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| Hauptseite > Publikationsdatenbank > Connectivity Concepts in Neuronal Network Modeling > print |
| 001 | 901916 | ||
| 005 | 20240313094935.0 | ||
| 037 | _ | _ | |a FZJ-2021-03903 |
| 088 | _ | _ | |a 2110.02883 |2 arXiv |
| 100 | 1 | _ | |a Senk, Johanna |0 P:(DE-Juel1)162130 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Connectivity Concepts in Neuronal Network Modeling |
| 260 | _ | _ | |c 2021 |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1636350882_21912 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a WORKING_PAPER |2 ORCID |
| 336 | 7 | _ | |a Electronic Article |0 28 |2 EndNote |
| 336 | 7 | _ | |a preprint |2 DRIVER |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a Output Types/Working Paper |2 DataCite |
| 520 | _ | _ | |a Sustainable 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. |
| 536 | _ | _ | |a 5231 - Neuroscientific Foundations (POF4-523) |0 G:(DE-HGF)POF4-5231 |c POF4-523 |f POF IV |x 0 |
| 536 | _ | _ | |a HBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270) |0 G:(EU-Grant)720270 |c 720270 |f H2020-Adhoc-2014-20 |x 1 |
| 536 | _ | _ | |a HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907) |0 G:(EU-Grant)785907 |c 785907 |f H2020-SGA-FETFLAG-HBP-2017 |x 2 |
| 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 3 |
| 536 | _ | _ | |a DEEP-EST - DEEP - Extreme Scale Technologies (754304) |0 G:(EU-Grant)754304 |c 754304 |f H2020-FETHPC-2016 |x 4 |
| 536 | _ | _ | |a GRK 2416: MultiSenses-MultiScales: Novel approaches to decipher neural processing in multisensory integration (368482240) |0 G:(GEPRIS)368482240 |c 368482240 |x 5 |
| 536 | _ | _ | |a ACA - Advanced Computing Architectures (SO-092) |0 G:(DE-HGF)SO-092 |c SO-092 |x 6 |
| 536 | _ | _ | |a neuroIC001 - NeuroModelingTalk (NMT) - Approaching the complexity barrier in neuroscientific modeling (EXS-SF-neuroIC001) |0 G:(DE-82)EXS-SF-neuroIC001 |c EXS-SF-neuroIC001 |x 7 |
| 536 | _ | _ | |a Brain-Scale Simulations (jinb33_20191101) |0 G:(DE-Juel1)jinb33_20191101 |c jinb33_20191101 |f Brain-Scale Simulations |x 8 |
| 536 | _ | _ | |a PhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405) |0 G:(DE-Juel1)PHD-NO-GRANT-20170405 |c PHD-NO-GRANT-20170405 |x 9 |
| 700 | 1 | _ | |a Kriener, Birgit |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Djurfeldt, Mikael |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Voges, Nicole |0 P:(DE-Juel1)168479 |b 3 |
| 700 | 1 | _ | |a Jiang, Han-Jia |0 P:(DE-Juel1)176594 |b 4 |
| 700 | 1 | _ | |a Schüttler, Lisa |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Gramelsberger, Gabriele |0 P:(DE-HGF)0 |b 6 |
| 700 | 1 | _ | |a Diesmann, Markus |0 P:(DE-Juel1)144174 |b 7 |
| 700 | 1 | _ | |a Plesser, Hans Ekkehard |0 P:(DE-Juel1)169781 |b 8 |
| 700 | 1 | _ | |a van Albada, Sacha |0 P:(DE-Juel1)138512 |b 9 |
| 856 | 4 | _ | |u https://arxiv.org/abs/2110.02883 |
| 909 | C | O | |o oai:juser.fz-juelich.de:901916 |p openaire |p VDB |p ec_fundedresources |
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| 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)POF4-523 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Neuromorphic Computing and Network Dynamics |9 G:(DE-HGF)POF4-5231 |x 0 |
| 914 | 1 | _ | |y 2021 |
| 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 |
| 980 | _ | _ | |a preprint |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)INM-6-20090406 |
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| 980 | _ | _ | |a I:(DE-Juel1)INM-10-20170113 |
| 980 | _ | _ | |a UNRESTRICTED |
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