  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.
