Event-encoding thresholds stop being trial-and-error: normal-condition variability sets an information boundary, and event density falls about 7.6-fold at comparable accuracy
Event-driven learning turns continuous signals into sparse events, but the triggering threshold has long been chosen empirically. This work maps signed threshold encoding onto discretized learning systems, uses the ratio of normal-condition increment standard deviation to threshold to define an information-sufficiency boundary, then picks the sparsest threshold retaining a prescribed fraction of peak discriminative information. On power-system fault classification, retaining 75% of peak information cut event density about 7.6-fold relative to that boundary while overall accuracy was 94.14%.