  Continual learning (CL) in the brain is facilitated by a complex set of
mechanisms. This includes the interplay of multiple memory systems for
consolidating information as posited by the complementary learning systems
(CLS) theory and synaptic consolidation for protecting the acquired knowledge
from erasure. Thus, we propose a general CL method that creates a synergy
between SYNaptic consolidation and dual memory Experience Replay (SYNERgy). Our
method maintains a semantic memory that accumulates and consolidates
information across the tasks and interacts with the episodic memory for
effective replay. It further employs synaptic consolidation by tracking the
importance of parameters during the training trajectory and anchoring them to
the consolidated parameters in the semantic memory. To the best of our
knowledge, our study is the first to employ dual memory experience replay in
conjunction with synaptic consolidation that is suitable for general CL whereby
the network does not utilize task boundaries or task labels during training or
inference. Our evaluation on various challenging CL scenarios and
characteristics analyses demonstrate the efficacy of incorporating both
synaptic consolidation and CLS theory in enabling effective CL in DNNs.
