Intrinsic neuronal excitability provides a functional proxy of a neuron’s activity state and reflects its capacity for adaptation through intrinsic plasticity. This thesis aims to discover and characterize electrophysiological excitability regimes as dynamic functional states by constructing a data-driven excitability landscape directly from patch-clamp recordings and assessing their relationship with neuronal types and subtypes defined by Patch-seq. Despite the central role of excitability in neuronal computation and plasticity, electrophysiology still lacks a unified characterization of excitability levels that quantifies where a neuron lies within a continuous spectrum of responses when subjected to stimulation. This thesis addresses this gap by constructing a data-driven excitability landscape directly from patch-clamp recordings and by testing whether electrophysiological excitability regimes align with, or cut across, transcriptomically defined neuronal types and subtypes measured with Patch-seq.