  Cortical circuits exhibit intricate recurrent architectures that are
remarkably similar across different brain areas. Such stereotyped structure
suggests the existence of common computational principles. However, such
principles have remained largely elusive. Inspired by gated-memory networks,
namely long short-term memory networks (LSTMs), we introduce a recurrent neural
network in which information is gated through inhibitory cells that are
subtractive (subLSTM). We propose a natural mapping of subLSTMs onto known
canonical excitatory-inhibitory cortical microcircuits. Our empirical
evaluation across sequential image classification and language modelling tasks
shows that subLSTM units can achieve similar performance to LSTM units. These
results suggest that cortical circuits can be optimised to solve complex
contextual problems and proposes a novel view on their computational function.
Overall our work provides a step towards unifying recurrent networks as used in
machine learning with their biological counterparts.
