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