  Repeating patterns of spike sequences from a neuronal network have been
proposed to be useful in the reconstruction of the network topology.
Reverberations in a physiologically realistic model with various physical
connection topologies (from random to scale-free) have been simulated to study
the effectiveness of the pattern-matching method in the reconstruction of
network topology from network dynamics. Simulation results show that functional
networks reconstructed from repeating spike patterns can be quite different
from the original physical networks; even global properties, such as the degree
distribution, cannot always be recovered. However, the pattern-matching method
can be effective in identifying hubs in the network. Since the form of
reverberations are quite different for networks with and without hubs, the form
of reverberations together with the reconstruction by repeating spike patterns
might provide a reliable method to detect hubs in neuronal cultures.
