This paper investigates the properties required to evolve Artificial Neural Networks for distributed control in mod- ular robotics, which typically involves non-linear dynamics and complex interactions in the sensori-motor space. We in- vestigate the relation between macro-scale properties (such as modularity and regularity) and micro-scale properties in Neural Network controllers. We show how neurons capable of multiplicative-like arithmetic operations may increase the performance of controllers in several ways whenever chal- lenging control problems with non-linear dynamics are in- volved. This paper provides evidence that performance and robustness of evolved controllers can be improved by a com- bination of carefully chosen micro- and macro-scale neural network properties.