  Recently, the use bio-plausible learning techniques such as Hebbian and
Spike-Timing-Dependent Plasticity (STDP) have drawn significant attention for
the design of compute-efficient AI systems that can continuously learn on-line
at the edge. A key differentiating factor regarding this emerging class of
neuromorphic continual learning system lies in the fact that learning must be
carried using a data stream received in its natural order, as opposed to
conventional gradient-based offline training where a static training dataset is
assumed available a priori and randomly shuffled to make the training set
independent and identically distributed (i.i.d). In contrast, the emerging
class of neuromorphic continual learning systems covered in this survey must
learn to integrate new information on the fly in a non-i.i.d manner, which
makes these systems subject to catastrophic forgetting. In order to build the
next generation of neuromorphic AI systems that can continuously learn at the
edge, a growing number of research groups are studying the use of bio-plausible
Hebbian neural network architectures and Spiking Neural Networks (SNNs)
equipped with STDP learning. However, since this research field is still
emerging, there is a need for providing a holistic view of the different
approaches proposed in literature so far. To this end, this survey covers a
number of recent works in the field of neuromorphic continual learning;
provides background theory to help interested researchers to quickly learn the
key concepts; and discusses important future research questions in light of the
different works covered in this paper. It is hoped that this survey will
contribute towards future research in the field of neuromorphic continual
learning.
