  An essential requirement for the representation of functional patterns in
complex neural networks, such as the mammalian cerebral cortex, is the
existence of stable network activations within a limited critical range. In
this range, the activity of neural populations in the network persists between
the extremes of quickly dying out, or activating the whole network. The nerve
fiber network of the mammalian cerebral cortex possesses a modular organization
extending across several levels of organization. Using a basic spreading model
without inhibition, we investigated how functional activations of nodes
propagate through such a hierarchically clustered network. The simulations
demonstrated that persistent and scalable activation could be produced in
clustered networks, but not in random networks of the same size. Moreover, the
parameter range yielding critical activations was substantially larger in
hierarchical cluster networks than in small-world networks of the same size.
These findings indicate that a hierarchical cluster architecture may provide
the structural basis for the stable and diverse functional patterns observed in
cortical networks.
