TY  - CONF
AU  - Senk, Johanna
TI  - On concepts, correctness, and performance of network simulations
M1  - FZJ-2023-00582
PY  - 2022
N1  - Session: Tools and formats for large scale network modelling
AB  - The development of large-scale neuronal network models is an iterative process and best described by a loop linking the conceptual description with its algorithmic implementation. Models are informed by experimental data and insights from theory; their simulated dynamics needs to be validated against experimentally observed activity. The inherent interdisciplinarity and intricacy of this endeavor make it prone to pitfalls that complicate the comprehensibility and reproducibility of each step. Addressing shortcomings in model descriptions, we review how connectivity is specified in published models and propose unified connectivity concepts and guidelines including a graphical notation for network diagrams. Furthermore, we focus on the challenge to compare simulations of a cortical microcircuit model with respect to correctness and performance across different simulation technologies; literature shows a race for the fastest and most energy efficient simulation. We also showcase a conceptual workflow for performance benchmarking of the simulator NEST together with the benchmarking framework beNNch as a reference implementation. Finally, we introduce NNMT, a toolbox for mean-field based analysis methods of neuronal network models. The presented approaches aim at raising awareness to common difficulties and fostering a sustainable and collaborative modeling culture.
T2  - INCF Neuroinformatics Assembly 2022
CY  - 13 Sep 2022 - 13 Sep 2022, virtual (virtual)
Y2  - 13 Sep 2022 - 13 Sep 2022
M2  - virtual, virtual
LB  - PUB:(DE-HGF)6
UR  - https://juser.fz-juelich.de/record/917354
ER  -