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A fundamental question in neuroscience is how structure and function ofneural systems are related. We study this interplay by combining a familiarauto-associative neural network with an evolving mechanism for the birth anddeath of synapses. A feedback loop then arises leading to two qualitativelydifferent types of behaviour. In one, the network structure becomesheterogeneous and dissasortative, and the system displays good memoryperformance; furthermore, the structure is optimised for the particular memorypatterns stored during the process. In the other, the structure remainshomogeneous and incapable of pattern retrieval. These findings provide aninspiring picture of brain structure and dynamics, are compatible withexperimental results on early brain development, and may help to explainsynaptic pruning. Other evolving networks -- such as those of proteininteraction -- might share the basic ingredients for this feedback loop andother questions, and indeed many of their structural features are as predictedby our model.

Concurrence of form and function in developing networks and its role in synaptic pruning