Journal Article

FZJ-2022-02154

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Connectivity concepts in neuronal network modeling

Senk, J. (Corresponding author)FZJ* ; Kriener, B. ; Djurfeldt, M. ; Voges, N. ; Jiang, H.-J.FZJ* ; Schüttler, L. ; Gramelsberger, G. ; Diesmann, M.FZJ* ; Plesser, H. E.FZJ* ; van Albada, S. J.FZJ*

2022

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Please use a persistent id in citations: http://hdl.handle.net/2128/33599  doi:10.1371/journal.pcbi.1010086

Abstract: 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.

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Contributing Institute(s):

1. Computational and Systems Neuroscience (INM-6)
2. Theoretical Neuroscience (IAS-6)
3. Jara-Institut Brain structure-function relationships (INM-10)

Research Program(s):

1. 5234 - Emerging NC Architectures (POF4-523) (POF4-523)
2. 5235 - Digitization of Neuroscience and User-Community Building (POF4-523) (POF4-523)
3. HBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270) (720270)
4. HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907) (785907)
5. HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) (945539)
6. DEEP-EST - DEEP - Extreme Scale Technologies (754304) (754304)
7. GRK 2416 - GRK 2416: MultiSenses-MultiScales: Neue Ansätze zur Aufklärung neuronaler multisensorischer Integration (368482240) (368482240)
8. SPP 2041 347572269 - Integration von Multiskalen-Konnektivität und Gehirnarchitektur in einem supercomputergestützten Modell der menschlichen Großhirnrinde (347572269) (347572269)
9. ACA - Advanced Computing Architectures (SO-092) (SO-092)
10. neuroIC001 - NeuroModelingTalk (NMT) - Approaching the complexity barrier in neuroscientific modeling (EXS-SF-neuroIC001) (EXS-SF-neuroIC001)
11. MetaMoSim - Generic metadata management for reproducible high-performance-computing simulation workflows - MetaMoSim (ZT-I-PF-3-026) (ZT-I-PF-3-026)
12. Open-Access-Publikationskosten Forschungszentrum Jülich (OAPKFZJ) (491111487) (491111487)

Appears in the scientific report 2022

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