Context-modulation of hippocampal dynamics and deep convolutional networks
This work addresses the challenge of enhancing neural network efficiency for AI applications, though it is incremental as it builds on existing biological insights and applies them to known datasets.
The paper tackled the problem of improving performance in size-constrained deep neural networks by introducing a context-sensitive bias mechanism inspired by hippocampal pathways, resulting in a dramatic performance increase on CIFAR-100 and Fashion-MNIST datasets without increasing network size.
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.