001     902980
005     20250324202209.0
024 7 _ |a 10.1126/science.abl8519
|2 doi
024 7 _ |a 0036-8075
|2 ISSN
024 7 _ |a 1095-9203
|2 ISSN
024 7 _ |a 1947-8062
|2 ISSN
024 7 _ |a 2128/29277
|2 Handle
024 7 _ |a altmetric:117635220
|2 altmetric
024 7 _ |a pmid:34822267
|2 pmid
024 7 _ |a WOS:000725668600021
|2 WOS
037 _ _ |a FZJ-2021-04726
082 _ _ |a 500
100 1 _ |a Amunts, Katrin
|0 P:(DE-Juel1)131631
|b 0
|e Corresponding author
|u fzj
245 _ _ |a Brain research challenges supercomputing
260 _ _ |a Cambridge, Mass.
|c 2021
|b Moses King
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 1742804611_20597
|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 The adult human brain contains ∼86 billion neurons (1). Zooming into its cellular and subcellular details to reveal different aspects of neuronal connectivity is a key area of research. However, to link the different spatial scales from the synaptic level (at nanometer range) through single neurons and glial cells (at the micrometer level) to the whole organ is most challenging. Recently, the connectome of Caenorhabditis elegans, with its 302 neurons, has been characterized, and a complete structural-functional model has been proposed (2). A comparable level of detail of the human brain connectome is still a long way off. As such, decoding the human connectome, the mechanisms of signal transduction, and relationships to brain function are linked to exponentially growing challenges in advanced computational and storage technologies, which in turn may lead to creative solutions beyond neuroscience.
536 _ _ |a 5251 - Multilevel Brain Organization and Variability (POF4-525)
|0 G:(DE-HGF)POF4-5251
|c POF4-525
|f POF IV
|x 0
536 _ _ |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)
|0 G:(EU-Grant)945539
|c 945539
|f H2020-SGA-FETFLAG-HBP-2019
|x 1
536 _ _ |a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)
|0 G:(DE-HGF)POF4-5111
|c POF4-511
|f POF IV
|x 2
588 _ _ |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de
700 1 _ |a Lippert, Thomas
|0 P:(DE-Juel1)132179
|b 1
|u fzj
773 _ _ |a 10.1126/science.abl8519
|g Vol. 374, no. 6571, p. 1054 - 1055
|0 PERI:(DE-600)2066996-3
|n 6571
|p 1054 - 1055
|t Science
|v 374
|y 2021
|x 0036-8075
856 4 _ |u https://www.science.org/stoken/author-tokens/ST-192/full
856 4 _ |u https://juser.fz-juelich.de/record/902980/files/science.abl8519.pdf
|y Restricted
856 4 _ |u https://juser.fz-juelich.de/record/902980/files/1126Amunts_DRAFT%20KA.pdf
|y OpenAccess
|z StatID:(DE-HGF)0510
909 C O |o oai:juser.fz-juelich.de:902980
|p openaire
|p open_access
|p driver
|p VDB
|p ec_fundedresources
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)131631
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 1
|6 P:(DE-Juel1)132179
913 1 _ |a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
|1 G:(DE-HGF)POF4-520
|0 G:(DE-HGF)POF4-525
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Decoding Brain Organization and Dysfunction
|9 G:(DE-HGF)POF4-5251
|x 0
913 1 _ |a DE-HGF
|b Key Technologies
|l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action
|1 G:(DE-HGF)POF4-510
|0 G:(DE-HGF)POF4-511
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Enabling Computational- & Data-Intensive Science and Engineering
|9 G:(DE-HGF)POF4-5111
|x 1
914 1 _ |y 2021
915 _ _ |a IF >= 40
|0 StatID:(DE-HGF)9940
|2 StatID
|b SCIENCE : 2019
|d 2021-02-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2021-02-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2021-02-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
|d 2021-02-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1190
|2 StatID
|b Biological Abstracts
|d 2021-02-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2021-02-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1040
|2 StatID
|b Zoological Record
|d 2021-02-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1200
|2 StatID
|b Chemical Reactions
|d 2021-02-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1060
|2 StatID
|b Current Contents - Agriculture, Biology and Environmental Sciences
|d 2021-02-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1030
|2 StatID
|b Current Contents - Life Sciences
|d 2021-02-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1210
|2 StatID
|b Index Chemicus
|d 2021-02-02
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2021-02-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2021-02-02
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2021-02-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1150
|2 StatID
|b Current Contents - Physical, Chemical and Earth Sciences
|d 2021-02-02
915 _ _ |a National-Konsortium
|0 StatID:(DE-HGF)0430
|2 StatID
|d 2021-02-02
|w ger
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b SCIENCE : 2019
|d 2021-02-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2021-02-02
915 _ _ |a Nationallizenz
|0 StatID:(DE-HGF)0420
|2 StatID
|d 2021-02-02
|w ger
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2021-02-02
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)INM-1-20090406
|k INM-1
|l Strukturelle und funktionelle Organisation des Gehirns
|x 0
920 1 _ |0 I:(DE-Juel1)JSC-20090406
|k JSC
|l Jülich Supercomputing Center
|x 1
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)INM-1-20090406
980 _ _ |a I:(DE-Juel1)JSC-20090406
980 _ _ |a UNRESTRICTED
980 1 _ |a FullTexts


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21