  The majority of ML research concerns slow, statistical learning of i.i.d.
samples from large, labelled datasets. Animals do not learn this way. An
enviable characteristic of animal learning is `episodic' learning - the ability
to memorise a specific experience as a composition of existing concepts, after
just one experience, without provided labels. The new knowledge can then be
used to distinguish between similar experiences, to generalise between classes,
and to selectively consolidate to long-term memory. The Hippocampus is known to
be vital to these abilities. AHA is a biologically-plausible computational
model of the Hippocampus. Unlike most machine learning models, AHA is trained
without external labels and uses only local credit assignment. We demonstrate
AHA in a superset of the Omniglot one-shot classification benchmark. The
extended benchmark covers a wider range of known hippocampal functions by
testing pattern separation, completion, and recall of original input. These
functions are all performed within a single configuration of the computational
model. Despite these constraints, image classification results are comparable
to conventional deep convolutional ANNs.
