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The majority of ML research concerns slow, statistical learning of i.i.d.samples from large, labelled datasets. Animals do not learn this way. Anenviable characteristic of animal learning is `episodic' learning - the abilityto memorise a specific experience as a composition of existing concepts, afterjust one experience, without provided labels. The new knowledge can then beused to distinguish between similar experiences, to generalise between classes,and to selectively consolidate to long-term memory. The Hippocampus is known tobe vital to these abilities. AHA is a biologically-plausible computationalmodel of the Hippocampus. Unlike most machine learning models, AHA is trainedwithout external labels and uses only local credit assignment. We demonstrateAHA in a superset of the Omniglot one-shot classification benchmark. Theextended benchmark covers a wider range of known hippocampal functions bytesting pattern separation, completion, and recall of original input. Thesefunctions are all performed within a single configuration of the computationalmodel. Despite these constraints, image classification results are comparableto conventional deep convolutional ANNs.

AHA! an 'Artificial Hippocampal Algorithm' for Episodic Machine Learning