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Transcription factor-based forward programming enables the efficient generation of forebrain excitatory and inhibitory neurons from human pluripotent stem cells (hPSCs). This provides an opportunity to study stimulation-response patterns in highly defined neuronal networks in a controlled and customizable in vitro environment. Cell populations composed of defined ratios of excitatory and inhibitory neurons were generated by forward programming genome-edited human hPSCs carrying the inducible transcription factors NGN2 and ASCL1/DLX2, respectively. These populations were cultured on multi-electrode arrays (MEAs), and population responses elicited by distinct spatial and temporal stimulation patterns were analyzed. In parallel, in silico network models fed with neuronal parameters obtained from the in vitro cultures were developed to explore potential mechanisms underlying experimental observations. Neuronal cultures developed network-level electrophysiological activities with pronounced synchronized network bursts (NBs), which responded to synaptic modulators. Interestingly, local electrical pulse stimulation at frequencies ≤ 0.2 Hz reliably elicited NBs, while frequencies of ≥ 1 Hz yielded no homogeneous responses, but only sporadic NBs. In contrast, multi-site stimulation at the same frequency could elicit NBs robustly. Data from computational models suggest that this phenomenon can be explained by exhaustion and presynaptic functional paralysis of targeted circuits by high-frequency local stimulation. Compared to hPSC-derived neurons generated solely by small molecule treatment, forward-programmed excitatory and inhibitory neurons enable the composition of highly confectionized networks. In silico simulation of induced biological network responses can be directly used to devise and validate mechanistic hypotheses underlying the recorded network dynamics. The present study demonstrates the prospect of the iPSC technology for conducting personalized in vitro studies of human neuronal networks and their responses to electric stimuli. It also illustrates how the combined use of biological and in silico neuronal networks can support the development of mechanistic hypotheses underlying network responses to specific stimuli. • Functional iPSC-derived neuronal networks with defined E/I ratios. • In silico modeling of biological human neuronal network dynamics. • Synaptic vesicle pool depletion as cause of local circuit paralysis. [ABSTRACT FROM AUTHOR]