  Recent experimental and theoretical studies have indicated that the putative
criticality of cortical dynamics may corresponds to a synchronization phase
transition. The critical dynamics near such a critical point needs further
investigation specifically when compared to the critical behavior near the
standard absorbing state phase transition. Since the phenomena of learning and
self-organized criticality (SOC) at the edge of synchronization transition can
emerge jointly in spiking neural networks due to the presence of spike-timing
dependent plasticity (STDP), it is tempting to ask: What is the relationship
between synchronization and learning in neural networks? Further, does learning
benefit from SOC at the edge of synchronization transition? In this paper, we
intend to address these important issues. Accordingly, we construct a
biologically inspired model of an autonomous cognitive system which learns to
perform stimulus-response tasks. We train this system using a reinforcement
learning rule implemented through dopamine-modulated STDP. We find that the
system exhibits a continuous transition from synchronous to asynchronous neural
oscillations upon increasing the average axonal time delay. We characterize the
learning performance of the system and observe that it is optimized near the
synchronization transition. We also study neuronal avalanches in the system and
provide evidence that optimized learning is achieved in a slightly
supercritical state.
