# Noise Resilience

ConwayNet adds to each neural-network layer a fixed, Game-of-Life-inspired lateral-inhibition rule whose parameters are never optimised: it reads the layer input's neighbourhood occupancy and blends birth and survival gates into the output according to each unit's own state. Wired into fully connected, convolutional, wide-residual and graph convolutional networks, it makes activation maps coherent across space, depth and seeds and pushes gradient saliency onto the object; on the shallow convolutional network, accuracy under Gaussian, shot and impulse noise rises by double digits, at a clean-accuracy cost of about one point on MNIST, EMNIST and their colour versions and a few points on FashionMNIST and KMNIST.

ConwayNet adds to each neural-network layer a fixed, Game-of-Life-inspired lateral-inhibition rule whose parameters are never optimised: it reads the layer input's neighbourhood occupancy and blends birth and survival gates into the output according to each unit's own state. Wired into fully connected, convolutional, wide-residual and graph convolutional networks, it makes activation maps coherent across space, depth and seeds and pushes gradient saliency onto the object; on the shallow convolutional network, accuracy under Gaussian, shot and impulse noise rises by double digits, at a clean-accuracy cost of about one point on MNIST, EMNIST and their colour versions and a few points on FashionMNIST and KMNIST.