This paper explores neuron models’ role in advancing neuromorphic computing by utilising neurons’ behaviours and controllable features for Edge AI. The growing landscape of AI applications demands optimised hardware for seamless operations, yet conventional Von Neumann architectures must be more efficient in meeting these escalating demands. As a solution, the rise of Edge AI has surfaced, targeting smaller-scale tasks at the source. Leveraging neuromorphic computing in this domain holds promise, particularly in crafting brain-like systems. At the core of this pursuit lies the fundamental element: The neuron. This paper aims to delve into a comprehensive study of the Izhikevich neuron model and test its diverse behaviours in simulators and its conversion advantages into the digital realm. The motto is exploring how these intricate neuronal concepts and behaviours can be effectively harnessed to develop compact-scale digital Edge systems tailored to execute small spiking neural networks, Liquid state Machines and Hopfield networks. By examining the nuanced dynamics of the Izhikevich neuron and extrapolating its principles, this study seeks to pave the way for innovative, efficient, and adaptable computing paradigms at the Edge, facilitating the realisation of brain-inspired functionalities in AI applications with constrained resources and environments.