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Chimera state is a simultaneous combination of synchronous and asynchronous environments in a network of identical oscillators, attracts the attention of researchers in the different fields of science. Neuroscientists believe that existing both the synchronous and asynchronous behavior in the brain plays a vital role in brain neuronal activity. As a result, in this paper, we investigate the ability of a neural network to produce chimera. Our focus has been on using a discrete neuronal model which resolve the curse of dimensionality by retraining the complex behavior at the same time. Each neuron is coupled to its neighboring with limited scope. The interdependency of the spatiotemporal dynamic on the coupling strength in a fixed range made the switching process visible, which rises with the appearance of thin bands of incoherence, occupying the whole space ultimately.

Exploration of coherence phenomena in ensembles of interacting dynamical systems has been in the centre of research in social, physical, biological and technological systems for decades. But, in most of the studies, either completely percolated time- and space-static networks or temporal connectivities disregarding the systems' own dynamics have been dealt with. In this work, we examine the correlation between structural and dynamical evolution in networks of interacting dynamical systems. We specifically demonstrate the scenario of convergence of a set of chaotic attractors into a single attractor as a result of sufficient interaction based on the closeness of their own states. We characterize this occurrence through different measures, and map the collective states in network parameters' space. We further validate our proposition while exposing the whole scenario for different chaotic systems, namely Lorenz and Rossler oscillators.