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

Context-modulation of hippocampal dynamics and deep convolutional networks