001     864816
005     20240313094933.0
037 _ _ |a FZJ-2019-04472
100 1 _ |a Pronold, Jari
|0 P:(DE-Juel1)165321
|b 0
|e Corresponding author
|u fzj
111 2 _ |a NEST Conference
|c Ås
|d 2019-06-24 - 2019-06-25
|w Norway
245 _ _ |a Multi-area spiking network models of macaque and humancortices
260 _ _ |c 2019
336 7 _ |a Conference Paper
|0 33
|2 EndNote
336 7 _ |a INPROCEEDINGS
|2 BibTeX
336 7 _ |a conferenceObject
|2 DRIVER
336 7 _ |a CONFERENCE_POSTER
|2 ORCID
336 7 _ |a Output Types/Conference Poster
|2 DataCite
336 7 _ |a Poster
|b poster
|m poster
|0 PUB:(DE-HGF)24
|s 1567761503_28189
|2 PUB:(DE-HGF)
|x After Call
520 _ _ |a Understanding the wiring of the brain at the micro-, meso- and macroscale and its influence onneuronal activity is a fundamental problem in neuroscience. Here we present a multi-scale spikingnetwork model of all vision related areas of macaque cortex [1] using the NEST simulator andoutline how we aim to simulate human visual cortex.The connectivity map in our model of the macaque visual cortex integrates data on corticalarchitecture and axonal tracing data into a consistent multi-scale framework and predicts theconnection probability between any two neurons based on their types and locations within areas andlayers [1]. Simulations using this connectivity map reveal a stable asynchronous irregular groundstate with heterogeneous activity across areas, layers and populations [2]. The model of humanvisual cortex will make use of this framework, replacing neuron densities, laminar thicknesses, andcortico-cortical connectivity by estimates for the human brain. To set up the framework, we willfirst model a full cortical hemisphere using published data on cortical architecture [3]. Human-macaque homologies and DTI data will provide reference values for comparison of the cortico-cortical connectivity map. These models will help to elucidate how detailed connectivity of cortexshapes its dynamics on multiple scales and how prominent features of cortical activity can beexplained by population-level connectivity.
536 _ _ |a 571 - Connectivity and Activity (POF3-571)
|0 G:(DE-HGF)POF3-571
|c POF3-571
|x 0
|f POF III
536 _ _ |a 574 - Theory, modelling and simulation (POF3-574)
|0 G:(DE-HGF)POF3-574
|c POF3-574
|x 1
|f POF III
536 _ _ |a HBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270)
|0 G:(EU-Grant)720270
|c 720270
|x 2
|f H2020-Adhoc-2014-20
536 _ _ |a HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)
|0 G:(EU-Grant)785907
|c 785907
|x 3
|f H2020-SGA-FETFLAG-HBP-2017
536 _ _ |a HBP - The Human Brain Project (604102)
|0 G:(EU-Grant)604102
|c 604102
|x 4
|f FP7-ICT-2013-FET-F
536 _ _ |a Brain-Scale Simulations (jinb33_20121101)
|0 G:(DE-Juel1)jinb33_20121101
|c jinb33_20121101
|x 5
|f Brain-Scale Simulations
536 _ _ |a SPP 2041 347572269 - Integration von Multiskalen-Konnektivität und Gehirnarchitektur in einem supercomputergestützten Modell der menschlichen Großhirnrinde (347572269)
|0 G:(GEPRIS)347572269
|c 347572269
|x 6
536 _ _ |a Advanced Computing Architectures (aca_20190115)
|0 G:(DE-Juel1)aca_20190115
|c aca_20190115
|x 7
|f Advanced Computing Architectures
536 _ _ |0 G:(DE-Juel1)PHD-NO-GRANT-20170405
|x 8
|c PHD-NO-GRANT-20170405
|a PhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405)
700 1 _ |a van Meegen, Alexander
|0 P:(DE-Juel1)173607
|b 1
|u fzj
700 1 _ |a Bakker, Rembrandt
|0 P:(DE-Juel1)145578
|b 2
|u fzj
700 1 _ |a Morales-Gregorio, Aitor
|0 P:(DE-Juel1)176593
|b 3
|u fzj
700 1 _ |a van Albada, Sacha
|0 P:(DE-Juel1)138512
|b 4
|u fzj
909 C O |o oai:juser.fz-juelich.de:864816
|p openaire
|p VDB
|p ec_fundedresources
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)165321
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 1
|6 P:(DE-Juel1)173607
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)145578
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 3
|6 P:(DE-Juel1)176593
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 4
|6 P:(DE-Juel1)138512
913 1 _ |a DE-HGF
|b Key Technologies
|l Decoding the Human Brain
|1 G:(DE-HGF)POF3-570
|0 G:(DE-HGF)POF3-571
|2 G:(DE-HGF)POF3-500
|v Connectivity and Activity
|x 0
|4 G:(DE-HGF)POF
|3 G:(DE-HGF)POF3
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
920 1 _ |0 I:(DE-Juel1)INM-6-20090406
|k INM-6
|l Computational and Systems Neuroscience
|x 0
920 1 _ |0 I:(DE-Juel1)IAS-6-20130828
|k IAS-6
|l Theoretical Neuroscience
|x 1
920 1 _ |0 I:(DE-Juel1)INM-10-20170113
|k INM-10
|l Jara-Institut Brain structure-function relationships
|x 2
980 _ _ |a poster
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)INM-6-20090406
980 _ _ |a I:(DE-Juel1)IAS-6-20130828
980 _ _ |a I:(DE-Juel1)INM-10-20170113
980 _ _ |a UNRESTRICTED
981 _ _ |a I:(DE-Juel1)IAS-6-20130828


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21