  Online continual learning (OCL) refers to the ability of a system to learn
over time from a continuous stream of data without having to revisit previously
encountered training samples. Learning continually in a single data pass is
crucial for agents and robots operating in changing environments and required
to acquire, fine-tune, and transfer increasingly complex representations from
non-i.i.d. input distributions. Machine learning models that address OCL must
alleviate \textit{catastrophic forgetting} in which hidden representations are
disrupted or completely overwritten when learning from streams of novel input.
In this chapter, we summarize and discuss recent deep learning models that
address OCL on sequential input through the use (and combination) of synaptic
regularization, structural plasticity, and experience replay. Different
implementations of replay have been proposed that alleviate catastrophic
forgetting in connectionists architectures via the re-occurrence of (latent
representations of) input sequences and that functionally resemble mechanisms
of hippocampal replay in the mammalian brain. Empirical evidence shows that
architectures endowed with experience replay typically outperform architectures
without in (online) incremental learning tasks.
