  We investigate the synchronization features of a network of spiking neurons
under a distance-dependent coupling following a power-law model. The interplay
between topology and coupling strength leads to the existence of different
spatiotemporal patterns, corresponding to either non-synchronized or
phase-synchronized states. Particularly interesting is what we call
synchronization malleability, in which the system depicts significantly
different phase synchronization degrees for the same parameters as a
consequence of a different ordering of neural inputs. We analyze the functional
connectivity of the network by calculating the mutual information between
neuronal spike trains, allowing us to characterize the structures of
synchronization in the network. We show that these structures are dependent on
the ordering of the inputs for the parameter regions where the network presents
synchronization malleability and we suggest that this is due to a complex
interplay between coupling, connection architecture, and individual neural
inputs.
