( 2012) and Gewaltig and Cannon ( 2014) further illustrate how productive use of a software application can lead to development and use beyond its original scope. In practice, however, it remains difficult to always have the right training, resources and overall understanding to develop good software and use it correctly. The need for better software sustainability, correctness and reproducibility (McDougal et al., 2016 Mulugeta et al., 2018) has prompted initiatives and proposals suggesting better practices when developing scientific software (Crouch et al., 2013 Erdemir et al., 2020) and when publishing computational results (Heroux, 2015 Willenbring, 2015). Unfortunately, this reliance on software also has inherent and increasing risks (Miller, 2006). ( 2014) found that there is a general trend that science relies more and more on software with the capability to automate complex processes and perform quantitative calculations for prediction and analysis. The increasing importance of software in science is, however, not specific to neuroscience. ( 2019) have argued that the central role of simulation software in neuroscience is analogous to physical infrastructure in other scientific domains, such as astronomical observatories and particle accelerators, and that the resources required to build and maintain software should be considered in this context. ![]() Today it is one of the most widely used simulation environments for biologically detailed neurosimulations (Tikidji-Hamburyan et al., 2017).Įinevoll et al. Subsequently it underwent massive enhancements in features and performance and it is now used for models that range in scale from subcellular (McDougal et al., 2013) to large networks (Migliore et al., 2006). Its development started in the laboratory of John Moore at Duke University in the mid-1980s as a tool for studying spike initiation and propagation in squid axons. NEURON is an open-source simulation environment that is particularly well suited for models of individual neurons and networks of neurons in which biophysical and anatomical complexity have important functional roles (Hines and Carnevale, 1997). 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. Similarly, we have been able to accelerate NEURON's reaction-diffusion simulation performance through the use of just-in-time compilation. 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. 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. ![]() ![]() 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. 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. 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.
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