Modernizing the NEURON Simulator for Sustainability, Portability, and Performance

Abstract

The need for reproducible, credible, multiscale biological modeling has led to the development of standardized simulation platforms, such as the widely-used NEURON environment for computational neuroscience. Developing and maintaining NEURON over several decades has required attention to the competing needs of backwards compatibility, evolving computer architectures, the addition of new scales and physical processes, accessibility to new users, and efficiency and flexibility for specialists. In order to meet these challenges, we have now substantially modernized NEURON, providing continuous integration, an improved build system and release workflow, and better documentation. With the help of a new source-to-source compiler of the NMODL domain-specific language we have enhanced NEURON’s ability to run efficiently, via the CoreNEURON simulation engine, on a variety of hardware platforms, including GPUs. Through the implementation of an optimized in-memory transfer mechanism this performance optimized backend is made easily accessible to users, providing training and model-development paths from laptop to workstation to supercomputer and cloud platform. Similarly, we have been able to accelerate NEURON’s reaction-diffusion simulation performance through the use of just-in-time compilation. We show that these efforts have led to a growing developer base, a simpler and more robust software distribution, a wider range of supported computer architectures, a better integration of NEURON with other scientific workflows, and substantially improved performance for the simulation of biophysical and biochemical models.

Deploying and Optimizing Embodied Simulations of Large-Scale Spiking Neural Networks on HPC Infrastructure

“…They allow to model a high degree of biological plausibility but differ in their focus on different aspects of the biological models or the technology they use. Highlighting a few, NEURON (Hines and Carnevale, 1997 ; Awile et al, 2022 ) GENESIS (Bower and Beeman, 2007 ) and Arbor (Abi Akar et al, 2019 ) allow the modeling of complex compartmental neurons and are tailored to the simulation from the sub-cellular level to networks, while the Open Source Brain (Gleeson et al, 2019 ) provides functionalities for visualization and focuses on user collaboration and accessibility of neuron models and networks.…”