Synchronization in Small-World Chaotic Neural Networks

In this study, we propose a complex network composed of chaotic model neurons. Specifically, we construct small-world networks with complex structure and investigate the synchronization phenomena of the proposed model through numerical experiments. To do this, we take into consideration a rate of synchronized pairs of neurons out of all possible pairs of them in the network. We show that, with increasing wiring probability, the rate of synchronization first reaches a peak, decreases locally, and then reaches another peak. We also show that for a specific wiring probability, the synchronization of relatively clustered neurons is periodic, whereas that of the other neurons is clearly non-periodic. In such a case, we confirmed that the dynamics of the network is deterministic chaos.