PyNN: A Common Interface for Neuronal Network Simulators.
PyNN: A Common Interface for Neuronal Network Simulators.
复制标题
DOI：
10.3389/neuro.11.011.2008
复制
发表时间：
2008
影响因子：
3.5
通讯作者：
Yger P
中科院分区：
文献类型：
--
作者：
Davison AP;Brüderle D;Eppler J;Kremkow J;Muller E;Pecevski D;Perrinet L;Yger P
计算神经科学已经开发出了多种用于模拟尖峰神经元网络的软件，这些软件产生了积极和消极的后果。一方面，每个模拟器都使用自己的编程或配置语言，导致将模型从一个模拟器移植到另一个模拟器相当困难。这阻碍了调查人员之间的沟通，并且使得复制和借鉴他人的工作变得更加困难。另一方面，可以在不同模拟器之间交叉检查模拟结果，从而对其正确性给予更大的信心，并且每个模拟器都有不同的优化，因此可以为给定的建模任务选择最合适的模拟器。多个模拟器的通用编程接口将减少或消除模拟器多样性的问题，同时保留其优点。 PyNN 就是这样一个接口，使得使用 Python 编程语言编写一次模拟脚本成为可能，并且无需修改即可在任何支持的模拟器（目前为 NEURON、NEST、PCSIM、Brian 和 Heidelberg VLSI 神经拟态硬件）上运行它。 PyNN 通过提供高级抽象、促进代码共享和重用以及为与模拟器无关的分析、可视化和数据管理工具提供基础来提高神经网络建模的生产力。 PyNN 使在多个模拟器上检查结果变得更加容易，从而提高了建模研究的可靠性。 PyNN 是开源软件，可从 .
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 by providing 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 .