  Applications that generate huge amounts of data in the form of fast streams
are becoming increasingly prevalent, being therefore necessary to learn in an
online manner. These conditions usually impose memory and processing time
restrictions, and they often turn into evolving environments where a change may
affect the input data distribution. Such a change causes that predictive models
trained over these stream data become obsolete and do not adapt suitably to new
distributions. Specially in these non-stationary scenarios, there is a pressing
need for new algorithms that adapt to these changes as fast as possible, while
maintaining good performance scores. Unfortunately, most off-the-shelf
classification models need to be retrained if they are used in changing
environments, and fail to scale properly. Spiking Neural Networks have revealed
themselves as one of the most successful approaches to model the behavior and
learning potential of the brain, and exploit them to undertake practical online
learning tasks. Besides, some specific flavors of Spiking Neural Networks can
overcome the necessity of retraining after a drift occurs. This work intends to
merge both fields by serving as a comprehensive overview, motivating further
developments that embrace Spiking Neural Networks for online learning
scenarios, and being a friendly entry point for non-experts.
