EDEN: A High-Performance, General-Purpose, NeuroML-Based Neural Simulator

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.

The NeuroML ecosystem for standardized multi-scale modeling in neuroscience

“…The simulation platforms (e.g. EDEN ( Panagiotou et al, 2022 ), NEURON ( Hines and Carnevale, 1997 )), along with other independently developed tools, form the next layer of the software ecosystem—providing extra functionality such as interactive model construction (e.g. neuroConstruct ( Gleeson et al, 2007 ), NetPyNE ( Dura-Bernal et al, 2019 )), additional visualization (e.g.…”

“…The second category deals with simulators which use NeuroML natively as their base descriptions of modeling entities, as opposed to having their own specific formats. The EDEN simulator is one such recently developed example, which was designed from its inception to read NeuroML and LEMS models for efficient, parallel simulation ( Panagiotou et al, 2022 ).…”

“…13 and 14 is simulated directly in jNeuroML. jNeuroML can also pass simulations to the EDEN simulator ( Panagiotou et al, 2022 ) for direct simulation. For simulators that require the conversion of NeuroML/LEMS simulations into either scripts which allow importing by the simulator’s own methods (e.g.…”

“…To provide clear, concise, searchable information for both users and developers, the NeuroML documentation has been significantly expanded and re-deployed using the latest modern web technologies 1 . Increased community-wide collaborations have also extended the software ecosystem well beyond the NeuroMLv2 tools developed by the core NeuroML team: additional simulators such as Brian ( Stimberg et al, 2019 ), NetPyNE ( Dura-Bernal et al, 2019 ), Arbor ( Abi Akar et al, 2019 ) and EDEN ( Panagiotou et al, 2022 ) all support NeuroMLv2. We have worked to ensure interoperability with other structured formats for model development in neuroscience such as PyNN ( Davison et al, 2009 ) and SONATA ( Dai et al, 2020 ).…”

“…modeling entities, as opposed to having their own specific formats. The EDEN simulator is one such recently developed example, which was designed from its inception to read NeuroML and LEMS models for efficient, parallel simulation(Panagiotou et al, 2022).The third category involves simulators which have their own internal formats, and include methods to import NeuroMLv2/LEMS and in this way translate the models to their own formats. Examples include NetPyNE (Dura-Bernal et al, 2019), MOOSE (Ray and Bhalla, 2008), N2A (Rothganger et al, 2014).The fourth category comprises simulators for which scripts can be generated by pyNeuroML.…”