{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T10:31:41Z","timestamp":1790677901949,"version":"4.1.0"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031441974","type":"print"},{"value":"9783031441981","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-44198-1_10","type":"book-chapter","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T08:02:34Z","timestamp":1695283354000},"page":"112-123","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Attractor Dynamics Drive Flexible Timing in\u00a0Birdsong"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5533-2270","authenticated-orcid":false,"given":"Fjola","family":"Hyseni","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6972-589X","authenticated-orcid":false,"given":"Nicolas P.","family":"Rougier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9392-5939","authenticated-orcid":false,"given":"Arthur","family":"Leblois","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"issue":"5","key":"10_CR1","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/s1364-6613(97)01058-9","volume":"1","author":"E Hazeltine","year":"1997","unstructured":"Hazeltine, E., Helmuth, L.L., Ivry, R.B.: Neural mechanisms of timing. Trends Cogn. Sci. 1(5), 163\u2013169 (1997). https:\/\/doi.org\/10.1016\/s1364-6613(97)01058-9","journal-title":"Trends Cogn. Sci."},{"key":"10_CR2","unstructured":"Hansel, D., Sompolinsky, H.: Modeling feature selectivity in local cortical circuits. Book Chapter (1998)"},{"key":"10_CR3","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1007\/BF00992696","volume":"8","author":"R Williams","year":"1992","unstructured":"Williams, R.: Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach. Learn. 8, 229\u2013256 (1992). https:\/\/doi.org\/10.1007\/BF00992696","journal-title":"Mach. Learn."},{"key":"10_CR4","doi-asserted-by":"publisher","unstructured":"Robbe, D.: Lost in time: rethinking duration estimation outside the brain. PsyArXiv (2021). https:\/\/doi.org\/10.31234\/osf.io\/3bcfy","DOI":"10.31234\/osf.io\/3bcfy"},{"issue":"7","key":"10_CR5","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/j.tics.2008.04.002","volume":"12","author":"R Ivry","year":"2008","unstructured":"Ivry, R., Schlerf, J.: Dedicated and intrinsic models of time perception. Trends Cogn. Sci. 12(7), 273\u201380 (2008). https:\/\/doi.org\/10.1016\/j.tics.2008.04.002","journal-title":"Trends Cogn. Sci."},{"issue":"1637","key":"10_CR6","doi-asserted-by":"publisher","first-page":"20120460","DOI":"10.1098\/rstb.2012.0460","volume":"369","author":"A Goel","year":"2014","unstructured":"Goel, A., Buonomano, D.V.: Timing as an intrinsic property of neural networks: evidence from in vivo and in vitro experiments. Philos. Trans. R. Soc. B: Biol. Sci. 369(1637), 20120460 (2014). https:\/\/doi.org\/10.1098\/rstb.2012.0460","journal-title":"Philos. Trans. R. Soc. B: Biol. Sci."},{"issue":"12","key":"10_CR7","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1016\/j.tics.2010.09.002","volume":"14","author":"DV Buonomano","year":"2010","unstructured":"Buonomano, D.V., Laje, R.: Population clocks: motor timing with neural dynamics. Trends Cogn. Sci. 14(12), 520\u2013527 (2010). https:\/\/doi.org\/10.1016\/j.tics.2010.09.002","journal-title":"Trends Cogn. Sci."},{"key":"10_CR8","doi-asserted-by":"publisher","first-page":"5342","DOI":"10.1523\/JNEUROSCI.23-12-05342","volume":"23","author":"D Durstewitz","year":"2003","unstructured":"Durstewitz, D.: Self-organizing neural integrator predicts interval times through climbing activity. J. Neurosci. 23, 5342\u20135353 (2003). https:\/\/doi.org\/10.1523\/JNEUROSCI.23-12-05342","journal-title":"J. Neurosci."},{"key":"10_CR9","doi-asserted-by":"publisher","first-page":"9238","DOI":"10.1523\/JNEUROSCI.3121-10.2011","volume":"31","author":"P Simen","year":"2011","unstructured":"Simen, P., Balci, F., Souza, L., Cohen, J., Holmes, P.: A model of interval timing by neural integration. J. Neurosci. Official J. Soc. Neurosci. 31, 9238\u20139253 (2011). https:\/\/doi.org\/10.1523\/JNEUROSCI.3121-10.2011","journal-title":"J. Neurosci. Official J. Soc. Neurosci."