  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.
