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In current deep network architectures, deeper layers in networks tend to contain hundreds of independent neurons which makes it hard for humans to understand how they interact with each other. By organizing the neurons by correlation, humans can observe how clusters of neighbouring neurons interact with each other. In this paper, we propose a novel algorithm for back propagation, called Locality Guided Neural Network(LGNN) for training networks that preserves locality between neighbouringarXiv:2007.06131v1 fatcat:6nauo3hblbhb5irusdi3dhc3dq