000901916 001__ 901916
000901916 005__ 20240313094935.0
000901916 037__ $$aFZJ-2021-03903
000901916 088__ $$2arXiv$$a2110.02883
000901916 1001_ $$0P:(DE-Juel1)162130$$aSenk, Johanna$$b0$$eCorresponding author
000901916 245__ $$aConnectivity Concepts in Neuronal Network Modeling
000901916 260__ $$c2021
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000901916 3367_ $$2BibTeX$$aARTICLE
000901916 3367_ $$2DataCite$$aOutput Types/Working Paper
000901916 520__ $$aSustainable research on computational models of neuronal networks requires published models to be understandable, reproducible, and extendable. Missing details or ambiguities about mathematical concepts and assumptions, algorithmic implementations, or parameterizations hinder progress. Such flaws are unfortunately frequent and one reason is a lack of readily applicable standards and tools for model description. Our work aims to advance complete and concise descriptions of network connectivity but also to guide the implementation of connection routines in simulation software and neuromorphic hardware systems. We first review models made available by the computational neuroscience community in the repositories ModelDB and Open Source Brain, and investigate the corresponding connectivity structures and their descriptions in both manuscript and code. The review comprises the connectivity of networks with diverse levels of neuroanatomical detail and exposes how connectivity is abstracted in existing description languages and simulator interfaces. We find that a substantial proportion of the published descriptions of connectivity is ambiguous. Based on this review, we derive a set of connectivity concepts for deterministically and probabilistically connected networks and also address networks embedded in metric space. Beside these mathematical and textual guidelines, we propose a unified graphical notation for network diagrams to facilitate an intuitive understanding of network properties. Examples of representative network models demonstrate the practical use of the ideas. We hope that the proposed standardizations will contribute to unambiguous descriptions and reproducible implementations of neuronal network connectivity in computational neuroscience.
000901916 536__ $$0G:(DE-HGF)POF4-5231$$a5231 - Neuroscientific Foundations (POF4-523)$$cPOF4-523$$fPOF IV$$x0
000901916 536__ $$0G:(EU-Grant)720270$$aHBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270)$$c720270$$fH2020-Adhoc-2014-20$$x1
000901916 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x2
000901916 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x3
000901916 536__ $$0G:(EU-Grant)754304$$aDEEP-EST - DEEP - Extreme Scale Technologies (754304)$$c754304$$fH2020-FETHPC-2016$$x4
000901916 536__ $$0G:(GEPRIS)368482240$$aGRK 2416:  MultiSenses-MultiScales: Novel approaches to decipher neural processing in multisensory integration (368482240)$$c368482240$$x5
000901916 536__ $$0G:(DE-HGF)SO-092$$aACA - Advanced Computing Architectures (SO-092)$$cSO-092$$x6
000901916 536__ $$0G:(DE-82)EXS-SF-neuroIC001$$aneuroIC001 - NeuroModelingTalk (NMT) - Approaching the complexity barrier in neuroscientific modeling (EXS-SF-neuroIC001)$$cEXS-SF-neuroIC001$$x7
000901916 536__ $$0G:(DE-Juel1)jinb33_20191101$$aBrain-Scale Simulations (jinb33_20191101)$$cjinb33_20191101$$fBrain-Scale Simulations$$x8
000901916 536__ $$0G:(DE-Juel1)PHD-NO-GRANT-20170405$$aPhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405)$$cPHD-NO-GRANT-20170405$$x9
000901916 7001_ $$0P:(DE-HGF)0$$aKriener, Birgit$$b1
000901916 7001_ $$0P:(DE-HGF)0$$aDjurfeldt, Mikael$$b2
000901916 7001_ $$0P:(DE-Juel1)168479$$aVoges, Nicole$$b3
000901916 7001_ $$0P:(DE-Juel1)176594$$aJiang, Han-Jia$$b4
000901916 7001_ $$0P:(DE-HGF)0$$aSchüttler, Lisa$$b5
000901916 7001_ $$0P:(DE-HGF)0$$aGramelsberger, Gabriele$$b6
000901916 7001_ $$0P:(DE-Juel1)144174$$aDiesmann, Markus$$b7
000901916 7001_ $$0P:(DE-Juel1)169781$$aPlesser, Hans Ekkehard$$b8
000901916 7001_ $$0P:(DE-Juel1)138512$$avan Albada, Sacha$$b9
000901916 8564_ $$uhttps://arxiv.org/abs/2110.02883
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000901916 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5231$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x0
000901916 9141_ $$y2021
000901916 920__ $$lno
000901916 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000901916 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
000901916 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x2
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