{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T22:10:00Z","timestamp":1778364600821,"version":"3.51.4"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032190987","type":"print"},{"value":"9783032190994","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-19099-4_30","type":"book-chapter","created":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T22:05:35Z","timestamp":1778364335000},"page":"416-431","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Modeling Diagonal State Space Models as\u00a0Electric Circuits for\u00a0Analog Neural Network Inference"],"prefix":"10.1007","author":[{"given":"Matthias","family":"Bittner","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Schn\u00f6ll","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fabian","family":"Seiler","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthias","family":"Wess","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Axel","family":"Jantsch","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,1]]},"reference":[{"issue":"5","key":"30_CR1","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1109\/JPROC.2014.2313565","volume":"102","author":"BV Benjamin","year":"2014","unstructured":"Benjamin, B.V., et al.: Neurogrid: a mixed-analog-digital multichip system for large-scale neural simulations. Proc. IEEE 102(5), 699\u2013716 (2014). https:\/\/doi.org\/10.1109\/JPROC.2014.2313565","journal-title":"Proc. IEEE"},{"issue":"8","key":"30_CR2","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1007\/s10994-025-06807-z","volume":"114","author":"M Bittner","year":"2025","unstructured":"Bittner, M., Schn\u00f6ll, D., Wess, M., Jantsch, A.: Efficient and interpretable raw audio classification with diagonal state space models. Mach. Learn. 114(8), 175 (2025). https:\/\/doi.org\/10.1007\/s10994-025-06807-z","journal-title":"Mach. Learn."},{"key":"30_CR3","doi-asserted-by":"publisher","unstructured":"Bonassi, F., Andersson, C., Mattsson, P., Sch\u00f6n, T.B.: Structured state-space models are deep wiener models. IFAC-PapersOnLine 58(15), 247\u2013252 (2024). https:\/\/doi.org\/10.1016\/j.ifacol.2024.08.536. iFAC Symposium on System Identification SYSID 2024","DOI":"10.1016\/j.ifacol.2024.08.536"},{"key":"30_CR4","doi-asserted-by":"publisher","unstructured":"Cho, K., Merri\u00ebnboer, B.v., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder\u2013decoder approaches. Proceedings Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation (2014). https:\/\/doi.org\/10.3115\/v1\/w14-4012","DOI":"10.3115\/v1\/w14-4012"},{"issue":"1","key":"30_CR5","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/MM.2018.112130359","volume":"38","author":"M Davies","year":"2018","unstructured":"Davies, M., et al.: Loihi: a neuromorphic manycore processor with on-chip learning. IEEE Micro 38(1), 82\u201399 (2018). https:\/\/doi.org\/10.1109\/MM.2018.112130359","journal-title":"IEEE Micro"},{"key":"30_CR6","unstructured":"Gu, A., Goel, K., R\u00e9, C.: Efficiently modeling long sequences with structured state spaces. In: The International Conference on Learning Representations (2022)"},{"key":"30_CR7","unstructured":"Gu, A., Gupta, A., Goel, K., R\u00e9, C.: On the parameterization and initialization of diagonal state space models. In: Proceedings of the 36th International Conference on Neural Information Processing Systems. NIPS \u201922, Curran Associates Inc., Red Hook, NY, USA (2024)"},{"key":"30_CR8","unstructured":"Gu, A., Johnson, I., Timalsina, A., Rudra, A., Re, C.: How to train your HIPPO: state space models with generalized orthogonal basis projections. In: International Conference on Learning Representations (2023)"},{"key":"30_CR9","unstructured":"Gupta, A., Gu, A., Berant, J.: Diagonal state spaces are as effective as structured state spaces. In: Proceedings of the 36th NeurIPS. NIPS \u201922, Curran Associates Inc., Red Hook, NY, USA (2024)"},{"key":"30_CR10","doi-asserted-by":"crossref","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"30_CR11","unstructured":"H\u00f6ppner, S., et