},{"key":"10_CR10","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.cobeha.2016.02.002","volume":"8","author":"F Balci","year":"2016","unstructured":"Balci, F., Simen, P.: A decision model of timing. Curr. Opinion Behav. Sci. 8, 94\u2013101 (2016). https:\/\/doi.org\/10.1016\/j.cobeha.2016.02.002","journal-title":"Curr. Opinion Behav. Sci."},{"key":"10_CR11","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1038\/nature00974","volume":"419","author":"R Hahnloser","year":"2002","unstructured":"Hahnloser, R., Kozhevnikov, A., Fee, M.: An ultra-sparse code underlies the generation of neural sequences in a songbird. Nature 419, 65\u201370 (2002). https:\/\/doi.org\/10.1038\/nature00974","journal-title":"Nature"},{"key":"10_CR12","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1038\/nature09514","volume":"468","author":"M Long","year":"2010","unstructured":"Long, M., Jin, D., Fee, M.: Support for a synaptic chain model of neuronal sequence generation. Nature 468, 394\u2013399 (2010). https:\/\/doi.org\/10.1038\/nature09514","journal-title":"Nature"},{"key":"10_CR13","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1007\/s10827-007-0032-z","volume":"23","author":"D Jin","year":"2007","unstructured":"Jin, D., Ramazano\u011flu, F., Seung, H.: Intrinsic bursting enhances the robustness of a neural network model of sequence generation by avian brain area HVC. J. Comput. Neurosci. 23, 283\u2013299 (2007)","journal-title":"J. Comput. Neurosci."},{"key":"10_CR14","doi-asserted-by":"publisher","first-page":"5419","DOI":"10.1073\/pnas.1016051108","volume":"108","author":"R Perin","year":"2011","unstructured":"Perin, R., Berger, T.K., Markram, H.: A synaptic organizing principle for cortical neuronal groups. PNAS 108, 5419\u20135424 (2011). https:\/\/doi.org\/10.1073\/pnas.1016051108","journal-title":"PNAS"},{"issue":"2","key":"10_CR15","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1016\/j.neuron.2013.07.049","volume":"80","author":"F Ali","year":"2013","unstructured":"Ali, F., Otchy, T.M., Pehlevan, C., Fantana, A.L., Burak, Y., \u00d6lveczky, B.P.: The basal ganglia is necessary for learning spectral, but not temporal, features of birdsong. Neuron 80(2), 494\u2013506 (2013). https:\/\/doi.org\/10.1016\/j.neuron.2013.07.049","journal-title":"Neuron"},{"key":"10_CR16","doi-asserted-by":"publisher","first-page":"977","DOI":"10.1038\/s41467-018-03261-5","volume":"9","author":"C Pehlevan","year":"2018","unstructured":"Pehlevan, C., Ali, F., \u00d6lveczky, B.: Flexibility in motor timing constrains the topology and dynamics of pattern generator circuits. Nat. Commun. 9, 977 (2018). https:\/\/doi.org\/10.1038\/s41467-018-03261-5","journal-title":"Nat. Commun."},{"key":"10_CR17","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1523\/JNEUROSCI.3387-05.2006","volume":"26","author":"CM Glaze","year":"2006","unstructured":"Glaze, C.M., Troye, T.W.: Temporal structure in zebra finch song: implications for motor coding. J. Neurosci. 26, 991\u20131005 (2006). https:\/\/doi.org\/10.1523\/JNEUROSCI.3387-05.2006","journal-title":"J. Neurosci."},{"key":"10_CR18","doi-asserted-by":"publisher","first-page":"744","DOI":"10.1038\/s41583-022-00642-0","volume":"23","author":"M Khona","year":"2022","unstructured":"Khona, M., Fiete, I.: Attractor and integrator networks in the brain. Nat. Rev. Neurosci. 23, 744\u2013766 (2022). https:\/\/doi.org\/10.1038\/s41583-022-00642-0","journal-title":"Nat. Rev. Neurosci."},{"key":"10_CR19","doi-asserted-by":"publisher","first-page":"6924","DOI":"10.1523\/JNEUROSCI.14-11-06924.1994","volume":"14","author":"E Vu","year":"1994","unstructured":"Vu, E., Mazurek, M., Kuo, Y.: Identification of a forebrain motor programming network for the learned song of zebra finches. J. Neurosci. 14, 6924\u20136934 (1994). https:\/\/doi.org\/10.1523\/JNEUROSCI.14-11-06924.1994","journal-title":"J. Neurosci."},{"key":"10_CR20","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1152\/jn.01035.2010","volume":"106","author":"K Naie","year":"2011","unstructured":"Naie, K., Hahnloser, R.: Regulation of learned vocal behavior by an auditory motor cortical nucleus in juvenile zebra finches. J. Neurophysiol. 