In parallel with expanding clinical indications, we have made foundational contributions to understanding the mechanisms and optimizing the technology of closed-loop brain stimulation. In a landmark study, we discovered that the therapeutic mechanism of RNS is not primarily acute seizure termination (which accounted for only approximately 5% of stimulated seizures), but rather progressive, indirect modulation of seizure network properties, including spontaneous ictal inhibition, frequency modulation, and network fragmentation, that emerged over weeks to months and predicted clinical response (Kokkinos et al., JAMA Neurology, 2019). To manage and analyze the vast quantities of intracranial EEG data generated by the RNS device, we developed BRAINStim (Biophysically Rational Analysis of Individual Neural Stimulation), a custom software platform that extracts, organizes, and enables spectral analysis of chronic electrocorticographic recordings (Sisterson et al., Neuroinformatics, 2020). We further quantified a specific frequency modulation response biomarker, an indirect electrophysiological effect of stimulation characterized by emergent shifts in the spectral content of seizure patterns, and demonstrated its potential for automated detection and use in optimizing stimulation parameters (Venkatesh et al., Journal of Neural Engineering, 2021). Building on these analytic foundations, we developed deep learning systems for expert-level automated detection of intracranial EEG seizure patterns in RNS recordings (Constantino et al., Frontiers in Neurology, 2021), subsequently extending this work to create iESPnet, a neural network capable of both detecting the presence and predicting the onset time of electrographic seizure patterns across patients with diverse seizure presentations (Peterson et al., Epilepsia, 2023).