TY  - EJOUR
AU  - Senk, Johanna
AU  - Kriener, Birgit
AU  - Djurfeldt, Mikael
AU  - Voges, Nicole
AU  - Jiang, Han-Jia
AU  - Schüttler, Lisa
AU  - Gramelsberger, Gabriele
AU  - Diesmann, Markus
AU  - Plesser, Hans Ekkehard
AU  - van Albada, Sacha
TI  - Connectivity Concepts in Neuronal Network Modeling
IS  - 2110.02883
M1  - FZJ-2021-03903
M1  - 2110.02883
PY  - 2021
AB  - 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.
LB  - PUB:(DE-HGF)25
UR  - https://juser.fz-juelich.de/record/901916
ER  -