  In the domain of machine learning, Neural Memory Networks (NMNs) have
recently achieved impressive results in a variety of application areas
including visual question answering, trajectory prediction, object tracking,
and language modelling. However, we observe that the attention based knowledge
retrieval mechanisms used in current NMNs restricts them from achieving their
full potential as the attention process retrieves information based on a set of
static connection weights. This is suboptimal in a setting where there are vast
differences among samples in the data domain; such as anomaly detection where
there is no consistent criteria for what constitutes an anomaly. In this paper,
we propose a plastic neural memory access mechanism which exploits both static
and dynamic connection weights in the memory read, write and output generation
procedures. We demonstrate the effectiveness and flexibility of the proposed
memory model in three challenging anomaly detection tasks in the medical
domain: abnormal EEG identification, MRI tumour type classification and
schizophrenia risk detection in children. In all settings, the proposed
approach outperforms the current state-of-the-art. Furthermore, we perform an
in-depth analysis demonstrating the utility of neural plasticity for the
knowledge retrieval process and provide evidence on how the proposed memory
model generates sparse yet informative memory outputs.
