  Lifelong learning is a long-standing aim for artificial agents that act in
dynamic environments, in which an agent needs to accumulate knowledge
incrementally without forgetting previously learned representations. We
investigate methods for learning from data produced by event cameras and
compare techniques to mitigate forgetting while learning incrementally. We
propose a model that is composed of both, feature extraction and continuous
learning. Furthermore, we introduce a habituation-based method to mitigate
forgetting. Our experimental results show that the combination of different
techniques can help to avoid catastrophic forgetting while learning
incrementally from the features provided by the extraction module.
