This paper introduces a novel approach for inter-neuron communication in the same layer in a standard neural network, a Neural Communication (NC) block. The NC block results in slightly better performance (absolute improvement of 1%) than previous similar methods (such as Squeeze-Excitation blocks) on image classification, semantic segmentation and object detection, and reviewers find the formulation to be more general and better motivated. The authors demonstrate that shallower networks with the NC block can achieve similar performance to deeper networks. They also provide a detailed and useful analysis of the properties of the learned representations, and show that the NC blocks leads to learning neurons that are less correlated. Reviewers are concerned that the improvement is only marginal, and that comparison to non-local networks is not reported. They suggest that the paper might be stronger if there is a better explanation of the motivations behind the NC block development and the specific choices made.