{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T16:55:24Z","timestamp":1790528124273,"version":"4.1.0"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030863821","type":"print"},{"value":"9783030863838","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-86383-8_19","type":"book-chapter","created":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T08:02:49Z","timestamp":1631260969000},"page":"233-244","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Dynamic Action Inference with Recurrent Spiking Neural Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0897-1701","authenticated-orcid":false,"given":"Manuel","family":"Traub","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8120-8537","authenticated-orcid":false,"given":"Martin V.","family":"Butz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8724-5507","authenticated-orcid":false,"given":"Robert","family":"Legenstein","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0305-0463","authenticated-orcid":false,"given":"Sebastian","family":"Otte","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,9,7]]},"reference":[{"key":"19_CR1","unstructured":"Bellec, G., Salaj, D., Subramoney, A., Legenstein, R., Maass, W.: Long short-term memory and learning-to-learn in networks of spiking neurons. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS 2018, pp. 795\u2013805. Curran Associates Inc., Red Hook (2018)"},{"issue":"1","key":"19_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-020-17236-y","volume":"11","author":"G Bellec","year":"2020","unstructured":"Bellec, G., et al.: A solution to the learning dilemma for recurrent networks of spiking neurons. Nat. Commun. 11(1), 1\u201315 (2020)","journal-title":"Nat. Commun."},{"key":"19_CR3","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.neunet.2019.05.001","volume":"117","author":"MV Butz","year":"2019","unstructured":"Butz, M.V., Bilkey, D., Humaidan, D., Knott, A., Otte, S.: Learning, planning, and control in a monolithic neural event inference architecture. Neural Netw. 117, 135\u2013144 (2019)","journal-title":"Neural Netw."},{"key":"19_CR4","volume-title":"How the Mind Comes Into Being: Introducing Cognitive Science from a Functional and Computational Perspective","author":"MV Butz","year":"2016","unstructured":"Butz, M.V., Kutter, E.F.: How the Mind Comes Into Being: Introducing Cognitive Science from a Functional and Computational Perspective. Oxford University Press, Oxford (2016)"},{"key":"19_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1007\/978-3-030-30487-4_49","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2019: Theoretical Neural Computation","author":"MV Butz","year":"2019","unstructured":"Butz, M.V., Menge, T., Humaidan, D., Otte, S.: Inferring event-predictive goal-directed object manipulations in REPRISE. In: Tetko, I.V., Kurkov\u00e1, V., Karpov, P., Theis, F. (eds.) ICANN 2019, Part I. LNCS, vol. 11727, pp. 639\u2013653. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-30487-4_49"},{"key":"19_CR6","volume-title":"Surfing Uncertainty: Prediction, Action, and the Embodied Mind","author":"A Clark","year":"2015","unstructured":"Clark, A.: Surfing Uncertainty: Prediction, Action, and the Embodied Mind. Oxford University Press, Oxford (2015)"},{"issue":"7","key":"19_CR7","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1016\/j.tics.2009.04.005","volume":"13","author":"K Friston","year":"2009","unstructured":"Friston, K.: The free-energy principle: a rough guide to the brain? Trends Cogn. Sci. 13(7), 293\u2013301 (2009)","journal-title":"Trends Cogn. Sci."},{"issue":"8","key":"19_CR8","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"19_CR9","unstructured":"Mnih, V., et al.: Playing atari with deep reinforcement learning. arXiv:1312.5602 (2013)"},{"key":"19_CR10","unstructured":"Nichol, A., Pfau, V., Hesse, C., Klimov, O., Schulman, J.: Gotta learn fast: a new benchmark for generalization in RL. arXiv:1804.03720 (2018)"},{"key":"19_CR11","unstructured":"OpenAI, et al.: Dota 2 with large scale deep reinforcement learning. arXiv:1912.06680 (2019)"},{"key":"19_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1007\/978-3-319-68600-4_27","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2017","author":"S Otte","year":"2017","unstructured":"Otte, S., Schmitt, T., Friston, K., Butz, M.V.: Inferring adaptive goal-directed behavior within recurrent neural networks. In: Lintas, A., Rovetta, S., Verschure, P.F.M.J., Villa, A.E.P. (eds.) ICANN 2017, Part I. LNCS, vol. 10613, pp. 227\u2013235. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-68600-4_27"},{"key":"19_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1007\/978-3-030-30490-4_44","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2019: Text and Time Series","author":"S Otte","year":"2019","unstructured":"Otte, S., Stoll, J., Butz, M.V.: Incorporating adaptive RNN-based action inference and sensory perception. In: Tetko, I.V., K\u016frkov\u00e1, V., Karpov, P., Theis, F. (eds.) ICANN 2019. LNCS, vol. 11730, pp. 543\u2013555. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-30490-4_44"},{"key":"19_CR14","unstructured":"Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv:1707.06347 (2017)"},{"key":"19_CR15","doi-asserted-by":"crossref","unstructured":"Traub, M., Legenstein, R., Otte, S.: Many-joint robot arm control with recurrent spiking neural networks. In: IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (2021). accepted for publication, preprint available (arXiv:2104.04064)","DOI":"10.1109\/IROS51168.2021.9636001"},{"key":"19_CR16","doi-asserted-by":"crossref","unstructured":"Yin, B., Corradi, F., Bohte, S.M.: Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks. arXiv:2103.12593 (2021)","DOI":"10.1101\/2021.03.22.436372"},{"key":"19_CR17","doi-asserted-by":"crossref","unstructured":"Yin, B., Corradi, F., Boht\u00e9, S.M.: Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks. bioRxiv:2021.03.22.436372 (2021)","DOI":"10.1101\/2021.03.22.436372"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86383-8_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T14:43:31Z","timestamp":1709822611000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86383-8_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030863821","9783030863838"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86383-8_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"7 September 2021","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":"Bratislava","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Slovakia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"496","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"265","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"53% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Conference was held online due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}