Abstract-Artificial neural networks are so-called because they are supposed to be inspired from the brain and from the ways the neurons work. While some networks are used purely for computational purpose and do not endeavor to be a plausible representation of what happens in the brain, such as deep learning neural networks, others do. However, the question of the noise in the brain and its impact on the functioning of those networks has been little-studied. For example, it is widely known that synapses misfire with a significant probability. We model this noise and study its impact on associative memories powered by neural networks: neural clique networks and Hopfield networks as a reference point. We show that synaptic noise can in fact slightly improve the performance of the decoding process of neural clique networks by avoiding local minima.