  Neural synchrony in the brain at rest is usually variable and intermittent,
thus intervals of predominantly synchronized activity are interrupted by
intervals of desynchronized activity. Prior studies suggested that this
temporal structure of the weakly synchronous activity might be functionally
significant: many short desynchronizations may be functionally different from
few long desynchronizations even if the average synchrony level is the same. In
this study, we used computational neuroscience methods to investigate the
effects of spike-timing dependent plasticity (STDP) on the temporal patterns of
synchronization in a simple model. We employed a small network of
conductance-based model neurons that were connected via excitatory plastic
synapses. The dynamics of this network was subjected to the time-series
analysis methods used in prior experimental studies. We found that STDP could
alter the synchronized dynamics in the network in several ways, depending on
the time scale that plasticity acts on. However, in general, the action of STDP
in the simple network considered here is to promote dynamics with short
desynchronizations (i.e. dynamics reminiscent of that observed in experimental
studies). Complex interplay of the cellular and synaptic dynamics may lead to
the activity-dependent adjustment of synaptic strength in such a way as to
facilitate experimentally observed short desynchronizations in the
intermittently synchronized neural activity.
