Systematic textual and graphical description of connectivity

Sustainable research on neuronal network models requires published models to be understandable, reproducible, and extendable. Left-out details about mathematical concepts and assumptions, algorithmic implementations, or parameterizations adversely affect progress. Such flaws are unfortunately frequent and one reason is a lack of readily applicable standards and tools for model description [1]. Here, we review models made available by the Computational Neuroscience community in databases like ModelDB [2] and Open Source Brain [3], and investigate the corresponding connectivity structures and their descriptions in both manuscript and code. Based on this review, we derive a set of connectivity concepts in combination with guidelines for a comprehensive, complete, and concise description of network connectivity. In particular, we propose a unified graphical notation for network diagrams to foster an intuitive understanding of network properties (compare [4]). This work also aims to guide the implementation of connection routines in simulation software like NEST [5] and neuromorphic hardware systems.References1. Nordlie E et al. (2009) Towards Reproducible Descriptions of Neuronal Network Models. PLoS Comput Biol. 5(8):e1000456, 10.1371/journal.pcbi.10004562. McDougal R A et al. (2017) Twenty years of ModelDB and beyond: building essential modeling tools for the future of neuroscience. J Comput Neurosci. 42:1-10, 10.1007/s10827-016-0623-73. Gleeson P et al. (2019) Open Source Brain: A Collaborative Resource for Visualizing, Analyzing, Simulating and Developing Standardized Models of Neurons and Circuits. Neuron. 103(3):395-411.e5, 10.1016/j.neuron.2019.05.0194. Le Novère N et al. (2009) The Systems Biology Graphical Notation. Nat Biotechnol. 27(8):735-41, 10.1038/nbt.15585. Gewaltig M-O and Diesmann M (2007). NEST (NEural Simulation Tool). Scholarpedia. 2(4):1430, 10.4249/scholarpedia.1430