  As a model of temporally evolving networks, we consider a globally coupled
logistic map with variable connection weights. The model exhibits
self-organization of network structure, reflected by the collective behavior of
units. Structural order emerges even without any inter-unit synchronization of
dynamics. Within this structure, units spontaneously separate into two groups
whose distinguishing feature is that the first group possesses many
outwardly-directed connections to the second group, while the second group
possesses only few outwardly-directed connections to the first. The relevance
of the results to structure formation in neural networks is briefly discussed.
