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Neural synchrony in the brain at rest is usually variable and intermittent,thus intervals of predominantly synchronized activity are interrupted byintervals of desynchronized activity. Prior studies suggested that thistemporal structure of the weakly synchronous activity might be functionallysignificant: many short desynchronizations may be functionally different fromfew long desynchronizations even if the average synchrony level is the same. Inthis study, we used computational neuroscience methods to investigate theeffects of spike-timing dependent plasticity (STDP) on the temporal patterns ofsynchronization in a simple model. We employed a small network ofconductance-based model neurons that were connected via excitatory plasticsynapses. The dynamics of this network was subjected to the time-seriesanalysis methods used in prior experimental studies. We found that STDP couldalter the synchronized dynamics in the network in several ways, depending onthe time scale that plasticity acts on. However, in general, the action of STDPin the simple network considered here is to promote dynamics with shortdesynchronizations (i.e. dynamics reminiscent of that observed in experimentalstudies). Complex interplay of the cellular and synaptic dynamics may lead tothe activity-dependent adjustment of synaptic strength in such a way as tofacilitate experimentally observed short desynchronizations in theintermittently synchronized neural activity.

Spike-Timing Dependent Plasticity Effect on the Temporal Patterning of Neural Synchronization