Conditions that trigger the abrupt transition of normal network activity into ictal seizures remain poorly defined. Understanding the mechanisms of ictal onset could enable seizure prediction and timely intervention. However, the unpredictable nature of seizures poses a major impediment to experimental analysis of ictal transitions. Interictal spikes, the brief sharp electroencephalographic transients that occur between seizures, often precede seizures in epileptic patients and animal models.1 Yet, their role in initiating seizures has remained controversial.2 To overcome the limitations of experimental analysis, Jacobs and colleagues adopt a network model, capable of generating spontaneous interictal and ictal activity to manipulate and analyze the conditions leading to ictal state transitions. Using a simplified neural network model, the study examines the structural features that make a network permissive to ictal onset and the synaptic physiological features that promote ictal transition.