{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T08:27:06Z","timestamp":1743064026487,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031150364"},{"type":"electronic","value":"9783031150371"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-15037-1_27","type":"book-chapter","created":{"date-parts":[[2022,8,19]],"date-time":"2022-08-19T15:03:06Z","timestamp":1660921386000},"page":"329-342","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Biologically Inspired Neural Path Finding"],"prefix":"10.1007","author":[{"given":"Hang","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qadeer","family":"Khan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Volker","family":"Tresp","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Cremers","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,8,20]]},"reference":[{"key":"27_CR1","doi-asserted-by":"crossref","unstructured":"Fornito, A., Zalesky, A., Bullmore, E.: Connectivity matrices and brain graphs. In: Fundamentals of Brain Network Analysis, pp. 89\u2013113. Academic Press, San Diego (2016)","DOI":"10.1016\/B978-0-12-407908-3.00003-0"},{"issue":"5","key":"27_CR2","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1002\/cne.21974","volume":"513","author":"FA Azevedo","year":"2009","unstructured":"Azevedo, F.A., et al.: Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. J. Compar. Neurol. 513(5), 532\u2013541 (2009)","journal-title":"J. Compar. Neurol."},{"issue":"4","key":"27_CR3","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1016\/j.neuron.2012.10.038","volume":"76","author":"AM Bastos","year":"2012","unstructured":"Bastos, A.M., et al.: Canonical microcircuits for predictive coding. Neuron 76(4), 695\u2013711 (2012)","journal-title":"Neuron"},{"issue":"10","key":"27_CR4","doi-asserted-by":"publisher","first-page":"678","DOI":"10.1038\/s43588-021-00130-y","volume":"1","author":"C Bongiorno","year":"2021","unstructured":"Bongiorno, C., et al.: Vector-based pedestrian navigation in cities. Nat. Comput. Sci. 1(10), 678\u2013685 (2021)","journal-title":"Nat. Comput. Sci."},{"key":"27_CR5","doi-asserted-by":"crossref","unstructured":"Brown, C.J., et al.: Connectome priors in deep neural networks to predict autism. In: 2018 IEEE 15th International Symposium on Biomedical Imaging, pp. 110\u2013113 (2018)","DOI":"10.1109\/ISBI.2018.8363534"},{"key":"27_CR6","doi-asserted-by":"crossref","unstructured":"Cheng, Z., et al.: The method based on Dijkstra of multi-directional ship\u2019s path planning. In: 2020 Chinese Control and Decision Conference (CCDC), pp. 5142\u20135146 (2020)","DOI":"10.1109\/CCDC49329.2020.9164597"},{"key":"27_CR7","doi-asserted-by":"crossref","unstructured":"Cire\u015fan, D., et al.: Multi-column deep neural network for traffic sign classification. Neural Netw. 32, 333\u2013338 (2012), selected Papers from IJCNN 2011","DOI":"10.1016\/j.neunet.2012.02.023"},{"issue":"1","key":"27_CR8","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1007\/BF01386390","volume":"1","author":"EW Dijkstra","year":"1959","unstructured":"Dijkstra, E.W.: A note on two problems in connexion with graphs. Num. Math. 1(1), 269\u2013271 (1959)","journal-title":"Num. Math."},{"key":"27_CR9","doi-asserted-by":"crossref","unstructured":"Geiger, A., et al.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2012)","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"27_CR10","unstructured":"Gilmer, J., et al.: Neural message passing for quantum chemistry. In: International Conference on Machine Learning, pp. 1263\u20131272. PMLR (2017)"},{"key":"27_CR11","doi-asserted-by":"crossref","unstructured":"Hidayatullah, A.S., et al.: Realization of depth first search algorithm on line maze solver robot. In: 2017 International Conference on Control, Electronics, Renewable Energy and Communications (ICCREC), pp. 247\u2013251 (2017)","DOI":"10.1109\/ICCEREC.2017.8226690"},{"key":"27_CR12","doi-asserted-by":"crossref","unstructured":"Jiang, J.R., et al.: Extending Dijkstra\u2019s shortest path algorithm for software defined networking. In: The 16th Asia-Pacific Network Operations & Management Symposium (2014)","DOI":"10.1109\/APNOMS.2014.6996609"},{"key":"27_CR13","doi-asserted-by":"publisher","first-page":"1038","DOI":"10.1016\/j.neuroimage.2016.09.046","volume":"146","author":"J Kawahara","year":"2017","unstructured":"Kawahara, J., et al.: Brainnetcnn: convolutional neural networks for brain networks; towards predicting neurodevelopment. NeuroImage 146, 1038\u20131049 (2017)","journal-title":"NeuroImage"},{"key":"27_CR14","unstructured":"Krizhevsky, et al.