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000886165 0247_ $$2doi$$a10.12751/NNCN.BC2020.0263
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000886165 041__ $$aeng
000886165 1001_ $$0P:(DE-Juel1)162130$$aSenk, Johanna$$b0$$eCorresponding author$$ufzj
000886165 1112_ $$aBernstein Conference 2020$$conline$$d2020-09-29 - 2020-10-01$$wonline
000886165 245__ $$aSystematic textual and graphical description of connectivity
000886165 260__ $$c2020
000886165 3367_ $$0PUB:(DE-HGF)1$$2PUB:(DE-HGF)$$aAbstract$$babstract$$mabstract$$s1607075172_15457
000886165 3367_ $$033$$2EndNote$$aConference Paper
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000886165 520__ $$aSustainable research on neuronal network models requires published models to be understandable, reproducible, and extendable. Left-out details about mathematical concepts and assumptions, algorithmic implementations, or parameterizations adversely affect progress. Such flaws are unfortunately frequent and one reason is a lack of readily applicable standards and tools for model description [1]. Here, we review models made available by the Computational Neuroscience community in databases like ModelDB [2] and Open Source Brain [3], and investigate the corresponding connectivity structures and their descriptions in both manuscript and code. Based on this review, we derive a set of connectivity concepts in combination with guidelines for a comprehensive, complete, and concise description of network connectivity. In particular, we propose a unified graphical notation for network diagrams to foster an intuitive understanding of network properties (compare [4]). This work also aims to guide the implementation of connection routines in simulation software like NEST [5] and neuromorphic hardware systems.References1. Nordlie E et al. (2009) Towards Reproducible Descriptions of Neuronal Network Models. PLoS Comput Biol. 5(8):e1000456, 10.1371/journal.pcbi.10004562. McDougal R A et al. (2017) Twenty years of ModelDB and beyond: building essential modeling tools for the future of neuroscience. J Comput Neurosci. 42:1-10, 10.1007/s10827-016-0623-73. Gleeson P et al. (2019) Open Source Brain: A Collaborative Resource for Visualizing, Analyzing, Simulating and Developing Standardized Models of Neurons and Circuits. Neuron. 103(3):395-411.e5, 10.1016/j.neuron.2019.05.0194. Le Novère N et al. (2009) The Systems Biology Graphical Notation. Nat Biotechnol. 27(8):735-41, 10.1038/nbt.15585. Gewaltig M-O and Diesmann M (2007). NEST (NEural Simulation Tool). Scholarpedia. 2(4):1430, 10.4249/scholarpedia.1430
000886165 536__ $$0G:(DE-HGF)POF3-574$$a574 - Theory, modelling and simulation (POF3-574)$$cPOF3-574$$fPOF III$$x0
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000886165 536__ $$0G:(DE-Juel1)aca_20190115$$aAdvanced Computing Architectures (aca_20190115)$$caca_20190115$$fAdvanced Computing Architectures$$x2
000886165 536__ $$0G:(EU-Grant)720270$$aHBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270)$$c720270$$fH2020-Adhoc-2014-20$$x3
000886165 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x4
000886165 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$x5
000886165 536__ $$0G:(EU-Grant)754304$$aDEEP-EST - DEEP - Extreme Scale Technologies (754304)$$c754304$$fH2020-FETHPC-2016$$x6
000886165 536__ $$0G:(Grant)248828_20200305$$aDigiBrain - DL: DigiBrain - From genes to brain function in health and disease (248828_20200305)$$c248828_20200305$$x7
000886165 536__ $$0G:(Grant)250128_20200305$$aCOBRA - COmputing BRAin signals (COBRA): Biophysical computations of electrical and magnetic brain signals (250128_20200305)$$c250128_20200305$$x8
000886165 536__ $$0G:(GEPRIS)347572269$$aSPP 2041 347572269 - Integration von Multiskalen-Konnektivität und Gehirnarchitektur in einem supercomputergestützten Modell der menschlichen Großhirnrinde (347572269)$$c347572269$$x9
000886165 536__ $$0G:(DE-Juel1)jinb33_20191101$$aBrain-Scale Simulations (jinb33_20191101)$$cjinb33_20191101$$fBrain-Scale Simulations$$x10
000886165 536__ $$0G:(GEPRIS)368482240$$aGRK 2416 - GRK 2416: MultiSenses-MultiScales: Neue Ansätze zur Aufklärung neuronaler multisensorischer Integration (368482240)$$c368482240$$x11
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000886165 7001_ $$0P:(DE-HGF)0$$aKriener, Birgit$$b1
000886165 7001_ $$0P:(DE-Juel1)168479$$aVoges, Nicole$$b2
000886165 7001_ $$0P:(DE-HGF)0$$aSchüttler, Lisa$$b3
000886165 7001_ $$0P:(DE-HGF)0$$aGramelsberger, Gabriele$$b4
000886165 7001_ $$0P:(DE-Juel1)169781$$aPlesser, Hans Ekkehard$$b5$$ufzj
000886165 7001_ $$0P:(DE-Juel1)144174$$aDiesmann, Markus$$b6$$ufzj
000886165 7001_ $$0P:(DE-Juel1)138512$$avan Albada, Sacha$$b7$$ufzj
000886165 773__ $$a10.12751/NNCN.BC2020.0263
000886165 8564_ $$uhttps://dx.doi.org/10.12751/nncn.bc2020.0263
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000886165 9141_ $$y2020
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000886165 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000886165 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
000886165 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x2
000886165 980__ $$aabstract
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000886165 980__ $$aI:(DE-Juel1)INM-10-20170113
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