Journal article Open Access

EDEN: A high-performance, general-purpose, NeuroML-based neural simulator

Sotirios Panagiotou; Harry Sidiropoulos; Mario Negrello; Dimitrios Soudris; Christos Strydis

Modern neuroscience employs in silico experimentation on ever-increasing and more detailed
neural networks. The high modelling 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 up to 2 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.

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