MOASN (Multi-Objective Analysis of Spiking Neuron Models) is a computational neuroscience project that investigates the trade-off between biological fidelity and computational efficiency in neuromorphic AI. The framework evaluates Leaky Integrate-and-Fire, Izhikevich, and Hodgkin–Huxley neuron models using spike timing accuracy, firing rate accuracy, and runtime cost. Through Pareto-based multi-objective analysis, the study demonstrates that while Hodgkin–Huxley offers the highest biological realism and LIF maximizes efficiency, the Izhikevich model provides the most balanced compromise for scalable spiking neural networks and neuromorphic computing applications. This work highlights the importance of hardware-aware neuron model selection for energy-efficient brain-inspired AI systems.