Neuro-evolution and computational neuroscience are two scientific domains that produce surprisingly different artificial neural networks. Inspired by the "toolbox" used by neuroscientists to create their models, this paper argues two main points: (1) neural maps (spatially-organized identical neurons) should be the building blocks to evolve neural networks able to perform cognitive functions and (2) well-identified modules of the brain for which there exists computational neuroscience models provide well-defined benchmarks for neuro-evolution. To support these claims, a method to evolve networks of neural maps is introduced then applied to evolve neural networks with a similar functionality to basal ganglia in animals (i.e. action selection). Results show that: (1) the map-based encoding easily achieves this task while a direct encoding never solves it; (2) this encoding is independent of the size of maps and can therefore be used to evolve large and brain-like neural networks; (3) the failure of direct encoding to solve the task validates the relevance of action selection as a benchmark for neuro-evolution.