Intermittent chimera-like and bi-stable synchronization states in network of distinct Izhikevich neurons

Phase synchronization phenomena of neuronal networks are one of many features depicted by real networks that can be studied using computational models. Here, we proceed with numerical simulations of a globally connected network composed of non-identical (distinct) Izhikevich neuron model to study clustered phase synchronization. We investigate the case in which, once coupled, there exist two main neuron clusters in the network: one of them is bi-stable, depicting phase-synchronized or unsynchronized states, depending on the initial conditions; and the second one shows just an unsynchronized state. For the set of initial conditions that lead the first cluster to the synchronized regime, we observe a chimera-like pattern of the network. For small networks, the dynamics can also present intermittent chimera-like scenarios. In this context, the mechanism for intermittent chimera states is based on two features: the coexistence of a synchronized cluster with an unsynchronized one; and the capability of one cluster to display bi-stability depending on the signal trait by which it is forced. We conclude with an understanding of intermittent chimera-like dynamics as the limit case where bi-stability is not maintained, which occurs due to the loss of uniformity in the neuron input synaptic currents. •The network of nonidentical neurons can display intermittent chimera-like phenomena.•Individual dynamics is important to characterize several complex phenomena in the collective network dynamics.•The size of the network for nonidentical neurons globally coupled can change the phase synchronization patterns.•Phase synchronization hysteresis emerges for coupling strength change.