{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T23:16:06Z","timestamp":1772752566417,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1011964","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T00:00:00Z","timestamp":1715212800000}}],"reference-count":91,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T00:00:00Z","timestamp":1714348800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Swiss Data Science Center","award":["C18-10"],"award-info":[{"award-number":["C18-10"]}]},{"DOI":"10.13039\/501100003006","name":"ETH Zurich","doi-asserted-by":"crossref","award":["PMB-01-18"],"award-info":[{"award-number":["PMB-01-18"]}],"id":[{"id":"10.13039\/501100003006","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","award":["694829"],"award-info":[{"award-number":["694829"]}],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Probing the architecture of neuronal circuits and the principles that underlie their functional organization remains an important challenge of modern neurosciences. This holds true, in particular, for the inference of neuronal connectivity from large-scale extracellular recordings. Despite the popularity of this approach and a number of elaborate methods to reconstruct networks, the degree to which synaptic connections can be reconstructed from spike-train recordings alone remains controversial. Here, we provide a framework to probe and compare connectivity inference algorithms, using a combination of synthetic ground-truth and in vitro data sets, where the connectivity labels were obtained from simultaneous high-density microelectrode array (HD-MEA) and patch-clamp recordings. We find that reconstruction performance critically depends on the regularity of the recorded spontaneous activity, i.e., their dynamical regime, the type of connectivity, and the amount of available spike-train data. We therefore introduce an ensemble artificial neural network (eANN) to improve connectivity inference. We train the eANN on the validated outputs of six established inference algorithms and show how it improves network reconstruction accuracy and robustness. Overall, the eANN demonstrated strong performance across different dynamical regimes, worked well on smaller datasets, and improved the detection of synaptic connectivity, especially inhibitory connections. Results indicated that the eANN also improved the topological characterization of neuronal networks. The presented methodology contributes to advancing the performance of inference algorithms and facilitates our understanding of how neuronal activity relates to synaptic connectivity.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1011964","type":"journal-article","created":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T13:43:40Z","timestamp":1714398220000},"page":"e1011964","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":10,"title":["Ensemble learning and ground-truth validation of synaptic connectivity inferred from spike trains"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4499-2895","authenticated-orcid":true,"given":"Christian","family":"Donner","sequence":"first","affiliation":[]},{"given":"Julian","family":"Bartram","sequence":"additional","affiliation":[]},{"given":"Philipp","family":"Hornauer","sequence":"additional","affiliation":[]},{"given":"Taehoon","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Damian","family":"Roqueiro","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3838-2468","authenticated-orcid":true,"given":"Andreas","family":"Hierlemann","sequence":"additional","affiliation":[]},{"given":"Guillaume","family":"Obozinski","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9347-9203","authenticated-orcid":true,"given":"Manuel","family":"Schr\u00f6ter","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2024,4,29]]},"reference":[{"issue":"6559","key":"pcbi.1011964.ref001","article-title":"Architectures of neuronal circuits","volume":"373","author":"L Luo","year":"2021","journal-title":"Science"},{"issue":"3","key":"pcbi.1011964.ref002","first-page":"131","article-title":"Micro-connectomics: probing the organization of neuronal networks at the cellular scale","volume":"18","author":"M Schr\u00f6ter","year":"2017","journal-title":"Nature Reviews Neuroscience 2017 18:3"},{"issue":"7443","key":"pcbi.1011964.ref003","first-page":"96","article-title":"The emergence of functional microcircuits in visual cortex","volume":"496","author":"H Ko","year":"2013","journal-title":"Nature 2013 496:7443"},{"issue":"7599","key":"pcbi.1011964.ref004","first-page":"370","article-title":"Anatomy and function of an excitatory network in the visual cortex","volume":"532","author":"WCA Lee","year":"2016","journal-title":"Nature 2016 532:7599"},{"issue":"11","key":"pcbi.1011964.ref005","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pbio.0020329","article-title":"Serial block-face scanning electron microscopy to reconstruct three-dimensional tissue nanostructure","volume":"2","author":"W Denk","year":"2004","journal-title":"PLoS Biology"},{"issue":"7495","key":"pcbi.1011964.ref006","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1038\/nature13186","article-title":"A mesoscale connectome of the mouse brain","volume":"508","author":"SW Oh","year":"2014","journal-title":"Nature"},{"issue":"6539","key":"pcbi.1011964.ref007","doi-asserted-by":"crossref","DOI":"10.1126\/science.abf4588","article-title":"Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings","volume":"372","author":"NA Steinmetz","year":"2021","journal-title":"Science"},{"issue":"JAN","key":"pcbi.1011964.ref008","first-page":"423","article-title":"Revealing neuronal function through microelectrode array recordings","volume":"9","author":"MEJ Obien","year":"2015","journal-title":"Frontiers in Neuroscience"},{"issue":"1","key":"pcbi.1011964.ref009","first-page":"1","article-title":"Versatile live-cell activity analysis platform for characterization of neuronal dynamics at single-cell and network level","volume":"11","author":"X Yuan","year":"2020","journal-title":"Nature Communications 2020 11:1"},{"key":"pcbi.1011964.ref010","doi-asserted-by":"crossref","unstructured":"Luo L. 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