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：在神经形态领域开发机器学习应用的有效学习系统需要大量的实验与仿真。软件框架通过提供一组可供研究人员直接使用的工具，有助于简化这一过程。近年来，对神经形态技术的兴趣催生了多个新框架的开发，这些框架丰富了神经科学领域已有库的版图。本文综述了9个专门面向数据科学应用开发的脉冲神经网络框架。我们重点介绍了脉冲神经元模型与学习规则的可用性，以便更轻松地指导针对不同类型研究选择最合适的框架。此外，我们提出了SpykeTorch框架的一个扩展，该扩展为用户提供了更广泛的神经元模型选择以嵌入SNN中，并将代码公开发布。