000907681 001__ 907681
000907681 005__ 20240313103115.0
000907681 0247_ $$2doi$$a10.1371/journal.pcbi.1010086
000907681 0247_ $$2ISSN$$a1553-734X
000907681 0247_ $$2ISSN$$a1553-7358
000907681 0247_ $$2Handle$$a2128/33599
000907681 0247_ $$2WOS$$aWOS:000933363400001
000907681 037__ $$aFZJ-2022-02154
000907681 082__ $$a610
000907681 1001_ $$0P:(DE-Juel1)162130$$aSenk, Johanna$$b0$$eCorresponding author
000907681 245__ $$aConnectivity concepts in neuronal network modeling
000907681 260__ $$c2022
000907681 3367_ $$2DRIVER$$aarticle
000907681 3367_ $$2DataCite$$aOutput Types/Journal article
000907681 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1673846515_28146
000907681 3367_ $$2BibTeX$$aARTICLE
000907681 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000907681 3367_ $$00$$2EndNote$$aJournal Article
000907681 520__ $$aSustainable research on computational models of neuronal networks requires published models to be understandable, reproducible, and extendable. Missing details or ambiguities about mathematical concepts and assumptions, algorithmic implementations, or parameterizations hinder progress. Such flaws are unfortunately frequent and one reason is a lack of readily applicable standards and tools for model description. Our work aims to advance complete and concise descriptions of network connectivity but also to guide the implementation of connection routines in simulation software and neuromorphic hardware systems. We first review models made available by the computational neuroscience community in the repositories ModelDB and Open Source Brain, and investigate the corresponding connectivity structures and their descriptions in both manuscript and code. The review comprises the connectivity of networks with diverse levels of neuroanatomical detail and exposes how connectivity is abstracted in existing description languages and simulator interfaces. We find that a substantial proportion of the published descriptions of connectivity is ambiguous. Based on this review, we derive a set of connectivity concepts for deterministically and probabilistically connected networks and also address networks embedded in metric space. Beside these mathematical and textual guidelines, we propose a unified graphical notation for network diagrams to facilitate an intuitive understanding of network properties. Examples of representative network models demonstrate the practical use of the ideas. We hope that the proposed standardizations will contribute to unambiguous descriptions and reproducible implementations of neuronal network connectivity in computational neuroscience.
000907681 536__ $$0G:(DE-HGF)POF4-5234$$a5234 - Emerging NC Architectures (POF4-523)$$cPOF4-523$$fPOF IV$$x0
000907681 536__ $$0G:(DE-HGF)POF4-5235$$a5235 - Digitization of Neuroscience and User-Community Building (POF4-523)$$cPOF4-523$$fPOF IV$$x1
000907681 536__ $$0G:(EU-Grant)720270$$aHBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270)$$c720270$$fH2020-Adhoc-2014-20$$x2
000907681 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x3
000907681 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x4
000907681 536__ $$0G:(EU-Grant)754304$$aDEEP-EST - DEEP - Extreme Scale Technologies (754304)$$c754304$$fH2020-FETHPC-2016$$x5
000907681 536__ $$0G:(GEPRIS)368482240$$aGRK 2416 - GRK 2416: MultiSenses-MultiScales: Neue Ansätze zur Aufklärung neuronaler multisensorischer Integration (368482240)$$c368482240$$x6
000907681 536__ $$0G:(GEPRIS)347572269$$aSPP 2041 347572269 - Integration von Multiskalen-Konnektivität und Gehirnarchitektur in einem supercomputergestützten Modell der menschlichen Großhirnrinde (347572269)$$c347572269$$x7
000907681 536__ $$0G:(DE-HGF)SO-092$$aACA - Advanced Computing Architectures (SO-092)$$cSO-092$$x8
000907681 536__ $$0G:(DE-82)EXS-SF-neuroIC001$$aneuroIC001 - NeuroModelingTalk (NMT) - Approaching the complexity barrier in neuroscientific modeling (EXS-SF-neuroIC001)$$cEXS-SF-neuroIC001$$x9
000907681 536__ $$0G:(DE-Juel-1)ZT-I-PF-3-026$$aMetaMoSim - Generic metadata management for reproducible high-performance-computing simulation workflows - MetaMoSim (ZT-I-PF-3-026)$$cZT-I-PF-3-026$$x10
000907681 536__ $$0G:(GEPRIS)491111487$$aOpen-Access-Publikationskosten Forschungszentrum Jülich (OAPKFZJ) (491111487)$$c491111487$$x11