al.: The spinnaker 2 processing element architecture for hybrid digital neuromorphic computing (2022). https:\/\/arxiv.org\/abs\/2103.08392"},{"key":"30_CR12","doi-asserted-by":"publisher","unstructured":"Ielmini, D., Pedretti, G.: Device and circuit architectures for in-memory computing. Adv. Intell. Syst. 2 (2020). https:\/\/doi.org\/10.1002\/aisy.202000040","DOI":"10.1002\/aisy.202000040"},{"key":"30_CR13","doi-asserted-by":"publisher","unstructured":"Khan, M., et al.: Spinnaker: Mapping neural networks onto a massively-parallel chip multiprocessor. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 2849\u20132856 (2008). https:\/\/doi.org\/10.1109\/IJCNN.2008.4634199","DOI":"10.1109\/IJCNN.2008.4634199"},{"key":"30_CR14","doi-asserted-by":"publisher","unstructured":"Mannocci, P., et al.: In-memory computing with emerging memory devices: status and outlook. APL Mach. Learn. 1(1), 010902 (2023). https:\/\/doi.org\/10.1063\/5.0136403","DOI":"10.1063\/5.0136403"},{"key":"30_CR15","doi-asserted-by":"publisher","unstructured":"Meier, K.: A mixed-signal universal neuromorphic computing system. In: 2015 IEEE International Electron Devices Meeting (IEDM), pp. 4.6.1\u20134.6.4 (2015). https:\/\/doi.org\/10.1109\/IEDM.2015.7409627","DOI":"10.1109\/IEDM.2015.7409627"},{"key":"30_CR16","doi-asserted-by":"publisher","unstructured":"Merolla, P., Arthur, J., Akopyan, F., Imam, N., Manohar, R., Modha, D.S.: A digital neurosynaptic core using embedded crossbar memory with 45pj per spike in 45nm. In: 2011 IEEE Custom Integrated Circuits Conference (CICC), pp.\u00a01\u20134 (2011). https:\/\/doi.org\/10.1109\/CICC.2011.6055294","DOI":"10.1109\/CICC.2011.6055294"},{"key":"30_CR17","doi-asserted-by":"crossref","unstructured":"Meyer, S.M., et al.: A diagonal structured state space model on loihi 2 for efficient streaming sequence processing (2024). https:\/\/arxiv.org\/abs\/2409.15022","DOI":"10.1109\/NICE65350.2025.11065663"},{"key":"30_CR18","unstructured":"Nagel, L.W., Pederson, D.: Spice (simulation program with integrated circuit emphasis). Technical Report UCB\/ERL M382 (1973). http:\/\/www2.eecs.berkeley.edu\/Pubs\/TechRpts\/1973\/22871.html"},{"key":"30_CR19","doi-asserted-by":"publisher","unstructured":"Nguyen, D.A., Tran, X.T., Iacopi, F.: A review of algorithms and hardware implementations for spiking neural networks. J. Low Pow. Electr. Appl. 11(2) (2021). https:\/\/doi.org\/10.3390\/jlpea11020023","DOI":"10.3390\/jlpea11020023"},{"key":"30_CR20","doi-asserted-by":"crossref","unstructured":"Orchard, G., et al.: Efficient neuromorphic signal processing with loihi 2 (2021). https:\/\/arxiv.org\/abs\/2111.03746","DOI":"10.1109\/SiPS52927.2021.00053"},{"key":"30_CR21","unstructured":"Orvieto, A., et al.: Resurrecting recurrent neural networks for long sequences. In: Proceedings of the 40th International Conference on Machine Learning. ICML\u201923 (2023)"},{"key":"30_CR22","doi-asserted-by":"crossref","unstructured":"Pehle, C., et al.: The brainscales-2 accelerated neuromorphic system with hybrid plasticity (2022). https:\/\/arxiv.org\/abs\/2201.11063","DOI":"10.3389\/fnins.2022.795876"},{"key":"30_CR23","doi-asserted-by":"crossref","unstructured":"Rahimi Azghadi, M., et al.: Complementary metal-oxide semiconductor and memristive hardware for neuromorphic computing. Adv. Intell. Syst. 2(5), 1900189 (2020)","DOI":"10.1002\/aisy.201900189"},{"key":"30_CR24","doi-asserted-by":"publisher","unstructured":"Rathi, N., et al.: Exploring neuromorphic computing based on spiking neural networks: Algorithms to hardware. ACM Comput. Surv. 55(12) (2023). https:\/\/doi.org\/10.1145\/3571155","DOI":"10.1145\/3571155"},{"key":"30_CR25","doi-asserted-by":"publisher","unstructured":"Schemmel, J., Fieres, J., Meier, K.: Wafer-scale integration of analog neural networks. