Universität zu Köln

Senk, Johanna, Kriener, Birgit, Djurfeldt, Mikael, Voges, Nicole, Jiang, Han-Jia ORCID: 0000-0002-9633-2573, Schuettler, Lisa, Gramelsberger, Gabriele, Diesmann, Markus ORCID: 0000-0002-2308-5727, Plesser, Hans E. and van Albada, Sacha J. (2022). Connectivity concepts in neuronal network modeling. PLoS Comput. Biol., 18 (9). SAN FRANCISCO: PUBLIC LIBRARY SCIENCE. ISSN 1553-7358

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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.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Senk, Johanna UNSPECIFIED UNSPECIFIED UNSPECIFIED Kriener, Birgit UNSPECIFIED UNSPECIFIED UNSPECIFIED Djurfeldt, Mikael UNSPECIFIED UNSPECIFIED UNSPECIFIED Voges, Nicole UNSPECIFIED UNSPECIFIED UNSPECIFIED Jiang, Han-Jia UNSPECIFIED orcid.org/0000-0002-9633-2573 UNSPECIFIED Schuettler, Lisa UNSPECIFIED UNSPECIFIED UNSPECIFIED Gramelsberger, Gabriele UNSPECIFIED UNSPECIFIED UNSPECIFIED Diesmann, Markus UNSPECIFIED orcid.org/0000-0002-2308-5727 UNSPECIFIED Plesser, Hans E. UNSPECIFIED UNSPECIFIED UNSPECIFIED van Albada, Sacha J. UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-690673
DOI:	10.1371/journal.pcbi.1010086
Journal or Publication Title:	PLoS Comput. Biol.
Volume:	18
Number:	9
Date:	2022
Publisher:	PUBLIC LIBRARY SCIENCE
Place of Publication:	SAN FRANCISCO
ISSN:	1553-7358
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language NEURAL MODEL; INTRINSIC CONNECTIONS; SYNAPTIC INHIBITION; SIMULATION; MICROCIRCUIT; DISTANCE; SYSTEMS; RECONSTRUCTION; ORGANIZATION; INTEGRATION Multiple languages Biochemical Research Methods; Mathematical & Computational Biology Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/69067