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc. (2012)"},{"key":"27_CR15","doi-asserted-by":"crossref","unstructured":"Leskovec, J., Rajaraman, A., Ullman, J.D.: Mining Social-Network Graphs, 2 edn. p. 325\u2013383. Cambridge University Press, London (2014)","DOI":"10.1017\/CBO9781139924801.011"},{"key":"27_CR16","doi-asserted-by":"crossref","unstructured":"Mattson, M.P.: Superior pattern processing is the essence of the evolved human brain. Front. Neurosci. 8, 265 (2014)","DOI":"10.3389\/fnins.2014.00265"},{"key":"27_CR17","doi-asserted-by":"crossref","unstructured":"Niv, Y.: Reinforcement learning in the brain. Special Issue: Dynamic Decision Making. J. Math. Psychol. 53(3), 139\u2013154 (2009)","DOI":"10.1016\/j.jmp.2008.12.005"},{"key":"27_CR18","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1016\/j.procs.2018.01.054","volume":"123","author":"AI Panov","year":"2018","unstructured":"Panov, A.I., et al.: Grid path planning with deep reinforcement learning: preliminary results. Procedia Comput. Sci. 123, 347\u2013353 (2018)","journal-title":"Procedia Comput. Sci."},{"key":"27_CR19","doi-asserted-by":"crossref","unstructured":"Pasa, F., et al.: Efficient deep network architectures for fast chest X-ray tuberculosis screening and visualization. Sci. Rep. 9 (2019)","DOI":"10.1038\/s41598-019-42557-4"},{"issue":"1","key":"27_CR20","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.tics.2020.10.004","volume":"25","author":"M Peer","year":"2021","unstructured":"Peer, M., Brunec, I.K., Newcombe, N.S., Epstein, R.A.: Structuring knowledge with cognitive maps and cognitive graphs. Trends Cogn. Sci. 25(1), 37\u201354 (2021)","journal-title":"Trends Cogn. Sci."},{"issue":"8","key":"27_CR21","first-page":"9","volume":"1","author":"A Radford","year":"2019","unstructured":"Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. OpenAI Blog 1(8), 9 (2019)","journal-title":"OpenAI Blog"},{"key":"27_CR22","unstructured":"Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, London (2018)"},{"key":"27_CR23","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. stat 1050, 20 (2017)"},{"key":"27_CR24","unstructured":"Yang, Z., Cohen, W., Salakhudinov, R.: Revisiting semi-supervised learning with graph embeddings. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of the 33rd International Conference on Machine Learning Research, vol. 48, pp. 40\u201348. PMLR, New York, USA, 20\u201322 June 2016. https:\/\/proceedings.mlr.press\/v48\/yanga16.html"},{"key":"27_CR25","doi-asserted-by":"crossref","unstructured":"Ying, R., et al.: Graph convolutional neural networks for web-scale recommender systems. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 974\u2013983 (2018)","DOI":"10.1145\/3219819.3219890"},{"issue":"24","key":"27_CR26","doi-asserted-by":"publisher","first-page":"9938","DOI":"10.1073\/pnas.1301691110","volume":"110","author":"M Zelikowsky","year":"2013","unstructured":"Zelikowsky, M., et al.: Prefrontal microcircuit underlies contextual learning after hippocampal loss. Proc. Natl. Acad. Sci. 110(24), 9938\u20139943 (2013)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"27_CR27","doi-asserted-by":"crossref","unstructured":"\u017duni\u0107, E., et al.: Software solution for optimal planning of sales persons work based on depth-first search and breadth-first search algorithms. In: 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) (2016)","DOI":"10.1109\/MIPRO.2016.7522330"}],"container-title":["Lecture Notes in Computer Science","Brain Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-15037-1_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,19]],"date-time":"2022-08-19T23:13:02Z","timestamp":1660950782000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-15037-1_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031150364","9783031150371"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-15037-1_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"20 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Brain Informatics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Padua","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 July 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 July 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"brain2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/wi-consortium.org\/conferences\/bi2022\/index.html","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":"Cyber Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"65","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":"29","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":"0","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","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":"3","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)"}}]}}