001     865112
005     20210130002915.0
024 7 _ |a 10.1162/netn_e_00108
|2 doi
024 7 _ |a 2128/23258
|2 Handle
024 7 _ |a pmid:31637330
|2 pmid
024 7 _ |a WOS:000489070300001
|2 WOS
037 _ _ |a FZJ-2019-04664
082 _ _ |a 610
100 1 _ |a Peyser, Alexander
|0 P:(DE-Juel1)161525
|b 0
|e Corresponding author
|u fzj
245 _ _ |a Editorial: Linking experimental and computational connectomics
260 _ _ |a Cambridge, MA
|c 2019
|b The MIT Press
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1586173057_15080
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a Large-scale in silico experimentation depends on the generation of connectomes beyond available anatomical structure. We suggest that linking research across the fields of experimental connectomics, theoretical neuroscience, and high-performance computing can enable a new generation of models bridging the gap between biophysical detail and global function. This Focus Feature on ”Linking Experimental and Computational Connectomics” aims to bring together some examples from these domains as a step toward the development of more comprehensive generative models of multiscale connectomes.
536 _ _ |a 511 - Computational Science and Mathematical Methods (POF3-511)
|0 G:(DE-HGF)POF3-511
|c POF3-511
|f POF III
|x 0
536 _ _ |a 574 - Theory, modelling and simulation (POF3-574)
|0 G:(DE-HGF)POF3-574
|c POF3-574
|f POF III
|x 1
536 _ _ |a SMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017)
|0 G:(DE-Juel1)HGF-SMHB-2013-2017
|c HGF-SMHB-2013-2017
|f SMHB
|x 2
536 _ _ |0 G:(DE-Juel1)PHD-NO-GRANT-20170405
|x 3
|c PHD-NO-GRANT-20170405
|a PhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405)
536 _ _ |a SLNS - SimLab Neuroscience (Helmholtz-SLNS)
|0 G:(DE-Juel1)Helmholtz-SLNS
|c Helmholtz-SLNS
|x 4
700 1 _ |a Diaz, Sandra
|0 P:(DE-Juel1)165859
|b 1
|u fzj
700 1 _ |a Klijn, Wouter
|0 P:(DE-Juel1)168169
|b 2
|u fzj
700 1 _ |a Morrison, Abigail
|0 P:(DE-Juel1)151166
|b 3
|u fzj
700 1 _ |a Triesch, Jochen
|0 P:(DE-HGF)0
|b 4
773 _ _ |a 10.1162/netn_e_00108
|0 PERI:(DE-600)2900481-0
|n 4
|p 902-904
|t Network neuroscience
|v 3
|y 2019
|x 2472-1751
856 4 _ |u https://juser.fz-juelich.de/record/865112/files/netn_e_00108.pdf
|y OpenAccess
856 4 _ |u https://juser.fz-juelich.de/record/865112/files/netn_e_00108.pdf?subformat=pdfa
|x pdfa
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:865112
|p openaire
|p open_access
|p VDB
|p driver
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)161525
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 1
|6 P:(DE-Juel1)165859
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)168169
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 3
|6 P:(DE-Juel1)151166
913 1 _ |a DE-HGF
|b Key Technologies
|1 G:(DE-HGF)POF3-510
|0 G:(DE-HGF)POF3-511
|2 G:(DE-HGF)POF3-500
|v Computational Science and Mathematical Methods
|x 0
|4 G:(DE-HGF)POF
|3 G:(DE-HGF)POF3
|l Supercomputing & Big Data
913 1 _ |a DE-HGF
|b Key Technologies
|l Decoding the Human Brain
|1 G:(DE-HGF)POF3-570
|0 G:(DE-HGF)POF3-574
|2 G:(DE-HGF)POF3-500
|v Theory, modelling and simulation
|x 1
|4 G:(DE-HGF)POF
|3 G:(DE-HGF)POF3
914 1 _ |y 2019
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
915 _ _ |a Creative Commons Attribution CC BY 4.0
|0 LIC:(DE-HGF)CCBY4
|2 HGFVOC
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b DOAJ : Peer review
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)JSC-20090406
|k JSC
|l Jülich Supercomputing Center
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)JSC-20090406
980 _ _ |a UNRESTRICTED
980 1 _ |a FullTexts


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21