106, 291\u2013300 (2011). https:\/\/doi.org\/10.1152\/jn.01035.2010","journal-title":"J. Neurophysiol."},{"key":"10_CR21","doi-asserted-by":"publisher","first-page":"3851","DOI":"10.1016\/j.neuron.2021.09.021","volume":"109","author":"M Elmaleh","year":"2021","unstructured":"Elmaleh, M., Kranz, D., Asensio, A., Moll, F., Long, M.: Sleep replay reveals premotor circuit structure for a skilled behavior. Neuron 109, 3851\u20133861 (2021). https:\/\/doi.org\/10.1016\/j.neuron.2021.09.021","journal-title":"Neuron"},{"key":"10_CR22","doi-asserted-by":"publisher","first-page":"877","DOI":"10.1016\/j.neuron.2016.04.021","volume":"90","author":"G Lynch","year":"2016","unstructured":"Lynch, G., Okubo, T., Hanuschkin, A., Hahnloser, R., Fee, M.: Rhythmic continuous-time coding in the songbird analog of vocal motor cortex. Neuron 90, 877\u2013892 (2016). https:\/\/doi.org\/10.1016\/j.neuron.2016.04.021","journal-title":"Neuron"},{"key":"10_CR23","doi-asserted-by":"publisher","first-page":"3637","DOI":"10.1152\/jn.00686.2005","volume":"94","author":"R Brette","year":"2005","unstructured":"Brette, R., Gerstner, W.: Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. J. Neurophysiol. 94, 3637\u20133642 (2005)","journal-title":"J. Neurophysiol."},{"key":"10_CR24","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1038\/nature07994","volume":"459","author":"O Feh\u00e9r","year":"2009","unstructured":"Feh\u00e9r, O., Wang, H., Saar, S., Mitra, P.P., Tchernichovski, O.: De novo establishment of wild-type song culture in the zebra finch. Nature 459, 564\u2013568 (2009). https:\/\/doi.org\/10.1038\/nature07994","journal-title":"Nature"},{"key":"10_CR25","doi-asserted-by":"publisher","first-page":"1282","DOI":"10.1126\/science.aah6799","volume":"354","author":"M Araki","year":"2016","unstructured":"Araki, M., Bandi, M.M., Yazaki-Sugiyama, Y.: Mind the gap: neural coding of species identity in birdsong prosody. Science 354, 1282\u20131287 (2016). https:\/\/doi.org\/10.1126\/science.aah6799","journal-title":"Science"},{"key":"10_CR26","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1038\/nature06492","volume":"451","author":"J Prather","year":"2008","unstructured":"Prather, J., Peters, S., Nowicki, S., Mooney, R.: Precise auditory-vocal mirroring in neurons for learned vocal communication. Nature 451, 305\u2013310 (2008). https:\/\/doi.org\/10.1038\/nature06492","journal-title":"Nature"},{"key":"10_CR27","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.neubiorev.2016.12.035","volume":"81","author":"J Prather","year":"2017","unstructured":"Prather, J., Okanoya, K., Bolhuis, J.: Brains for birds and babies: neural parallels between birdsong and speech acquisition. Neurosci. Biobehav. Rev. 81, 225\u2013237 (2017). https:\/\/doi.org\/10.1016\/j.neubiorev.2016.12.035","journal-title":"Neurosci. Biobehav. Rev."}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44198-1_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T22:11:33Z","timestamp":1701209493000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44198-1_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031441974","9783031441981"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44198-1_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"22 September 2023","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":"Heraklion","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"32","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2023\/","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":"easyacademia.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"947","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":"426","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":"22","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":"45% - 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":"2.4","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":"4","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"type of other papers accepted  : 9 Abstract","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)"}}]}}