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 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.