  Complex architectures of biological neural circuits, such as parallel
processing pathways, has been behaviorally implicated in many cognitive
studies. However, the theoretical consequences of circuit complexity on neural
computation have only been explored in limited cases. Here, we introduce a
mechanism by which direct and indirect pathways from cortex to the CA3 region
of the hippocampus can balance both contextual gating of memory formation and
driving network activity. We implement this concept in a deep artificial neural
network by enabling a context-sensitive bias. The motivation for this is to
improve performance of a size-constrained network. Using direct knowledge of
the superclass information in the CIFAR-100 and Fashion-MNIST datasets, we show
a dramatic increase in performance without an increase in network size.
