Time delay and coupling strength are important factors that affect the of neural networks. In this study, a containing s of different scales was constructed using the ;Huxley (HH) neural model; i.‍e., a small-scale random network was unidirectionally connected to a large-scale small-world network through chemical synapses. Time delays were found to induce multiple transitions in the network. An increase in coupling strength also promoted of the network when the time delay was an integer multiple of the firing period of a single neuron. Considering that time delays at different locations in a modular network may have different effects, we explored the influence of time delays within each and between two s on the of modular networks. We found that when the s were well synchronized internally, an increase in the time delay within both s induced multiple transitions of their own. In addition, the state of the small-scale network affected the of the large-scale network. It was surprising to find that an increase in the time delay between the two s caused the factor of the modular network to vary periodically, but it had essentially no effect on the within the receiving . By analyzing the phase difference between the two s, we found that the mechanism of the periodic variation of the factor of the modular network was the periodic variation of the phase difference. Finally, the generality of the results was demonstrated by investigating modular networks at different scales.