000907681 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
000907681 7001_ $$0P:(DE-HGF)0$$aKriener, Birgit$$b1
000907681 7001_ $$0P:(DE-HGF)0$$aDjurfeldt, Mikael$$b2
000907681 7001_ $$0P:(DE-HGF)0$$aVoges, Nicole$$b3
000907681 7001_ $$0P:(DE-Juel1)176594$$aJiang, Han-Jia$$b4
000907681 7001_ $$0P:(DE-HGF)0$$aSchüttler, Lisa$$b5
000907681 7001_ $$0P:(DE-HGF)0$$aGramelsberger, Gabriele$$b6
000907681 7001_ $$0P:(DE-Juel1)144174$$aDiesmann, Markus$$b7
000907681 7001_ $$0P:(DE-Juel1)169781$$aPlesser, Hans E.$$b8
000907681 7001_ $$0P:(DE-Juel1)138512$$avan Albada, Sacha J.$$b9
000907681 773__ $$0PERI:(DE-600)2193340-6$$a10.1371/journal.pcbi.1010086$$gVol. 18, no. 9, p. e1010086 -$$n9$$pe1010086$$tPLoS Computational Biology$$v18$$x1553-734X$$y2022
000907681 8564_ $$uhttps://juser.fz-juelich.de/record/907681/files/Invoice_PAB337183.pdf
000907681 8564_ $$uhttps://juser.fz-juelich.de/record/907681/files/journal.pcbi.1010086.pdf$$yOpenAccess
000907681 8767_ $$8PAB337183$$92022-05-02$$a1200181085$$d2022-05-17$$eAPC$$jZahlung erfolgt$$zUSD 2575,-
000907681 909CO $$ooai:juser.fz-juelich.de:907681$$pdnbdelivery$$popenCost$$pec_fundedresources$$pVDB$$pdriver$$pOpenAPC$$popen_access$$popenaire
000907681 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)162130$$aForschungszentrum Jülich$$b0$$kFZJ
000907681 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)176594$$aForschungszentrum Jülich$$b4$$kFZJ
000907681 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144174$$aForschungszentrum Jülich$$b7$$kFZJ
000907681 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)169781$$aForschungszentrum Jülich$$b8$$kFZJ
000907681 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)138512$$aForschungszentrum Jülich$$b9$$kFZJ
000907681 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5234$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x0
000907681 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5235$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x1
000907681 9141_ $$y2022
000907681 915pc $$0PC:(DE-HGF)0001$$2APC$$aLocal Funding
000907681 915pc $$0PC:(DE-HGF)0002$$2APC$$aDFG OA Publikationskosten
000907681 915pc $$0PC:(DE-HGF)0003$$2APC$$aDOAJ Journal
000907681 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
000907681 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2022-11-18
000907681 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2022-11-18
000907681 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews$$d2022-11-18
000907681 915__ $$0StatID:(DE-HGF)1190$$2StatID$$aDBCoverage$$bBiological Abstracts$$d2022-11-18
000907681 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2022-11-18
000907681 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bPLOS COMPUT BIOL : 2021$$d2022-11-18
000907681 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2022-04-12T10:24:26Z
000907681 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2022-04-12T10:24:26Z
000907681 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2022-11-18
000907681 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2022-11-18
000907681 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2022-11-18
000907681 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5$$d2022-11-18
000907681 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000907681 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2022-11-18
000907681 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2022-11-18
000907681 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2022-11-18
000907681 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2022-11-18
000907681 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Peer review$$d2022-04-12T10:24:26Z
000907681 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000907681 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
000907681 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x2
000907681 9801_ $$aAPC
000907681 9801_ $$aFullTexts
000907681 980__ $$ajournal
000907681 980__ $$aVDB
000907681 980__ $$aUNRESTRICTED
000907681 980__ $$aI:(DE-Juel1)INM-6-20090406
000907681 980__ $$aI:(DE-Juel1)IAS-6-20130828
000907681 980__ $$aI:(DE-Juel1)INM-10-20170113
000907681 980__ $$aAPC
000907681 981__ $$aI:(DE-Juel1)IAS-6-20130828