  Neural Memory Networks (NMNs) have received increased attention in recent
years compared to deep architectures that use a constrained memory. Despite
their new appeal, the success of NMNs hinges on the ability of the
gradient-based optimiser to perform incremental training of the NMN
controllers, determining how to leverage their high capacity for knowledge
retrieval. This means that while excellent performance can be achieved when the
training data is consistent and well distributed, rare data samples are hard to
learn from as the controllers fail to incorporate them effectively during model
training. Drawing inspiration from the human cognition process, in particular
the utilisation of neuromodulators in the human brain, we propose to decouple
the learning process of the NMN controllers to allow them to achieve flexible,
rapid adaptation in the presence of new information. This trait is highly
beneficial for meta-learning tasks where the memory controllers must quickly
grasp abstract concepts in the target domain, and adapt stored knowledge. This
allows the NMN controllers to quickly determine which memories are to be
retained and which are to be erased, and swiftly adapt their strategy to the
new task at hand. Through both quantitative and qualitative evaluations on
multiple public benchmarks, including classification and regression tasks, we
demonstrate the utility of the proposed approach. Our evaluations not only
highlight the ability of the proposed NMN architecture to outperform the
current state-of-the-art methods, but also provide insights on how the proposed
augmentations help achieve such superior results. In addition, we demonstrate
the practical implications of the proposed learning strategy, where the
feedback path can be shared among multiple neural memory networks as a
mechanism for knowledge sharing.
