Abstract
NEST is a simulator for large-scale networks of spiking point neuron models (Gewaltig and Diesmann, 2007). Originally, simulations were controlled via the Simulation Language Interpreter (SLI), a built-in scripting facility implementing a language derived from PostScript (Adobe Systems, Inc., 1999). The introduction of PyNEST (Eppler et al., 2008), the Python interface for NEST, enabled users to control simulations using Python. As the majority of NEST users found PyNEST easier to use and to combine with other applications, it immediately displaced SLI as the default NEST interface. However, developing and maintaining PyNEST has become increasingly difficult over time. This is partly because adding new features requires writing low-level C++ code intermixed with calls to the Python/C API, which is unrewarding. Moreover, the Python/C API evolves with each new version of Python, which results in a proliferation of version-dependent code branches. In this contribution we present the re-implementation of PyNEST in the Cython language, a superset of Python that additionally supports the declaration of C/C++ types for variables and class attributes, and provides a convenient foreign function interface (FFI) for invoking C/C++ routines (Behnel et al., 2011). Code generation via Cython allows the production of smaller and more maintainable bindings, including increased compatibility with all supported Python releases without additional burden for NEST developers. Furthermore, this novel approach opens up the possibility to support alternative implementations of the Python language at no cost given a functional Cython back-end for the corresponding implementation, and also enables cross-compilation of Python bindings for embedded systems and supercomputers alike.
Highlights
Several projects in simulation have established themselves in the domain of neuroscience as long-term providers of tools aiming to supply the community with the simulation technology that users can rely upon for a particular level of modeling
The Python/C API evolves with each new version of Python, which results in a proliferation of version-dependent code branches. In this contribution we present the re-implementation of PyNEST in the Cython language, a superset of Python that supports the declaration of C/C++ types for variables and class attributes, and provides a convenient foreign function interface (FFI) for invoking C/C++ routines (Behnel et al, 2011)
The affected files contained the definitions of Simulation Language Interpreter (SLI) data types, and previously had to be modified to add calls to the converter class used to coerce them to the appropriate Python data types
Summary
Several projects in simulation have established themselves in the domain of neuroscience as long-term providers of tools aiming to supply the community with the simulation technology that users can rely upon for a particular level of modeling. These include STEPS (Hepburn et al, 2012) for stochastic simulation of reaction-diffusion systems in three dimensions, NEURON (Carnevale and Hines, 2006) for empirically-based simulations of neurons and networks of neurons, and NEST (Gewaltig and Diesmann, 2007) for large-scale networks of spiking point neuron models This process of establishement occurs partially as a result of their respective maintainers’ consistent efforts to ensure the quality and the sustainability of these software packages, and partially by virtue of the fact that as the projects reach a critical level of usage in the community, it creates a positive feedback loop which reinforces their acceptance. A software tool that can be maintained with ease is preferable to an otherwise excellent, but completely unmaintainable software package
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
