Developing effective learning systems for Machine Learning (ML) applications in the Neuromorphic (NM) field requires extensive experimentation and simulation. Software frameworks aid and ease this process by providing a set of ready-to-use tools that researchers can leverage. The recent interest in NM technology has seen the development of several new frameworks that do this, and that add up to the panorama of already existing libraries that belong to neuroscience fields. This work reviews 9 frameworks for the development of Spiking Neural Networks (SNNs) that are specifically oriented towards data science applications. We emphasize the availability of spiking neuron models and learning rules to more easily direct decisions on the most suitable frameworks to carry out different types of research. Furthermore, we present an extension to the SpykeTorch framework that gives users access to a much broader choice of neuron models to embed in SNNs and make the code publicly available.

翻译：开发神经地貌(NM)领域机器学习应用的有效学习系统需要广泛的试验和模拟。软件框架通过提供研究人员可以利用的一套现成工具来帮助和方便这一进程。最近对NM技术的兴趣已看到为此开发了若干新的框架,这些新框架加之了属于神经科学领域的现有图书馆的全景。这项工作审查了9个专门面向数据科学应用的Spiking神经网络(SNN)开发框架。我们强调,可提供快速的神经模型和学习规则,以便更方便地直接决定开展不同类型研究的最合适的框架。此外,我们还介绍了SpykeTorch框架的扩展,使用户可以广泛选择神经模型,将其嵌入SNNS并向公众开放。