TY - CONF AB - This paper investigates the properties required to evolve Artificial Neural Networks for distributed control in modular robotics, which typically involves non-linear dynamics and complex interactions in the sensori-motor space. We investigate 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 challenging control problems with non-linear dynamics are involved. This paper provides evidence that performance and robustness of evolved controllers can be improved by a combination of carefully chosen micro- and macro-scale neural network properties. AU - Hamann, Heiko AU - Stradner, Jürgen AU - Bredeche, Nicolas AU - Cazenille, Leo ID - 20173 T2 - 14th Annual Genetic and Evolutionary Computation Conference, GECCO 2012 TI - Impact of Neuron Models and Network Structure on Evolving Modular Robot Neural Network Controllers ER -