  Standard few-shot experiments involve learning to efficiently match
previously unseen samples by class. We claim that few-shot learning should be
long term, assimilating knowledge for the future, without forgetting previous
concepts. In the mammalian brain, the hippocampus is understood to play a
significant role in this process, by learning rapidly and consolidating
knowledge to the neocortex over a short term period. In this research we tested
whether an artificial hippocampal algorithm, AHA, could be used with a
conventional ML model analogous to the neocortex, to achieve one-shot learning
both short and long term. The results demonstrated that with the addition of
AHA, the system could learn in one-shot and consolidate the knowledge for the
long term without catastrophic forgetting. This study is one of the first
examples of using a CLS model of hippocampus to consolidate memories, and it
constitutes a step toward few-shot continual learning.
