Precise control of signal propagation in modular neural networks represents a fundamental challenge in computational neuroscience. We establish a framework for identifying optimal control nodes that maximize stimulus transmission between weakly coupled neural populations. Using spiking stochastic block model networks, we systematically compare driver node selection strategies—including random sampling and topology-based centrality measures (degree, betweenness, closeness, eigenvector, harmonic, and percolation centrality)—to determine minimal control inputs for achieving inter-population synchronization. Targeted stimulation of just 10-20% of the most central neurons in the source population significantly enhances spiking propagation fidelity compared to random selection. This approach yields a \(\sim \) 64-fold increase in signal transfer efficiency at critical inter-module connection densities. These findings establish a theoretical foundation for precision neuromodulation in biological neural systems and neurotechnology applications.