  In this paper, we study instances of complex neural networks, i.e. neural
netwo rks with complex topologies. We use Self-Organizing Map neural networks
whose n eighbourhood relationships are defined by a complex network, to
classify handwr itten digits. We show that topology has a small impact on
performance and robus tness to neuron failures, at least at long learning
times. Performance may howe ver be increased (by almost 10%) by artificial
evolution of the network topo logy. In our experimental conditions, the evolved
networks are more random than their parents, but display a more heterogeneous
degree distribution.
