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The phase synchronization in a network of mean field coupled Hindmarsh–Rose neurons and the control of phase synchrony by an external input has been analyzed in this work. The analysis of interspike interval, with varying coupling strength, reveals the dynamical change induced in each neuron in the network. The bursting phase lines depict that mean field coupling induces phase synchrony in excitatory mode and desynchrony in inhibitory mode. The coefficient of variability, in spatial and temporal domain, signifies the deviations in firing times of neurons, in a collective manner. The Kuramoto order parameter quantifies the intermittent and complete phase synchrony, induced by excitatory mean field coupling. The capability of external input, in the form of spikes, to control the intermittent and complete phase synchrony has been analyzed. The coefficient of variability and Kuramoto order parameter has been studied by varying the amplitude, pulse width and frequency of the input. The studies have shown that high-frequency spike input, with optimum amplitude and pulse width, has high desynchronizing ability, which is substantiated by the parameter space analysis. The control of synchrony in the network of neurons may find application in rectifying neural disorders.

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In this work, we have analysed the synchronous dynamics and pattern formation in Hindmarsh-Rose neurons with cross interactions between membrane potential and magnetic flux, in the chemical mode. The self, mixed and cross interactions are realised by varying coupling phase. The magnetic flux induces plateau bursting and amplitude death in the network. The self chemical coupling induces synchrony, whereas, the cross coupling is incapable of it. However, the cross coupling acts along with self coupling to form mixed coupling and induces synchrony in the system. The stability of the synchronous state has been studied by Master stability approach. The parameter space reveals the bifurcation point at which cross coupling overrides self coupling effects. The synchronising ability of interactions are justified in a network of neurons as well. The statistical factor of synchronisation quantifies the amount of synchrony in the network in different interaction modes. The combined effect of non local interactions and mixed coupling of variables initiates the emergence of chimera and multichimera states. However, in cross-coupled systems, only incoherent states are present. The existence of chimera and multichimera states are confirmed by calculating the strength of incoherence and discontinuity measure. The analysis of spatiotemporal patterns reveals the presence of travelling chimeras within the network. The Hamilton energy function indicate that a greater amount of energy is required to sustain coherent neurons at higher potential. This work may enhance the understanding of chimera states and improve its applicability to real-world systems.