The way information is represented and processed in a neural network may have important consequences on its computational power and complexity. Basically, information representation refers to distributed or localist encoding and information processing refers to schemes of connectivity that can be complete or minimal. In the past, theoretical and biologically inspired approaches of neural computation have insisted on complementary views (respectively distributed and complete versus localist and minimal) with complementary arguments (complexity versus expressiveness). In this paper, we report experiments on biologically inspired neural networks performing sensorimotor coordination that indicate that a localist and minimal view may have good performances if some connectivity constraints (also coming from biological inspiration) are respected.