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| 001 | 863678 | ||
| 005 | 20240313094920.0 | ||
| 037 | _ | _ | |a FZJ-2019-03684 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Senk, Johanna |0 P:(DE-Juel1)162130 |b 0 |e Corresponding author |
| 111 | 2 | _ | |a NEST Conference 2019 |c Aas |d 2019-06-24 - 2019-06-25 |w Norway |
| 245 | _ | _ | |a Connectivity Concepts for Neuronal Networks |
| 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 1569849661_21077 |2 PUB:(DE-HGF) |x After Call |
| 520 | _ | _ | |a A statement like “$N_\text{s}$ source neurons and $N_\text{t}$ target neurons are connected randomly with connectionprobability $p$” may be used to describe the structure of a neuronal network model, but itsinterpretation is inherently ambiguous. A lacking detail is, for example, information on thedistribution of in- and outgoing connections, resulting in substantial differences in networkdynamics. For reproducible research, unambiguous network descriptions and correspondingalgorithmic implementations are necessary [1]. Here, we review simulation software (e.g., NEST[2]), specification languages (e.g., CSA [3]), and published network models made available by thecommunity in databases like ModelDB [4] and Open Source Brain [5]. We investigate the networkstructures computational neuroscientists use in their models and the terminology they use todescribe these models. From this, we derive a set of connectivity concepts providing modelers withguidelines to specify connectivity in a complete and concise way. Furthermore, this work aims toguide the comprehensive and efficient implementation of connection routines in simulationsoftware like NEST, thereby facilitating reproducible research on network models.$\qquad$References: $\qquad$1. Nordlie E, et al. (2009) Towards Reproducible Descriptions of Neuronal Network Models. PLoS Comput Biol. 5(8): e1000456. doi:10.1371/journal.pcbi.1000456 $\qquad$2. Gewaltig M-O and Diesmann M (2007). NEST (NEural Simulation Tool). Scholarpedia. 2(4):1430, doi:10.4249/scholarpedia.1430 $\qquad$3. Djurfeldt M (2012) The Connection-set Algebra—A Novel Formalism for the Representation of Connectivity Structure in Neuronal Network Models. Neuroinform. 10:287–304. doi:10.1007/s12021-012-9146-1 $\qquad$4. ModelDB [https://senselab.med.yale.edu/modeldb] $\qquad$5. Gleeson P, Cantarelli M, Marin B, . . . van Albada SJ, van Geit W, R Silver RA (in press) Open Source Brain: a collaborative resource for visualizing, analyzing, simulating and developing standardized models of neurons and circuits. Neuron. |
| 536 | _ | _ | |a 571 - Connectivity and Activity (POF3-571) |0 G:(DE-HGF)POF3-571 |c POF3-571 |x 0 |f POF III |
| 536 | _ | _ | |a HBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270) |0 G:(EU-Grant)720270 |c 720270 |x 1 |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 2 |f H2020-SGA-FETFLAG-HBP-2017 |
| 536 | _ | _ | |a Brain-Scale Simulations (jinb33_20121101) |0 G:(DE-Juel1)jinb33_20121101 |c jinb33_20121101 |x 3 |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 4 |
| 536 | _ | _ | |a Advanced Computing Architectures (aca_20190115) |0 G:(DE-Juel1)aca_20190115 |c aca_20190115 |x 5 |f Advanced Computing Architectures |
| 700 | 1 | _ | |a Kriener, Birgit |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Hagen, Espen |0 P:(DE-Juel1)164166 |b 2 |
| 700 | 1 | _ | |a Bos, Hannah |0 P:(DE-Juel1)162131 |b 3 |
| 700 | 1 | _ | |a Plesser, Hans Ekkehard |0 P:(DE-Juel1)169781 |b 4 |u fzj |
| 700 | 1 | _ | |a Gewaltig, Marc-Oliver |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Diesmann, Markus |0 P:(DE-Juel1)144174 |b 6 |u fzj |
| 700 | 1 | _ | |a Djurfeldt, Mikael |0 P:(DE-HGF)0 |b 7 |
| 700 | 1 | _ | |a Voges, Nicole |0 P:(DE-Juel1)168479 |b 8 |u fzj |
| 700 | 1 | _ | |a van Albada, Sacha |0 P:(DE-Juel1)138512 |b 9 |u fzj |
| 856 | 4 | _ | |u https://indico-jsc.fz-juelich.de/event/92/ |
| 909 | C | O | |o oai:juser.fz-juelich.de:863678 |p openaire |p VDB |p ec_fundedresources |
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| 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 |
| 914 | 1 | _ | |y 2019 |
| 920 | _ | _ | |l no |
| 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 |
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