Frameworks for SNNs: a review of data science-oriented software and an expansion of SpykeTorch

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 belonged to neuroscience fields. This work reviews 9 frameworks for the development of Spiking Neural Networks (SNNs) that are specifically oriented towards data science applications. Furthermore, we present an extension to the SpykeTorch framework that gives users access to a much broader choice of spiking neurons to embed in SNNs and make the code publicly available.