Task-Dependent Evolution of Modularity in Neural Networks — A Quantitative Case Study

There exist many ideas and assumptions concerning the development and meaning of modularity in biological and technical neural systems. Nevertheless, this wide field is far from being understood; quantitative simulations and investigations are rare. In our contribution, we empirically study the development of connectionist models in the context of the evolution of artificial neural networks for highly modular problems. We define two measures for the degree of modularity and monitor their values during the evolutionary process. We identify two different reasons for the development of modular structures: the modularity of the task is reflected by the modularity of the adapted structure and the demand for fast learning structures increases the selective pressure towards modularity. However, learning can also counterbalance some imperfection of the underlying structure.