{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T07:14:13Z","timestamp":1742973253512,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031430749"},{"type":"electronic","value":"9783031430756"}],"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\/978-3-031-43075-6_9","type":"book-chapter","created":{"date-parts":[[2023,9,12]],"date-time":"2023-09-12T07:02:17Z","timestamp":1694502137000},"page":"96-108","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Investigating the\u00a0Generative Dynamics of\u00a0Energy-Based Neural Networks"],"prefix":"10.1007","author":[{"given":"Lorenzo","family":"Tausani","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7062-4861","authenticated-orcid":false,"given":"Alberto","family":"Testolin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4651-6390","authenticated-orcid":false,"given":"Marco","family":"Zorzi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,13]]},"reference":[{"issue":"2","key":"9_CR1","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1016\/j.neuron.2018.03.015","volume":"98","author":"A Mitra","year":"2018","unstructured":"Mitra, A., et al.: Spontaneous infra-slow brain activity has unique spatiotemporal dynamics and laminar structure. Neuron 98(2), 297\u2013305 (2018)","journal-title":"Neuron"},{"issue":"1705","key":"9_CR2","doi-asserted-by":"publisher","first-page":"20150546","DOI":"10.1098\/rstb.2015.0546","volume":"371","author":"A Mitra","year":"2016","unstructured":"Mitra, A., Raichle, M.E.: How networks communicate: propagation patterns in spontaneous brain activity. Philos. Trans. R. Soc. B Biol. Sci. 371(1705), 20150546 (2016)","journal-title":"Philos. Trans. R. Soc. B Biol. Sci."},{"issue":"2","key":"9_CR3","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1089\/brain.2011.0014","volume":"1","author":"W-J Pan","year":"2011","unstructured":"Pan, W.-J., Thompson, G., Magnuson, M., Majeed, W., Jaeger, D., Keilholz, S.: Broadband local field potentials correlate with spontaneous fluctuations in functional magnetic resonance imaging signals in the rat somatosensory cortex under isoflurane anesthesia. Brain Connect. 1(2), 119\u2013131 (2011)","journal-title":"Brain Connect."},{"key":"9_CR4","doi-asserted-by":"crossref","unstructured":"Tortella-Feliu, M., Morillas-Romero, A., Balle, M., Llabr\u00e9s, J., Bornas, X., Putman, P.: Spontaneous EEG activity and spontaneous emotion regulation. Int. J. Psychophysiol. 94(3), 365\u2013372 (2014)","DOI":"10.1016\/j.ijpsycho.2014.09.003"},{"issue":"6","key":"9_CR5","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1159\/000442424","volume":"93","author":"EC Leuthardt","year":"2015","unstructured":"Leuthardt, E.C., et al.: Resting-state blood oxygen level-dependent functional MRI: a paradigm shift in preoperative brain mapping. Stereotact. Funct. Neurosurg. 93(6), 427\u2013439 (2015)","journal-title":"Stereotact. Funct. Neurosurg."},{"issue":"9","key":"9_CR6","doi-asserted-by":"publisher","first-page":"730","DOI":"10.1016\/j.tics.2021.05.007","volume":"25","author":"G Pezzulo","year":"2021","unstructured":"Pezzulo, G., Zorzi, M., Corbetta, M.: The secret life of predictive brains: what\u2019s spontaneous activity for? Trends Cogn. Sci. 25(9), 730\u2013743 (2021)","journal-title":"Trends Cogn. Sci."},{"key":"9_CR7","doi-asserted-by":"publisher","first-page":"90","DOI":"10.3389\/fncom.2018.00090","volume":"12","author":"T Parr","year":"2018","unstructured":"Parr, T., Friston, K.J.: The anatomy of inference: generative Models and brain structure. Front. Comput. Neurosci. 12, 90 (2018)","journal-title":"Front. Comput. Neurosci."},{"issue":"1","key":"9_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-017-03073-5","volume":"7","author":"G Deco","year":"2017","unstructured":"Deco, G., Kringelbach, M.L., Jirsa, V.K., Ritter, P.: The dynamics of resting fluctuations in the brain: metastability and its dynamical cortical core. Sci. Rep. 7(1), 1\u201314 (2017)","journal-title":"Sci. Rep."},{"issue":"1","key":"9_CR9","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.neuron.2013.12.022","volume":"81","author":"E Tognoli","year":"2014","unstructured":"Tognoli, E., Kelso, J.A.S.: The metastable brain. Neuron 81(1), 35\u201348 (2014)","journal-title":"Neuron"},{"issue":"11","key":"9_CR10","doi-asserted-by":"publisher","first-page":"1761","DOI":"10.1038\/s41593-019-0520-2","volume":"22","author":"BA Richards","year":"2019","unstructured":"Richards, B.A., et al.: A deep learning framework for neuroscience. Nat. Neurosci. 22(11), 1761\u20131770 (2019)","journal-title":"Nat. Neurosci."},{"issue":"1","key":"9_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12021-018-9360-6","volume":"16","author":"E De Schutter","year":"2018","unstructured":"De Schutter, E.: Deep learning and computational neuroscience. Neuroinformatics 16(1), 1\u20132 (2018)","journal-title":"Neuroinformatics"},{"issue":"3","key":"9_CR12","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1038\/nn.4244","volume":"19","author":"DL Yamins","year":"2016","unstructured":"Yamins, D.L., DiCarlo, J.J.: Using goal-driven deep learning models to understand sensory cortex. Nat. Neurosci. 19(3), 356\u2013365 (2016)","journal-title":"Nat. Neurosci."},{"issue":"2","key":"9_CR13","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1038\/nn.2996","volume":"15","author":"I Stoianov","year":"2012","unstructured":"Stoianov, I., Zorzi, M.: Emergence of a \u2018visual number sense\u2019 in hierarchical generative models. Nat. Neurosci. 