Decision-Identifiable Control of Partially Observed Epilepsy Networks

Selecting a suppressive target in an epileptic network requires accounting for pathways that intracranial electrodes cannot observe. PULSE connects local probe responses to suppression benefits and uses the uncertainty in those benefits to decide where to measure next. In a stable nonnegative Hawkes network with calibrated probes, integrated observed responses and baseline activity determine stationary outcomes under local gain suppression, even when hidden structure is not uniquely identified. A target's baseline rate and two response summaries suffice; correcting for recurrent returns explains why the strongest evoked response need not identify the best target. In 24 network families whose members differ in hidden structure and transient response, all 2,880 intervention conditions share the benefits predicted by this formula, and predictions from finite measurements err by 2.38 percentage points of baseline observed burden on average across 3,072 interventions. The same summaries also direct measurement: in 96 benchmark networks, decision-focused acquisition reaches 2.35% selection regret at 2,560 trial blocks against 5.33% for uniform allocation, and at 5,120 blocks it keeps regret at 1.72% against 3.11% for an acquisition rule that reconstructs the responses more accurately. Independent post-action simulations of the intervened networks confirm the predicted benefits, and experiments on hidden coverage, probe calibration, and response windows delineate the required measurement conditions, including a 250-ms benchmark built on empirical human response timing. The information a suppression decision needs can thus be read from local probes, without reconstructing the hidden network.