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 431\u2013438 (2008). https:\/\/doi.org\/10.1109\/IJCNN.2008.4633828","DOI":"10.1109\/IJCNN.2008.4633828"},{"key":"30_CR26","doi-asserted-by":"publisher","unstructured":"Schuman, C., et al.: A survey of neuromorphic computing and neural networks in hardware (2017). https:\/\/doi.org\/10.48550\/arXiv.1705.06963","DOI":"10.48550\/arXiv.1705.06963"},{"key":"30_CR27","doi-asserted-by":"publisher","unstructured":"Sebastian, A., Gallo, M.L., Khaddam-Aljameh, R., Eleftheriou, E.: Memory devices and applications for in-memory computing. Nat. Nanotechnol. 15(7), 529\u2013544 (2020). https:\/\/doi.org\/10.1038\/s41565-020-0655-z","DOI":"10.1038\/s41565-020-0655-z"},{"issue":"1","key":"30_CR28","doi-asserted-by":"publisher","first-page":"8719","DOI":"10.1038\/s41598-023-35760-x","volume":"13","author":"MREU Shougat","year":"2023","unstructured":"Shougat, M.R.E.U., Li, X., Shao, S., McGarvey, K., Perkins, E.: Hopf physical reservoir computer for reconfigurable sound recognition. Sci. Rep. 13(1), 8719 (2023). https:\/\/doi.org\/10.1038\/s41598-023-35760-x","journal-title":"Sci. Rep."},{"issue":"2","key":"30_CR29","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1109\/MCAS.2022.3166331","volume":"22","author":"A Shrestha","year":"2022","unstructured":"Shrestha, A., Fang, H., Mei, Z., Rider, D.P., Wu, Q., Qiu, Q.: A survey on neuromorphic computing: models and hardware. IEEE Circuits Syst. Mag. 22(2), 6\u201335 (2022). https:\/\/doi.org\/10.1109\/MCAS.2022.3166331","journal-title":"IEEE Circuits Syst. Mag."},{"key":"30_CR30","doi-asserted-by":"crossref","unstructured":"Siegel, S., Yang, M.J., Strachan, J.P.: Imssa: Deploying modern state-space models on memristive in-memory compute hardware (2024). https:\/\/arxiv.org\/abs\/2412.20215","DOI":"10.1109\/ISCAS56072.2025.11043527"},{"key":"30_CR31","unstructured":"Smith, J.T., Warrington, A., Linderman, S.: Simplified state space layers for sequence modeling. In: The Eleventh International Conference on Learning Representations (2023)"},{"key":"30_CR32","doi-asserted-by":"crossref","unstructured":"Wang, S., Xue, B.: State-space models with layer-wise nonlinearity are universal approximators with exponential decaying memory. In: Thirty-seventh Conference on Neural Information Processing Systems (2023)","DOI":"10.52202\/075280-3239"},{"key":"30_CR33","unstructured":"Warden, P.: Speech commands: a dataset for limited-vocabulary speech recognition (2018)"},{"issue":"1","key":"30_CR34","doi-asserted-by":"publisher","first-page":"2056","DOI":"10.1038\/s41467-024-45187-1","volume":"15","author":"M Yan","year":"2024","unstructured":"Yan, M., Huang, C., Bienstman, P., Tino, P., Lin, W., Sun, J.: Emerging opportunities and challenges for the future of reservoir computing. Nat. Commun. 15(1), 2056 (2024). https:\/\/doi.org\/10.1038\/s41467-024-45187-1","journal-title":"Nat. Commun."},{"issue":"3","key":"30_CR35","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1109\/MCAS.2021.3092533","volume":"21","author":"S Yu","year":"2021","unstructured":"Yu, S., Jiang, H., Huang, S., Peng, X., Lu, A.: Compute-in-memory chips for deep learning: recent trends and prospects. IEEE Circuits Syst. Mag. 21(3), 31\u201356 (2021). https:\/\/doi.org\/10.1109\/MCAS.2021.3092533","journal-title":"IEEE Circuits Syst. Mag."}],"container-title":["Communications in Computer and Information Science","Machine Learning and Principles and Practice of Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-19099-4_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T22:05:39Z","timestamp":1778364339000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-19099-4_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032190987","9783032190994"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-19099-4_30","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"1 April 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Porto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecmlpkdd.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}