15(2), 194\u2013196 (2012)","journal-title":"Nat. Neurosci."},{"key":"9_CR14","doi-asserted-by":"publisher","first-page":"515","DOI":"10.3389\/fpsyg.2013.00515","volume":"4","author":"M Zorzi","year":"2013","unstructured":"Zorzi, M., Testolin, A., Stoianov, I.P.: Modeling language and cognition with deep unsupervised learning: a tutorial overview. Front. Psychol. 4, 515 (2013)","journal-title":"Front. Psychol."},{"issue":"9","key":"9_CR15","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1038\/s41562-017-0186-2","volume":"1","author":"A Testolin","year":"2017","unstructured":"Testolin, A., Stoianov, I., Zorzi, M.: Letter perception emerges from unsupervised deep learning and recycling of natural image features. Nat. Hum. Behav. 1(9), 657\u2013664 (2017)","journal-title":"Nat. Hum. Behav."},{"issue":"5","key":"9_CR16","doi-asserted-by":"publisher","DOI":"10.1111\/desc.12940","volume":"23","author":"A Testolin","year":"2020","unstructured":"Testolin, A., Zou, W.Y., McClelland, J.L.: Numerosity discrimination in deep neural networks: initial competence, developmental refinement and experience statistics. Dev. Sci. 23(5), e12940 (2020)","journal-title":"Dev. Sci."},{"issue":"1","key":"9_CR17","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1007\/s12559-022-10085-5","volume":"15","author":"M Zambra","year":"2022","unstructured":"Zambra, M., Testolin, A., Zorzi, M.: A developmental approach for training deep belief networks. Cogn. Comput. 15(1), 103\u2013120 (2022)","journal-title":"Cogn. Comput."},{"issue":"1","key":"9_CR18","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1207\/s15516709cog0901_7","volume":"9","author":"DH Ackley","year":"1985","unstructured":"Ackley, D.H., Hinton, G.E., Sejnowski, T.J.: A learning algorithm for Boltzmann machines. Cogn. Sci. 9(1), 147\u2013169 (1985)","journal-title":"Cogn. Sci."},{"key":"9_CR19","doi-asserted-by":"crossref","unstructured":"Roussel, C., Cocco, S., Monasson, R.: Barriers and dynamical paths in alternating Gibbs sampling of restricted Boltzmann machines. Phys. Rev. E 104(3) (2021)","DOI":"10.1103\/PhysRevE.104.034109"},{"issue":"11","key":"9_CR20","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"9_CR21","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: 2015 IEEE International Conference on Computer Vision (ICCV) (2015)","DOI":"10.1109\/ICCV.2015.425"},{"key":"9_CR22","volume-title":"Deep Learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. The MIT Press, Cambridge, MA, USA (2016)"},{"issue":"8","key":"9_CR23","doi-asserted-by":"publisher","first-page":"1771","DOI":"10.1162\/089976602760128018","volume":"14","author":"GE Hinton","year":"2002","unstructured":"Hinton, G.E.: Training products of experts by minimizing contrastive divergence. Neural Comput. 14(8), 1771\u20131800 (2002)","journal-title":"Neural Comput."},{"key":"9_CR24","doi-asserted-by":"crossref","unstructured":"Testolin, A., Stoianov, I., De Filippo De Grazia, M., Zorzi, M.: Deep unsupervised learning on a desktop PC: a primer for cognitive scientists. Front. Psychol. 4 (2013)","DOI":"10.3389\/fpsyg.2013.00251"},{"key":"9_CR25","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv abs\/1409.1556 (2014)"},{"key":"9_CR26","doi-asserted-by":"crossref","unstructured":"Testolin, A., De Filippo De Grazia, M., Zorzi, M.: The role of architectural and learning constraints in neural network models: a case study on visual space coding. Front. Comput. Neurosci. 11, 13 (2017)","DOI":"10.3389\/fncom.2017.00013"},{"key":"9_CR27","unstructured":"Liao, R., Kornblith, S., Ren, M., Fleet, D. J., Hinton, G.: Gaussian-Bernoulli RBMs Without Tears. arXiv abs\/2210.10318 (2022)"},{"key":"9_CR28","unstructured":"Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp. 807\u2013814 (2010)"},{"key":"9_CR29","doi-asserted-by":"crossref","unstructured":"Sauvola, J., Pietik\u00e4inen, M.: Adaptive document image binarization. Pattern Recognit. 33, 225\u2013236 (2000)","DOI":"10.1016\/S0031-3203(99)00055-2"},{"key":"9_CR30","doi-asserted-by":"crossref","unstructured":"Fernandez-de-Cossio-Diaz, J., Cocco, S., Monasson, R.: Disentangling representations in restricted Boltzmann machines without adversaries. Phys. Rev. X 13 (2023)","DOI":"10.1103\/PhysRevX.13.021003"},{"key":"9_CR31","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.90"}],"container-title":["Lecture Notes in Computer Science","Brain Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43075-6_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,12]],"date-time":"2023-09-12T07:03:23Z","timestamp":1694502203000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43075-6_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031430749","9783031430756"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43075-6_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"13 September 2023","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":"Hoboken, NJ","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","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":"1 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"brain2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/wi-consortium.org\/conferences\/bi2023\/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":"CyberChair System","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"101","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":"40","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":"40% - 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)"}}]}}