PyNN: a common interface for neuronal network simulators

Frontiers in Neuroinformatics

Laurent Perrinet²  Andrew P Davison²  Pierre Yger²  Eilif Muller³  Jochen M Eppler⁴  Jens Kremkow⁵  Dejan Pecevski⁶  Daniel Brüderle⁷ 

[1] Albert-Ludwigs-University;CNRS;Ecole Polytechnique Fédérale de Lausanne;Honda Research Institute, GmbH;Neurobiology and Biophysics,Bernstein Center for Computational Neuroscience,Albert-Ludwigs-University;Technische Universität Graz;University of Heidelberg;

关键词: simulation;    computational neuroscience;    interoperability;    large-scale models;    Parallel Computing;    python;   

DOI  :  10.3389/neuro.11.011.2008

来源: DOAJ

【 摘 要 】

Computational neuroscience has produced a diversity of software for simulations of networks of spiking neurons, with both negative and positive consequences. On the one hand, each simulator uses its own programming or configuration language, leading to considerable difficulty in porting models from one simulator to another. This impedes communication between investigators and makes it harder to reproduce and build on the work of others.On the other hand, simulation results can be cross-checked between different simulators, giving greater confidence in their correctness, and each simulator has different optimizations, so the most appropriate simulator can be chosen for a given modelling task. A common programming interface to multiple simulators would reduce or eliminate the problems of simulator diversity while retaining the benefits. PyNN is such an interface, making it possible to write a simulation script once, using the Python programming language, and run it without modification on any supported simulator (currently NEURON, NEST, PCSIM, Brian and the Heidelberg VLSI neuromorphic hardware). PyNN increases the productivity of neuronal network modelling by providing high-level abstraction, by promoting code sharing and reuse, and byproviding a foundation for simulator-agnostic analysis, visualization, and data-management tools. PyNN increases the reliability of modelling studies by making it much easier to check results on multiple simulators. PyNN is open-source software and is available from http://neuralensemble.org/PyNN.

【 授权许可】

Unknown