Print Email Facebook Twitter EDEN Title EDEN: A High-Performance, General-Purpose, NeuroML-Based Neural Simulator Author Panagiotou, S. (National Technical University of Athens; Erasmus MC) Sidiropoulos, Harry (Erasmus MC) Soudris, Dimitrios (National Technical University of Athens) Negrello, Mario (Erasmus MC) Strydis, C. (TU Delft Computer Engineering; TU Delft Bio-Electronics; Erasmus MC) Date 2022 Abstract Modern neuroscience employs in silico experimentation on ever-increasing and more detailed neural networks. The high modeling detail goes hand in hand with the need for high model reproducibility, reusability and transparency. Besides, the size of the models and the long timescales under study mandate the use of a simulation system with high computational performance, so as to provide an acceptable time to result. In this work, we present EDEN (Extensible Dynamics Engine for Networks), a new general-purpose, NeuroML-based neural simulator that achieves both high model flexibility and high computational performance, through an innovative model-analysis and code-generation technique. The simulator runs NeuroML-v2 models directly, eliminating the need for users to learn yet another simulator-specific, model-specification language. EDEN's functional correctness and computational performance were assessed through NeuroML models available on the NeuroML-DB and Open Source Brain model repositories. In qualitative experiments, the results produced by EDEN were verified against the established NEURON simulator, for a wide range of models. At the same time, computational-performance benchmarks reveal that EDEN runs from one to nearly two orders-of-magnitude faster than NEURON on a typical desktop computer, and does so without additional effort from the user. Finally, and without added user effort, EDEN has been built from scratch to scale seamlessly over multiple CPUs and across computer clusters, when available. Subject computational neurosciencebiological neural networkssimulationHigh-Performance Computingcode morphinginteroperabilityNeuroMLsoftware To reference this document use: http://resolver.tudelft.nl/uuid:8ebb080b-625e-45a1-82c8-938dbe9f3faa DOI https://doi.org/10.3389/fninf.2022.724336 ISSN 1662-5196 Source Frontiers in Neuroinformatics, 16 Part of collection Institutional Repository Document type journal article Rights © 2022 S. Panagiotou, Harry Sidiropoulos, Dimitrios Soudris, Mario Negrello, C. Strydis Files PDF fninf_16_724336.pdf 4 MB Close viewer /islandora/object/uuid:8ebb080b-625e-45a1-82c8-938dbe9f3faa/datastream/OBJ/view