Few people would disagree that the human brain is modular, but there is less agreement on the reasons why it has evolved to be like that. Recently I re-examined the Rueckl, Cave & Kosslyn study which demonstrated the advantages of having a modular architecture in neural network models of a simplified version of the "what" and "where" vision tasks. Explicit evolutionary simulations confirmed that the advantage can cause modularity to evolve, but also demonstrated that simply changing the learning cost function produced a system that learnt even better than before, and in which modularity did not evolve. In this paper I attempt to find a more robust characterization of the evolution of modularity in terms of gated sub-networks (i.e. mixtures of expert networks). Once again, a careful analysis of a systematic series of explicit evolutionary simulations indicates that drawing reliable conclusions in this area is not as straightforward as it might at first appear.
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