Developments in micro-aerial vehicle technology highlight a transition towards bio-inspired flapping wing designs, mirroring the agile flight of birds and insects. Inspired by the control strategies employed by insects, this work presents a two-phase adaptive full-envelop spiking neural network (SNN) control design for flapping-wing micro-aerial vehicles (FWMAVs). The approach developed in this work allows the FWMAV to adapt to unmodeled uncertainties, which is demonstrated in simulation through two case studies and comparison with an SNN approximation of a traditional gain-scheduled PIF Compensator control scheme. Further, this work uses flight recordings from real insects (Hawk Moths) to develop a generalized state-space model and solve a constrained non-linear optimization problem to obtain a family of set points that satisfy hovering conditions. Singular value decomposition is used on insect wing and body kinematics to identify the most dominant actuation modes and create a reduced rank approximation. The approaches presented in this work can inform the development of the next generation of intelligent, agile, and highly adaptive FWMAVs.