FAIRsharing record for: PyNN

This FAIRsharing record describes: The PyNN API aims to support modelling at a high-level of abstraction (populations of neurons, layers, columns and the connections between them) while still allowing access to the details of individual neurons and synapses when required. PyNN provides a library of standard neuron, synapse and synaptic plasticity models, which have been verified to work the same on the different supported simulators. PyNN also provides a set of commonly-used connectivity algorithms (e.g. all-to-all, random, distance-dependent, small-world) but makes it easy to provide your own connectivity in a simulator-independent way, either using the Connection Set Algebra or by writing your own Python code. PyNN has been developed as a procedural description in Python which can be used to instantiate a network across multiple simulators.

本FAIRsharing记录介绍如下内容：PyNN应用程序编程接口（Application Programming Interface，API）旨在支持高抽象层级的建模工作，涵盖神经元群体、神经元层、皮层柱以及其间的连接关系，同时在有需求时仍可访问单个神经元与突触的细节信息。PyNN提供了一套标准化的神经元、突触以及突触可塑性模型库，经验证，这些模型在不同受支持的神经模拟器上均可正常运行且表现一致。此外，PyNN还内置了一系列常用的连接算法（例如全连接、随机连接、距离依赖连接、小世界连接），同时支持以模拟器无关的方式轻松自定义连接规则，既可以通过连接集代数（Connection Set Algebra）实现，也可以通过编写自定义Python代码完成。PyNN以Python编写的过程化描述形式开发，可用于在多种模拟器上实例化神经网络。